AI/ML, Software as a Medical Device, Real-World Evidence
Real-World Evidence
Submission
Device
Sponsor
RWD Sources
RWE Use Summary
Key Tags
K242748 · Apr 11, 2025
Oncospace
Oncospace, Inc.
Retrospective clinical treatment plans from Johns Hopkins University (2008-2019); Retrospective clinical treatment plans from Montefiore Einstein Comprehensive Cancer Center; External clinical datasets from Institution_2 and Institution_3
Retrospective clinical data were used to train and tune machine learning models for OAR dose prediction and to validate the clinical performance of the device by comparing Oncospace-assisted plans against historical clinical treatment plans.
Model Development and Clinical Validation Datasets; Retrospective cohort analysis; Follow-up/Duration: 2008-2019 (Development); 2021-2024 (Clinical Validation); Study Period: 2008-2024
Patients with malignant or benign disease in head and neck, thoracic, abdominal, and pelvic regions; Sample Size: Over 3,500 total treatment plans across multiple anatomical locations; Number of Sites: 3 (Johns Hopkins University, Montefiore Einstein, and external institutions)
Not applicable for this study
Mean organ-at-risk (OAR) dose sparing, target coverage, and OAR name standardization accuracy
AI Performance
Output
Algorithm
Acceptance
Observed
Dev DS
Dev Readers
Test DS
Test Readers
Radiotherapy treatment plan OAR dose sparing
Machine learning algorithms for OAR dose prediction
Non-inferiority margin of 10 Gy for mean OAR dose
Non-inferiority demonstrated; mean dose statistically significantly lower for 5 OARs (abdominal) and 4 OARs (gynecological)
94.1% accuracy for structure names; 91.0% accuracy for unique structure names
Prostate, gynecological, head and neck, thoracic, and pancreas cancer datasets licensed from Johns Hopkins University
—
External testing dataset: 221 structures with 145 unique original names
—
Indications for Use
Oncospace is used to configure and review radiotherapy treatment plans for a patient with malignant or benign disease in the head and neck, thoracic, abdominal, and pelvic regions. It allows for set up of radiotherapy treatment protocols, association of a potential treatment plan with the protocol(s), submission of a dose prescription and achievable dosimetric goals to a treatment planning system, and review of the treatment plan. It is intended for use by qualified, trained radiation therapy professionals (such as medical physicists, oncologists, and dosimetrists). This device is for prescription use by order of a physician.
Device Story
Oncospace is a software-only tool for radiation therapy treatment planning. It ingests patient-specific anatomical data and existing treatment plans to assist oncologists and dosimetrists. The device uses locked machine learning algorithms to predict organ-at-risk (OAR) dosimetric goals based on patient anatomy and standardized protocols. It automates the initiation of plan optimization in a separate treatment planning system (TPS) by providing dose prescriptions and predicted objectives. The software includes an NLP model to map non-standardized OAR names to AAPM TG-263 standards. Used in clinics or hospitals, it provides a user interface for plan evaluation, comparison, and review of isodose lines and dose-volume histograms (DVH). It does not interface with treatment machines or record-and-verify systems. By providing data-driven OAR sparing objectives, it aims to reduce planning effort and improve plan quality while maintaining target coverage. Human judgment remains central to final plan acceptance and optimization.
Clinical Evidence
Clinical validation used retrospective datasets (18 H&N, 20 thoracic, 17 abdominal, 17 pelvic patients). Primary endpoints: non-inferiority of mean OAR dose sparing and maintenance of target coverage compared to traditional clinical plans. Results showed statistically significant lower mean doses for several OARs in abdominal and pelvic (gynecological) cohorts, with no significant difference in target coverage. Non-inferiority margins were 2.2 Gy (thoracic), 1 Gy (abdominal), and 1.9 Gy (pelvic). Model performance testing (MAE of DVH dose values) showed most OARs within 5-8% of prescription dose. NLP model achieved >96% accuracy and >0.92 F1 score.
Technological Characteristics
Software-only medical device; client-server architecture (clinic-provided clients, cloud Windows servers). Connectivity via DICOM-RT. Features locked machine learning algorithms for OAR dose prediction and NLP for structure name standardization (TG-263). Operates on Windows/Web-browser platforms. No hardware components; does not interface with radiation delivery devices.
Indications for Use
Indicated for patients with malignant or benign disease in the head and neck, thoracic, abdominal, and pelvic regions requiring radiotherapy treatment planning. Intended for use by qualified radiation therapy professionals (medical physicists, oncologists, dosimetrists) under physician prescription.
Regulatory Classification
Identification
A medical charged-particle radiation therapy system is a device that produces by acceleration high energy charged particles (e.g., electrons and protons) intended for use in radiation therapy. This generic type of device may include signal analysis and display equipment, patient and equipment supports, treatment planning computer programs, component parts, and accessories.
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FDA U.S. FOOD & DRUG ADMINISTRATION
April 11, 2025
Oncospace, Inc.
Sigrid Schoepel
Regulatory Affairs
1812 Ashland Ave., Suite 100K
Baltimore, Maryland 21205
Re: K242748
Trade/Device Name: Oncospace
Regulation Number: 21 CFR 892.5050
Regulation Name: Medical Charged-Particle Radiation Therapy System
Regulatory Class: Class II
Product Code: MUJ
Dated: March 12, 2025
Received: March 12, 2025
Dear Sigrid Schoepel:
We have reviewed your section 510(k) premarket notification of intent to market the device referenced above and have determined the device is substantially equivalent (for the indications for use stated in the enclosure) to legally marketed predicate devices marketed in interstate commerce prior to May 28, 1976, the enactment date of the Medical Device Amendments, or to devices that have been reclassified in accordance with the provisions of the Federal Food, Drug, and Cosmetic Act (the Act) that do not require approval of a premarket approval application (PMA). You may, therefore, market the device, subject to the general controls provisions of the Act. Although this letter refers to your product as a device, please be aware that some cleared products may instead be combination products. The 510(k) Premarket Notification Database available at https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm identifies combination product submissions. The general controls provisions of the Act include requirements for annual registration, listing of devices, good manufacturing practice, labeling, and prohibitions against misbranding and adulteration. Please note: CDRH does not evaluate information related to contract liability warranties. We remind you, however, that device labeling must be truthful and not misleading.
If your device is classified (see above) into either class II (Special Controls) or class III (PMA), it may be subject to additional controls. Existing major regulations affecting your device can be found in the Code of Federal Regulations, Title 21, Parts 800 to 898. In addition, FDA may publish further announcements concerning your device in the Federal Register.
Additional information about changes that may require a new premarket notification are provided in the FDA guidance documents entitled "Deciding When to Submit a 510(k) for a Change to an Existing Device"
U.S. Food & Drug Administration
10903 New Hampshire Avenue
Silver Spring, MD 20993
www.fda.gov
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(https://www.fda.gov/media/99812/download) and "Deciding When to Submit a 510(k) for a Software Change to an Existing Device" (https://www.fda.gov/media/99785/download).
Your device is also subject to, among other requirements, the Quality System (QS) regulation (21 CFR Part 820), which includes, but is not limited to, 21 CFR 820.30, Design controls; 21 CFR 820.90, Nonconforming product; and 21 CFR 820.100, Corrective and preventive action. Please note that regardless of whether a change requires premarket review, the QS regulation requires device manufacturers to review and approve changes to device design and production (21 CFR 820.30 and 21 CFR 820.70) and document changes and approvals in the device master record (21 CFR 820.181).
Please be advised that FDA's issuance of a substantial equivalence determination does not mean that FDA has made a determination that your device complies with other requirements of the Act or any Federal statutes and regulations administered by other Federal agencies. You must comply with all the Act's requirements, including, but not limited to: registration and listing (21 CFR Part 807); labeling (21 CFR Part 801); medical device reporting (reporting of medical device-related adverse events) (21 CFR Part 803) for devices or postmarketing safety reporting (21 CFR Part 4, Subpart B) for combination products (see https://www.fda.gov/combination-products/guidance-regulatory-information/postmarketing-safety-reporting-combination-products); good manufacturing practice requirements as set forth in the quality systems (QS) regulation (21 CFR Part 820) for devices or current good manufacturing practices (21 CFR Part 4, Subpart A) for combination products; and, if applicable, the electronic product radiation control provisions (Sections 531-542 of the Act); 21 CFR Parts 1000-1050.
All medical devices, including Class I and unclassified devices and combination product device constituent parts are required to be in compliance with the final Unique Device Identification System rule ("UDI Rule"). The UDI Rule requires, among other things, that a device bear a unique device identifier (UDI) on its label and package (21 CFR 801.20(a)) unless an exception or alternative applies (21 CFR 801.20(b)) and that the dates on the device label be formatted in accordance with 21 CFR 801.18. The UDI Rule (21 CFR 830.300(a) and 830.320(b)) also requires that certain information be submitted to the Global Unique Device Identification Database (GUDID) (21 CFR Part 830 Subpart E). For additional information on these requirements, please see the UDI System webpage at https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance/unique-device-identification-system-udi-system.
Also, please note the regulation entitled, "Misbranding by reference to premarket notification" (21 CFR 807.97). For questions regarding the reporting of adverse events under the MDR regulation (21 CFR Part 803), please go to https://www.fda.gov/medical-devices/medical-device-safety/medical-device-reporting-mdr-how-report-medical-device-problems.
For comprehensive regulatory information about medical devices and radiation-emitting products, including information about labeling regulations, please see Device Advice (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-assistance) and CDRH Learn (https://www.fda.gov/training-and-continuing-education/cdrh-learn). Additionally, you may contact the Division of Industry and Consumer Education (DICE) to ask a question about a specific regulatory topic. See the DICE website (https://www.fda.gov/medical-devices/device-advice-comprehensive-regulatory-
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assistance/contact-us-division-industry-and-consumer-education-dice) for more information or contact DICE by email (DICE@fda.hhs.gov) or phone (1-800-638-2041 or 301-796-7100).
Sincerely,

Lora D. Weidner, Ph.D.
Assistant Director
Radiation Therapy Team
DHT8C: Division of Radiological
Imaging and Radiation Therapy Devices
OHT8: Office of Radiological Health
Office of Product Evaluation and Quality
Center for Devices and Radiological Health
Enclosure
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DEPARTMENT OF HEALTH AND HUMAN SERVICES
Food and Drug Administration
Indications for Use
Form Approved: OMB No. 0910-0120
Expiration Date: 07/31/2026
See PRA Statement below.
Submission Number (if known)
K242748
Device Name
Oncospace
Indications for Use (Describe)
Oncospace is used to configure and review radiotherapy treatment plans for a patient with malignant or benign disease in the head and neck, thoracic, abdominal, and pelvic regions. It allows for set up of radiotherapy treatment protocols, association of a potential treatment plan with the protocol(s), submission of a dose prescription and achievable dosimetric goals to a treatment planning system, and review of the treatment plan. It is intended for use by qualified, trained radiation therapy professionals (such as medical physicists, oncologists, and dosimetrists). This device is for prescription use by order of a physician.
Type of Use (Select one or both, as applicable)
☑ Prescription Use (Part 21 CFR 801 Subpart D)
☐ Over-The-Counter Use (21 CFR 801 Subpart C)
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ONCOSPACE
K242748
Page 1 of 10
510(k) Summary
I. SUBMITTER
Oncospace, Inc.
1812 Ashland Ave., Suite 100K
Baltimore, MD 21205 USA
Phone: 608-335-3176
Email: Sigrid.Schoepel@oncospace.com
Contact Person: Sigrid Schoepel
Date Prepared: 2025 March 7
II. DEVICE
Name of Device: Oncospace
Common or Usual Name: System, Planning, Radiation Therapy Treatment
Classification Name: Medical charged-particle radiation therapy system (21 CFR 892.5050)
Regulatory Class: II
Product Code: MUJ
III. PREDICATE DEVICE
Oncospace, K222803
This predicate device has not been the subject of a recall.
IV. DEVICE DESCRIPTION
The Oncospace software supports radiation oncologists and medical dosimetrists during radiotherapy treatment planning. The software includes locked machine learning algorithms. During treatment planning, the Oncospace software works in conjunction with, and does not replace, a treatment planning system (TPS).
The Oncospace software is intended to augment the treatment planning process by:
- allowing the radiation oncologist to select and customize a treatment planning protocol that includes dose prescription (number of fractions, dose per fraction, dose normalization), a delivery method (beam type and geometry), and protocol-based dosimetric goals/objectives for treatment targets, and organs at risk (OAR);
- predicting dosimetric goals/objectives for OARs based on patient-specific anatomical geometry;
oncospace.com
info@oncospace.com
1812 Ashland Ave., Suite 100K
Baltimore, MD 21205 USA
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ONCOSPACE
K242748
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- automating the initiation of plan optimization on a TPS by supplying the dose prescription, delivery method, protocol-based target objectives, and predicted OAR objectives;
- providing a user interface for plan evaluation against protocol-based and predicted goals.
Diagnosis and treatment decisions occur prior to treatment planning and do not involve Oncospace. Decisions involving Oncospace are restricted to setting of dosimetric goals for use during plan optimization and plan evaluation. Human judgement continues to be applied in accepting these goals and updating them as necessary during the iterative beam optimization process. Human judgement is also still applied as in standard practice during plan quality assessment; the protocol-based OAR goals are used as the primary means of plan assessment, with the role of the predicted goals being to provide additional information as to whether dose to an OAR may be able to be further lowered.
When Oncospace is used in conjunction with a TPS, the user retains full control of the TPS, including finalization of the treatment plan created for the patient. Oncospace also does not interface with the treatment machines. The risk to patient safety is lower than a TPS since it only informs the treatment plan, does not allow region of interest editing, does not make treatment decisions, and does not interface directly with the treatment machine or any record and verify system.
Oncospace’s OAR dose prediction approach, and the use of predictions in end-to-end treatment planning workflow, has been tested for use with a variety of cancer treatment plans. These included a wide range of target and OAR geometries, prescriptions and boost strategies (sequential and simultaneous delivery). Validity has thus been demonstrated for the range of prediction model input features encountered in the test cases. This range is representative of the diversity of the same feature types (describing target-OAR proximity, target and OAR shapes, sizes, etc.) encountered across all cancer sites. Given that the same feature types will be used in OAR dose prediction models trained for all sites, the modeling approach validated here is not cancer site specific, but rather is designed to predict OAR DVHs based on impactful features common to all sites. The software is designed to be used in the context of all forms of intensity-modulated photon beam radiotherapy. The planning objectives themselves are intended to be TPS-independent: these are instead dependent on the degree of organ sparing possible given the beam modality and range of delivery techniques for plans in the database. To facilitate streamlined transmission of DICOM files and plan parameters Oncospace includes scripts using the treatment planning system’s scripting language (for example, Pinnacle).
The Oncospace software includes an algorithm for transforming non-standardized OAR names used by treatment planners to standardized names defined by AAPM Task Group 263. This matching process primarily uses a table of synonyms that is updated as matches are made during use of the product, as well as a Natural Language Processing (NLP) model that attempts to match plan names not already in the synonym table. The NLP model selects the most likely match, which may be a correct match to a standard OAR name, an incorrect match, or no match (when the model considers this to be most likely, such as for names resembling a target). The user can also manually match names using a drop-down menu of all TG-263 OAR names. The user is instructed to check each automated match and make corrections using the drop-down menu as needed.
oncospace.com
info@oncospace.com
1812 Ashland Ave., Suite 100K
Baltimore, MD 21205 USA
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ONCOSPACE
K242748
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# V. INDICATIONS FOR USE
Oncospace is used to configure and review radiotherapy treatment plans for a patient with malignant or benign disease in the head and neck, thoracic, abdominal, and pelvic regions. It allows for set up of radiotherapy treatment protocols, association of a potential treatment plan with the protocol(s), submission of a dose prescription and achievable dosimetric goals to a treatment planning system, and review of the treatment plan. It is intended for use by qualified, trained radiation therapy professionals (such as medical physicists, oncologists, and dosimetrists). This device is for prescription use by order of a physician.
# VI. COMPARISON OF TECHNOLOGICAL CHARACTERISTICS WITH THE PREDICATE DEVICE
The Oncospace subject device is a software-only medical device that performs the same functions as the Oncospace predicate device. The following differences exist between the subject and predicate devices:
- The prostate, head, and neck models have been updated to improve performance.
- The thoracic, abdominal, and gynecological regions have been added using the same machine learning methods as in the predicate device.
Note: The subject device algorithms, and any future algorithm updates, are locked prior to clinical use.
| Element | Subject | Predicate | Conclusion |
| --- | --- | --- | --- |
| Device Name | Oncospace | Oncospace | Identical |
| 510(k) Owner | Oncospace, Inc. | Oncospace, Inc. | |
| 510(k) Number | -- | K222803 | |
| Product Code | MUJ | MUJ | Identical |
| Product Name | System, Planning, Radiation Therapy Treatment | System, Planning, Radiation Therapy Treatment | Identical |
| Intended Use | Oncospace is used to configure and review radiotherapy treatment plans for a patient with malignant or benign disease in the head and neck, thoracic, abdominal, and pelvic regions. It allows for set up of radiotherapy treatment protocols, association of a potential treatment plan with the protocol(s), submission of a dose prescription and achievable dosimetric goals to a treatment planning system, and review of the treatment plan. It is intended for use by qualified, trained radiation therapy professionals (such as medical physicists, oncologists, and dosimetrists). This device is for prescription use by order of a physician. | Oncospace is used to configure and review radiotherapy treatment plans for a patient with malignant or benign disease in the prostate, head, and neck regions. It allows for set up of radiotherapy treatment protocols, association of a potential treatment plan with the protocol(s), submission of a dose prescription and achievable dosimetric goals to a treatment planning system, and review of the treatment plan. It is intended for use by qualified, trained radiation therapy professionals (such as medical physicists, oncologists, and dosimetrists). This device is for prescription use by order of a physician. | Substantially Equivalent
Added thoracic and abdominal regions, added gynecological sites to expand the prostate reference to the pelvic region. |
oncospace.com
info@oncospace.com
1812 Ashland Ave., Suite 100K
Baltimore, MD 21205 USA
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ONCOSPACE
K242748
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| Element | Subject | Predicate | Conclusion |
| --- | --- | --- | --- |
| | This device is for prescription use by order of a physician. | | |
| Operating System | Windows/Web-browser | Windows/Web-browser | Identical |
| Platform | Client-Server (Clinic-provided client machines, cloud Windows servers controlled by Oncospace) | Client-Server (Clinic-provided client machines, cloud Windows servers controlled by Oncospace) | Identical |
| DICOM-RT Compliant | Yes | Yes | Identical |
| Full Treatment Planning System | No | No | Identical |
| Connected to or Controlling of Radiation Delivery Devices | No | No | Identical |
| Typical Users | Medical professionals, including but not limited to, radiation oncologists, medical physicists or physicians. | Medical professionals, including but not limited to, radiation oncologists, medical physicists or physicians. | Identical |
| Patient Population | There are no demographic, regional, or cultural limitations for patients. It is up to the user to determine if the system can be used for a patient. | There are no demographic, regional, or cultural limitations for patients. It is up to the user to determine if the system can be used for a patient. | Identical |
| Body Regions | Head and neck
Thoracic
Abdominal
Pelvic
The thoracic, abdominal, and gynecological regions have been added using the same machine learning methods as in the predicate device. | Head and neck
Prostate | New:
Gynecologic, thoracic, and abdominal organs
Identical: Head and neck, prostate |
| Environment | The system can be used in a hospital environment or in a doctor’s office. | The system can be used in a hospital environment or in a doctor’s office. | Identical |
| JPEG image support | Yes | Yes | Identical |
| Import Treatment Plans | Yes. Import existing plans from third-party systems to compare dose objectives against templates. | Yes. Import existing plans from third-party systems to compare dose objectives against templates. | Identical |
oncospace.com
info@oncospace.com
1812 Ashland Ave., Suite 100K
Baltimore, MD 21205 USA
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| Element | Subject | Predicate | Conclusion |
| --- | --- | --- | --- |
| “Template” Treatment Plans | Yes. Factory-default plans with dose goals exist and users can configure a dose template. | Yes. Factory-default plans with dose goals exist and users can configure a dose template. | Identical |
| Automatic Initial Tumor Selection | Yes. Regions of interest are matched as the study is opened in the device. Users can adjust or match to more available regions of interest. | Yes. Regions of interest are matched as the study is opened in the device. Users can adjust or match to more available regions of interest. | Identical |
| Dose Objective Comparison | Yes. Comparisons can be done between more than one selected treatment plan. Dose is based on calculated dose and curated, gold-standard treatment plans. | Yes. Comparisons can be done between more than one selected treatment plan. Dose is based on calculated dose and curated, gold-standard treatment plans. | Identical |
| Image Viewer Capabilities | Yes. Display, pan, zoom, scroll, windowing, viewport layout. | Yes. Display, pan, zoom, scroll, windowing, viewport layout. | Identical |
| Calculate and Display Isodose Lines | Yes | Yes | Identical |
| Calculate and Display Dose Volume Histograms | Yes | Yes | Identical |
| Compare Dose from Multiple Plans | Yes | Yes | Identical |
| Dose Summation/ Treatment-Over-Time Data | Yes | Yes | Identical |
| Plan Review | Yes. Contains features for review of isodose lines, review of DVHs, dose comparison and dose summation. | Yes. Contains features for review of isodose lines, review of DVHs, dose comparison and dose summation. | Identical |
| Export Plan Information | Yes. Can export the selected plan for review and setup by a dosimetrist.
Oncospace does not export a final plan, it will not export to a record-and-verify system. | Yes. Can export the selected plan for review and setup by a dosimetrist.
Oncospace does not export a final plan, it will not export to a record-and-verify system. | Identical |
oncospace.com
info@oncospace.com
1812 Ashland Ave., Suite 100K
Baltimore, MD 21205 USA
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ONCOSPACE
K242748
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# VII. PERFORMANCE DATA
The following verification and validation testing results (performance data) support the substantial equivalence determination. Since this is a software-only medical device that does not control other devices the performance data does not include biocompatibility, electrical safety, electromagnetic compatibility, mechanical, acoustic, or animal testing.
The verification tests met all system requirements and acceptance criteria which address clinical, standard user interface, and cybersecurity requirements for the Oncospace device.
The validation testing for dose prediction was performed using retrospective clinical data. The Oncospace device's purpose is to reduce the effort needed to achieve a clinically viable and deliverable radiation treatment plan by supplying a treatment planning system with patient-specific plan optimization objectives derived from Oncospace dose predictions. Thus a trial of clinical performance was designed to demonstrate that plan quality, as represented by mean organ-at-risk (OAR) dose sparing, is non-inferior to that of plans created without use of Oncospace.. A comparison of target coverage was also made, because a comparison of OAR sparing is only valid if target coverage is maintained. Reduction in effort for the plans created with use of Oncospace was ensured by the planner using only the OAR optimization objective dose values supplied by Oncospace (rather than being allowed to adjust them via the usual trial-and-error process). Sample sizes were determined by estimating variance in mean OAR dose so that the trial would have 80% power at a significance level of 0.05 and a non-inferiority margin of 10 Gy. The head and neck and prostate models have previously undergone clinical performance testing, so for these only model performance testing (comparison of predicted dose values to ground truth values) was repeated here. Please note the distinction here between clinical performance testing (maintaining plan quality when Oncospace-derived objectives are used) and model performance testing (model accuracy).
Table 1 summarizes the characteristics of the datasets used for model development and internal testing, external performance testing, and clinical validation. Some datasets were fully decoupled from the medical record such that technique information could not be linked.
| Anatomical Location | Development (Training/Tuning) and Internal Performance Testing Dataset (randomly split 80/20) | External Performance Test Dataset(s) | Clinical Validation Dataset |
| --- | --- | --- | --- |
| Head and Neck | 1145 treatment plans for patients who received radiation therapy for HNC at Johns Hopkins University between 2008-2019.
Plans were required to exhibit 90% target coverage.
10% of the plans were single-target and 90% were SIB (19% 2-target, 51% 3-target, 20% 4-target).
96% of plans had a total prescribed dose >= 60 Gy, 4% had a total prescribed dose < 60 Gy. | Dataset A: 265 patients who received radiation therapy for HNC at Institution_2.
Plans were required to exhibit 90% target coverage.
11% of the plans were single-target and 89% were SIB (3% 2-target, 41% 3-target, 45% 4-target). 98% of plans had a total prescribed dose >= 60 Gy, 2% had a total prescribed dose < 60 Gy. | 18 patients who received radiation therapy for HNC at JHU between 2021-2022. This included plans with 1-4 target dose levels, 10 to 48 fractions, in a variety of anatomical locations: lip, larynx, base of tongue, parotid, nasopharynx, scalp, neck, etc. HN was clinically validated in a previous 510k submission. |
oncospace.com
info@oncospace.com
1812 Ashland Ave., Suite 100K
Baltimore, MD 21205 USA
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ONCOSPACE
K242748
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| Anatomical Location | Development (Training/Tuning) and Internal Performance Testing Dataset (randomly split 80/20) | External Performance Test Dataset(s) | Clinical Validation Dataset |
| --- | --- | --- | --- |
| | The dataset contained 35% IMRT, 27% VMAT, 15% tomotherapy, 2% 3D conformal, and 22% unspecified plans. | Dataset B: 27 patients who received radiation therapy for HNC at Institution_3. Plans were required to exhibit 90% target coverage. All plans had 3 targets and total prescribed dose >= 60 Gy. | |
| Thoracic | 1623 treatment plans (1437 lung and 186 esophagus) for patients who received radiation therapy for thoracic cancer at Johns Hopkins University between 2008-2019. Plans were required to exhibit 92% target coverage. 82% of plans had a total prescribed dose >= 45 Gy, 18% had a total prescribed dose < 45 Gy. 57% of plans had conventional fractionation (dose per fraction <2.3Gy) and 43% were hypo-fractionated plans. | | 20 patients (14 lung and 6 esophagus) who received radiation therapy at JHU between 2021-2024. This included targets in multiple locations within the lungs and esophagus, including single-target, SIB, and multi-phase courses, treated with conventional- and hypo-fractionation, and SBRT. |
| Abdominal | 712 treatment plans for patients who received radiation therapy for pancreatic cancer at Johns Hopkins University between 2008-2019, and 69 treatment plans from patients who received radiation therapy for liver cancer at Montefiore Einstein Comprehensive Cancer Center. Plans were required to exhibit 85% target coverage. 3% of plans had conventional fractionation (dose per fraction | | 17 patients (11 pancreas and 6 liver) who received radiation therapy for at JHU between 2021-2024. This included single-target and SIB courses, treated with conventional- and hypo-fractionation and SBRT. |
oncospace.com
info@oncospace.com
1812 Ashland Ave., Suite 100K
Baltimore, MD 21205 USA
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ONCOSPACE
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| Anatomical Location | Development (Training/Tuning) and Internal Performance Testing Dataset (randomly split 80/20) | External Performance Test Dataset(s) | Clinical Validation Dataset |
| --- | --- | --- | --- |
| | <2.3Gy) and 97% were hypo-fractionated plans. | | |
| Pelvis | 1785 treatment plans (1662 prostate and 123 gynecological) for patients who received radiation therapy for pelvic cancer at Johns Hopkins University between 2008-2019.
Plans were required to exhibit 94% target coverage.
98% of plans had conventional fractionation (dose per fraction <2.3Gy) and 2% were hypo-fractionated plans.
37% of plans had at least one phase with a nodal PTV, and 63% did not.
The Prostate dataset used in the Pelvis model contained 15% IMRT, 46% VMAT, 14% tomotherapy, and 25% unspecified plans. | 40 patients who received radiation therapy for prostate cancer at Institution_3. Plans were required to exhibit 94% target coverage. All plans had conventional fractionation. | 17 patients (12 prostate and 5 gynecological) who received radiation therapy at JHU between 2021-2024. This included single-target and SIB courses, treated with conventional- and hypo-fractionation and SBRT. Prostate was clinically validated in previous 510k submission. |
Table 1. Dataset characteristics.
Clinical performance testing involved comparison of plans generated with the aid of the Oncospace software to heterogenous sets of traditionally-planned clinical treatment plans.
Model performance testing involved comparison of DVH difference metrics with acceptance criteria.
- For the head and neck model, clinical performance testing was previously performed for the following OARs: brain, brainstem, spinal cord, left and right cranial nerve VIII (acoustic nerve), left and right parotid glands, left and right eyes, left and right lens, left and right optic nerve, optic chiasm, oral cavity, soft palate, glottis, cricopharyngeus, esophagus, sublingual gland, mandible bone, left and right submandibular glands, left and right cochlea, thyroid gland, and pharyngeal constrictor muscle(s).
- For the abdominal model, clinical performance testing was performed for the following OARs: bowel, duodenum, heart, left and right kidneys, liver, spinal canal, and stomach.
- For the thoracic model, clinical performance testing was performed for the following OARs: esophagus, heart, left and right kidneys, liver, left and right lungs, both lungs, spinal canal, stomach, and trachea.
- For the pelvis model, clinical performance testing for gynecological was performed for the following OARs: bladder, bowel, left and right femur heads, rectum, sigmoid colon. Previously,
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info@oncospace.com
1812 Ashland Ave., Suite 100K
Baltimore, MD 21205 USA
{12}
ONCOSPACE
K242748
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clinical performance testing for prostate was performed for the following OARs: bladder, left and right femur heads, rectum, sigmoid colon, penile bulb, and bowel bag.
In clinical performance testing, for the plans using Oncospace, mean dose was statistically significantly lower for 5 OARs for abdominal and 4 OAR for pelvis (gynecological), and there were no statistically significant differences in mean dose for any of the remaining 11 OARs for thoracic, 3 OARs for abdominal, and 2 OARs for pelvis (gynecological). The trial demonstrated non-inferiority of mean OAR dose to 2.2 Gy for thoracic, 1 Gy for abdominal, and 1.9 Gy for pelvis (gynecological). There was no statistically significant difference in target coverage between clinical plans and plans created with use of the Oncospace system. Out of all the plans tested no plan required more optimization cycles using Oncospace versus using traditional radiation treatment planning clinical workflow.
All models met acceptance criteria for internal model performance. In external model performance testing for H&N plans, for Institution 2, mean absolute error in OAR DVH dose values is within 5% of the prescription dose value for 9/12 OARs, and does not exceed 9% for any OARs; for Institution 3, mean absolute error in OAR DVH dose values is within 5% of the prescription dose value for 10/12 OARs, and does not exceed 8% for any OARs. In external model performance testing for prostate plans, for Institution 3, mean absolute error in OAR DVH dose values is within 5% of the prescription dose value for 4/6 OARs, are 5.1% for one OAR, and 15.9% for one OAR. The small systematic differences between predicted and actual dose values are as expected given inter-institutional differences/preferences in contouring and in trade-offs between target coverage and OAR sparing.
The NLP model used for transforming non-standardized OAR names to standardized TG-263 names was trained using known name matches in the prostate, gynecological, head and neck, thoracic, and pancreas cancer datasets licensed to Oncospace by Johns Hopkins University. The model has undergone 5-fold cross validation, and external validation, for each anatomic region. During 5-fold cross-validation all models achieved a validation macro-averaged F1 score above 0.92 and accuracy above 96% for classifying previously unseen terms. The external testing dataset contained a total of 221 structures with 145 unique original names. The model correctly matched 207/221 (94.1%) of all structure names, or 131/145 (91.0%) unique structure names.
## Conclusion
Verification and validation (including performance testing) was conducted in accordance with FDA guidance recommendations to confirm the device design met all specifications, user needs, and was acceptable to qualified clinical users. Oncospace has passed all the tests and the provided testing results demonstrate safety and effectiveness as compared to the predicate device. It is therefore concluded that Oncospace is substantially equivalent to the predicate device.
oncospace.com
info@oncospace.com
1812 Ashland Ave., Suite 100K
Baltimore, MD 21205 USA
{13}
ONCOSPACE
K242748
Page 10 of 10
# VIII. CONCLUSIONS
The subject Oncospace device is similar in intended use and functionality to the predicate Oncospace device. Oncospace has the same technological characteristics and features as the previously cleared device and does not raise new questions of safety or efficacy compared to the predicate device as demonstrated through the system design and testing.
Non-clinical and clinical verification, validation, and performance testing was conducted to confirm the device design met user needs and specifications and was acceptable to qualified clinical and non-clinical users. Oncospace has passed the verification and validation tests and provided clinical performance testing results with a library clinical dataset in order to demonstrate safety or effectiveness as compared to the predicate device. It is therefore concluded that the subject Oncospace device is substantially equivalent to the predicate Oncospace device.
oncospace.com
info@oncospace.com
1812 Ashland Ave., Suite 100K
Baltimore, MD 21205 USA
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Learn the FDA Browser
Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.