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About

Challenge Performance

Date	Challenge	Phase	Rank
June 17, 2022	PI-CAI	Open Development Phase - Tuning	359
April 23, 2024	PI-CAI	Open Development Phase - Testing	25

Model Facts

Summary

This algorithm represents the baseline supervised nnDetection algorithm from the PI-CAI challenge [1]. This algorithm is trained using the PI-CAI Public Training and Development Dataset v2.0 (1295 cases). This algorithm will perform two tasks: localize and classify each lesion with clinically significant prostate cancer (if any) using a 0–100% likelihood score and classify the overall case using a 0–100% likelihood score for clinically significant prostate cancer diagnosis.

To this end, this model uses biparametric MRI data. Specifically, this algorithm uses the axial T2-weighted MRI scan, the axial apparent diffusion coefficient map, and the calculated or acquired axial high b-value scan.

1. A. Saha, J. S. Bosma, J. J. Twilt, B. van Ginneken, A. Bjartell, A. R. Padhani, D. Bonekamp, G. Villeirs, G. Salomon, G. Giannarini, J. Kalpathy-Cramer, J. Barentsz, K. H. Maier-Hein, M. Rusu, O. Rouvière, R. van den Bergh, V. Panebianco, V. Kasivisvanathan, N. A. Obuchowski, D. Yakar, M. Elschot, J. Veltman, J. J. Fütterer, M. de Rooij, H. Huisman, and the PI-CAI consortium. “Artificial Intelligence and Radiologists in Prostate Cancer Detection on MRI (PI-CAI): An International, Paired, Non-Inferiority, Confirmatory Study”. The Lancet Oncology 2024; 25(7): 879-887. doi:10.1016/S1470-2045(24)00220-1

Mechanism

This algorithm is a deep learning-based detection/diagnosis model, which ensembles 5 independent nnDetection models (5-fold cross-validation). To train these models, a total of 1295 prostate biparametric MRI (bpMRI) scans paired with human-annotated ISUP ≥ 2 delineations were used.

Source Code:

• Algorithm inference
• Algorithm training

Validation and Performance

This algorithm was evaluated on the PI-CAI Testing Cohort. This hidden testing cohort included prostate MRI examinations from 1000 patients across four centers, including 197 cases from an external unseen center. Histopathology and a follow-up period of at least 3 years were used to establish the reference standard. See the PI-CAI paper for more information [1].

Patient-level diagnosis performance is evaluated using the Area Under Receiver Operating Characteristic (AUROC) metric. Lesion-level detection performance is evaluated using the Average Precision (AP) metric. Overall score used to rank each AI algorithm is the average of both task-specific metrics: Overall Ranking Score = (AP + AUROC) / 2.

This algorithm achieved an AUROC of 0.785 an AP of 0.386, and an Overal Ranking Score of 0.586.

Free-Response Receiver Operating Characteristic (FROC) curve is used for secondary analysis of AI detections (as recommended in Penzkofer et al., 2022). We highlight the performance on the FROC curve using the SensX metric. SensX refers to the sensitivity of a given AI system at detecting clinically significant prostate cancer (i.e., Gleason grade group ≥ 2 lesions) on MRI, given that it generates the same number of false positives per examination as the PI-RADS ≥ X operating point of radiologists. Here, by radiologists, we refer to the radiology readings that were historically made for these cases during multidisciplinary routine practice. Across the PI-CAI testing leaderboards (Open Development Phase - Testing Leaderboard, Closed Testing Phase - Testing Leaderboard), SensX is computed at thresholds that are specific to the testing cohort (i.e., depending on the radiology readings and set of cases).

This algorithm achieved a Sens3 of 0.513, a Sens4 of 0.465, and a Sens5 of 0.299.

1. A. Saha, J. S. Bosma, J. J. Twilt, B. van Ginneken, A. Bjartell, A. R. Padhani, D. Bonekamp, G. Villeirs, G. Salomon, G. Giannarini, J. Kalpathy-Cramer, J. Barentsz, K. H. Maier-Hein, M. Rusu, O. Rouvière, R. van den Bergh, V. Panebianco, V. Kasivisvanathan, N. A. Obuchowski, D. Yakar, M. Elschot, J. Veltman, J. J. Fütterer, M. de Rooij, H. Huisman, and the PI-CAI consortium. “Artificial Intelligence and Radiologists in Prostate Cancer Detection on MRI (PI-CAI): An International, Paired, Non-Inferiority, Confirmatory Study”. The Lancet Oncology 2024; 25(7): 879-887. doi:10.1016/S1470-2045(24)00220-1

Uses and Directions

• For research use only. This algorithm is intended to be used only on biparametric prostate MRI examinations of patients with raised PSA levels or clinical suspicion of prostate cancer. This algorithm should not be used in different patient demographics.

• Benefits: AI-based risk stratification for clinically significant prostate cancer using prostate MRI can potentially aid the diagnostic pathway of prostate cancer, reducing over-treatment and unnecessary biopsies.

• Target population: This algorithm was trained on patients with raised PSA levels or clinical suspicion of prostate cancer, without prior treatment (e.g. radiotherapy, transurethral resection of the prostate (TURP), transurethral ultrasound ablation (TULSA), cryoablation, etc.), without prior positive biopsies, without artifacts, and with reasonably-well aligned sequences.

• MRI scanner: This algorithm was trained and evaluated exclusively on prostate biparametric MRI scans acquired with various commercial 1.5 Tesla or 3 Tesla scanners using surface coils from Siemens Healthineers, Erlangen, Germany or Philips Medical Systems, Eindhoven, Netherland. It does not account for vendor-neutral properties or domain adaptation, and in turn, the compatibility with scans derived using any other MRI scanner or those using endorectal coils is unkown.

• Sequence alignment and position of the prostate: While the input images (T2W, HBV, ADC) can be of different spatial resolutions, the algorithm assumes that they are co-registered or aligned reasonably well.

• General use: This model is intended to be used by radiologists for predicting clinically significant prostate cancer in biparametric MRI examinations. The model is not a diagnostic for cancer and is not meant to guide or drive clinical care. This model is intended to complement other pieces of patient information in order to determine the appropriate follow-up recommendation.

• Appropriate decision support: The model identifies lesion X as at a high risk of being malignant. The referring radiologist reviews the prediction along with other clinical information and decides the appropriate follow-up recommendation for the patient.

• Before using this model: Test the model retrospectively and prospectively on a diagnostic cohort that reflects the target population that the model will be used upon to confirm the validity of the model within a local setting.

• Safety and efficacy evaluation: To be determined in a clinical validation study.

Warnings

• Risks: Even if used appropriately, clinicians using this model can misdiagnose cancer. Delays in cancer diagnosis can lead to metastasis and mortality. Patients who are incorrectly treated for cancer can be exposed to risks associated with unnecessary interventions and treatment costs related to follow-ups.

• Inappropriate Settings: This model was not trained on MRI examinations of patients with prior treatment (e.g. radiotherapy, transurethral resection of the prostate (TURP), transurethral ultrasound ablation (TULSA), cryoablation, etc.), prior positive biopsies, artifacts or misalignment between sequences. Hence it is susceptible to faulty predictions and unintended behaviour when presented with such cases. Do not use the model in the clinic without further evaluation.

• Clinical rationale: The model is not interpretable and does not provide a rationale for high risk scores. Clinical end users are expected to place the model output in context with other clinical information to make the final determination of diagnosis.

• Inappropriate decision support: This model may not be accurate outside of the target population. This model is not designed to guide clinical diagnosis and treatment for prostate cancer.

• Generalizability: This model was developed with prostate MRI examinations from Radboud University Medical Center, Ziekenhuisgroep Twente, and Prostaat Centrum Noord-Nederland. Do not use this model in an external setting without further evaluation.

• Discontinue use if: Clinical staff raise concerns about the utility of the model for the intended use case or large, systematic changes occur at the data level that necessitate re-training of the model.

Common Error Messages

Information on this algorithm has been provided by the Algorithm Editors, following the Model Facts labels guidelines from Sendak, M.P., Gao, M., Brajer, N. et al. Presenting machine learning model information to clinical end users with model facts labels. npj Digit. Med. 3, 41 (2020). 10.1038/s41746-020-0253-3