Association Between Artificial Intelligence-Derived Tumor Volume and Oncologic Outcomes for Localized Prostate Cancer Treated with Radiation Therapy
Bibliographic record
Abstract
Background Although clinical features of multi-parametric magnetic resonance imaging (mpMRI) have been associated with biochemical recurrence in localized prostate cancer, such features are subject to inter-observer variability. Objective To evaluate whether the volume of the dominant intraprostatic lesion (DIL), as provided by a deep learning segmentation algorithm, could provide prognostic information for patients treated with definitive radiation therapy (RT). Design, Setting, and Participants Retrospective study of 438 patients with localized prostate cancer who underwent an endorectal coil, high B-value, 3-Tesla mpMRI and were treated with RT between 2010 and 2017. Intervention RT. Outcome Measurements and Statistical Analysis Biochemical recurrence and metastasis risk, assessed with a cause-specific Cox regression and time-dependent receiver operating characteristic analysis. Results and Limitations The artificial intelligence (AI) model identified DILs with an area under the receiver operating characteristic curve (AUROC) of 0.827 at the patient level. For the 233 patients with available PI-RADS scores, with a median follow-up of 5.6 years, AI-defined DIL volume was significantly associated with biochemical failure (adjusted hazard ratio 1.54, 95% confidence interval 1.09-2.17, p=0.014) after adjustment for PI-RADS score. Among all 438 patients with a median follow-up of 6.9 years, the AUROC for predicting 7-year biochemical failure for AI volume (0.790) was similar to that for an expanded National Comprehensive Cancer Network (NCCN+) category (p=0.17). The AUROC for predicting 7-year metastasis for AI volume trended towards being higher compared to NCCN+ categories (0.854 vs 0.769, p=0.06). Conclusions A deep learning algorithm could identify the DIL with good performance. AI-defined DIL volume may be able to provide prognostic information independent of the NCCN+ risk group or other radiologic factors for patients with localized prostate cancer treated with RT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".