Association between Artificial Intelligence-Derived Tumor Volume and Oncologic Outcomes for Localized Prostate Cancer Treated with Radiation Therapy.
PURPOSE/OBJECTIVE(S): 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. We evaluated 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).MATERIALS/METHODS: We conducted a retrospective study of 438 patients with localized prostate cancer who underwent an endorectal coil, high B-value, 3-Tesla mpMRI and were treated with definitive RT at our institution between 2010 and 2017. We utilized the publicly available nnUNet to train a segmentation model which was used to identify the DIL. We examined the association between the artificial intelligence (AI)-generated DIL volume and oncologic outcomes, including biochemical recurrence and metastasis risk, using cause-specific Cox regression and time-dependent receiver operating characteristic analysis.RESULTS: The AI model identified DILs with an area under the receiver operating characteristic (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, there were 28 biochemical failures. AI-defined DIL volume was significantly associated with biochemical failure (adjusted hazard ratio 1.60, 95% confidence interval 1.14-2.24, p = 0.007) after adjustment for PI-RADS score. Among all 438 patients with a median follow-up of 6.9 years, there were 49 biochemical failures and 22 metastases. The AUROC for predicting 7-year biochemical failure for AI volume (0.790) was similar to that for 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 category (0.854 vs 0.769, p = 0.06).CONCLUSION: An AI algorithm using deep learning 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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International journal of radiation oncology, biology, physics