Evaluating the prognostic significance of artificial intelligence-delineated gross tumor volume and prostate volume measurements for prostate radiotherapy.
Authors
Affiliations (5)
Affiliations (5)
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Lakeridge Health, Durham Regional Cancer Centre, Ontario, Canada. Electronic address: [email protected].
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; The Ottawa Hospital, Ontario, Canada.
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; McGill University Health Centre, Quebec, Canada.
- Department of Radiation Oncology, Brigham and Women's Hospital/Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Department of Radiology, Brigham and Women's Hospital, USA.
Abstract
Artificial intelligence (AI) may extract prognostic information from MRI for localized prostate cancer. We evaluate whether AI-derived prostate and gross tumor volume (GTV) are associated with toxicity and oncologic outcomes after radiotherapy. We conducted a retrospective study of patients, who underwent radiotherapy between 2010 and 2017. We trained an AI segmentation algorithm to contour the prostate and GTV from patients treated with external-beam RT, and applied the algorithm to those treated with brachytherapy. AI prostate and GTV volumes were calculated from segmentation results. We evaluated whether AI GTV volume was associated with biochemical failure (BF) and metastasis. We evaluated whether AI prostate volume was associated with acute and late grade 2+ genitourinary toxicity, and International Prostate Symptom Score (IPSS) resolution for monotherapy and combination sets, separately. We identified 187 patients who received brachytherapy (monotherapy (N = 154) or combination therapy (N = 33)). AI GTV volume was associated with BF (hazard ratio (HR):1.28[1.14,1.44];p < 0.001) and metastasis (HR:1.34[1.18,1.53;p < 0.001). For the monotherapy subset, AI prostate volume was associated with both acute (adjusted odds ratio:1.16[1.07,1.25];p < 0.001) and late grade 2 + genitourinary toxicity (adjusted HR:1.04[1.01,1.07];p = 0.01), but not IPSS resolution (0.99[0.97,1.00];p = 0.13). For the combination therapy subset, AI prostate volume was not associated with either acute (p = 0.72) or late (p = 0.75) grade 2 + urinary toxicity. However, AI prostate volume was associated with IPSS resolution (0.96[0.93, 0.99];p = 0.01). AI-derived prostate and GTV volumes may be prognostic for toxicity and oncologic outcomes after RT. Such information may aid in treatment decision-making, given differences in outcomes among RT treatment modalities.