AI-derived Tumor Volume from Multiparametric MRI and Outcomes in Localized Prostate Cancer

Background An artificial intelligence (AI)-based method for measuring intraprostatic tumor volume based on data from MRI may provide prognostic information. Purpose To evaluate whether the total volume of intraprostatic tumor from AI-generated segmentations (V<sub>AI</sub>) provides independent prognostic information in patients with localized prostate cancer treated with radiation therapy (RT) or radical prostatectomy (RP). Materials and Methods For this retrospective, single-center study (January 2021 to August 2023), patients with cT1-3N0M0 prostate cancer who underwent MRI and were treated with RT or RP were identified. Patients who underwent RT were randomly divided into cross-validation and test RT groups. An AI segmentation algorithm was trained to delineate Prostate Imaging Reporting and Data System (PI-RADS) 3-5 lesions in the cross-validation RT group before providing segmentations for the test RT and RP groups. Cox regression models were used to evaluate the association between V<sub>AI</sub> and time to metastasis and adjusted for clinical and radiologic factors for combined RT (ie, cross-validation RT and test RT) and RP groups. Areas under the receiver operating characteristic curve (AUCs) were calculated for V<sub>AI</sub> and National Comprehensive Cancer Network (NCCN) risk categorization for prediction of 5-year metastasis (RP group) and 7-year metastasis (combined RT group). Results Overall, 732 patients were included (combined RT group, 438 patients; RP group, 294 patients). Median ages were 68 years (IQR, 62-73 years) and 61 years (IQR, 56-66 years) for the combined RT group and the RP group, respectively. V<sub>AI</sub> was associated with metastasis in the combined RT group (median follow-up, 6.9 years; adjusted hazard ratio [AHR], 1.09 per milliliter increase; 95% CI: 1.04, 1.15; <i>P</i> = .001) and the RP group (median follow-up, 5.5 years; AHR, 1.22; 95% CI: 1.08, 1.39; <i>P</i> = .001). AUCs for 7-year metastasis for the combined RT group for V<sub>AI</sub> and NCCN risk category were 0.84 (95% CI: 0.74, 0.94) and 0.74 (95% CI: 0.80, 0.98), respectively (<i>P</i> = .02). Five-year AUCs for the RP group for V<sub>AI</sub> and NCCN risk category were 0.89 (95% CI: 0.80, 0.98) and 0.79 (95% CI: 0.64, 0.94), respectively (<i>P</i> = .25). Conclusion The volume of AI-segmented lesions was an independent, prognostic factor for localized prostate cancer. © RSNA, 2024 <i>Supplemental material is available for this article.</i>

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Journal
Radiology
Published
2024-10-01
DOI
https://doi.org/10.1148/radiol.240041
Citations
19
Primary Topic
Prostate Cancer Diagnosis and Treatment
Type
article
Field-Weighted Citation Impact
7.96
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article

AI-derived Tumor Volume from Multiparametric MRI and Outcomes in Localized Prostate Cancer

David D. Yang, Atchar Sudhyadhom, Paul Linh Nguyen, Kee‐Young Shin et al.
19 citations
Radiology
Prostate Cancer Diagnosis and Treatment
7.96
article

AI-derived Tumor Volume from Multiparametric MRI and Outcomes in Localized Prostate Cancer

David D. Yang, Atchar Sudhyadhom, Paul Linh Nguyen, Kee‐Young Shin, Heather M. McClure, Jonathan E. Leeman, Mary‐Ellen Taplin, James Man Git Tsui, Martin T. King, Peter F. Orio, Neil E. Martin, Katie Nadine Lee, Anthony V. D’Amico, Kent W. Mouw, Quoc‐Dien Trinh, Leslie K. Lee, Christian V. Guthier
article en
19 citations

Abstract

Background An artificial intelligence (AI)-based method for measuring intraprostatic tumor volume based on data from MRI may provide prognostic information. Purpose To evaluate whether the total volume of intraprostatic tumor from AI-generated segmentations (V<sub>AI</sub>) provides independent prognostic information in patients with localized prostate cancer treated with radiation therapy (RT) or radical prostatectomy (RP). Materials and Methods For this retrospective, single-center study (January 2021 to August 2023), patients with cT1-3N0M0 prostate cancer who underwent MRI and were treated with RT or RP were identified. Patients who underwent RT were randomly divided into cross-validation and test RT groups. An AI segmentation algorithm was trained to delineate Prostate Imaging Reporting and Data System (PI-RADS) 3-5 lesions in the cross-validation RT group before providing segmentations for the test RT and RP groups. Cox regression models were used to evaluate the association between V<sub>AI</sub> and time to metastasis and adjusted for clinical and radiologic factors for combined RT (ie, cross-validation RT and test RT) and RP groups. Areas under the receiver operating characteristic curve (AUCs) were calculated for V<sub>AI</sub> and National Comprehensive Cancer Network (NCCN) risk categorization for prediction of 5-year metastasis (RP group) and 7-year metastasis (combined RT group). Results Overall, 732 patients were included (combined RT group, 438 patients; RP group, 294 patients). Median ages were 68 years (IQR, 62-73 years) and 61 years (IQR, 56-66 years) for the combined RT group and the RP group, respectively. V<sub>AI</sub> was associated with metastasis in the combined RT group (median follow-up, 6.9 years; adjusted hazard ratio [AHR], 1.09 per milliliter increase; 95% CI: 1.04, 1.15; <i>P</i> = .001) and the RP group (median follow-up, 5.5 years; AHR, 1.22; 95% CI: 1.08, 1.39; <i>P</i> = .001). AUCs for 7-year metastasis for the combined RT group for V<sub>AI</sub> and NCCN risk category were 0.84 (95% CI: 0.74, 0.94) and 0.74 (95% CI: 0.80, 0.98), respectively (<i>P</i> = .02). Five-year AUCs for the RP group for V<sub>AI</sub> and NCCN risk category were 0.89 (95% CI: 0.80, 0.98) and 0.79 (95% CI: 0.64, 0.94), respectively (<i>P</i> = .25). Conclusion The volume of AI-segmented lesions was an independent, prognostic factor for localized prostate cancer. © RSNA, 2024 <i>Supplemental material is available for this article.</i>

RadiologyVol. 313(1)
Norwegian Womens Public Health Association (NO)
Good health and well-being
Openalex Percentile: Top 2%
Prostate Cancer Diagnosis and Treatment
7.96
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