Pegah Khosravi, Shady Saikali, Abolfazl Alipour, Saber Mohammadi, Maxwell Boger, Dalanda M Diallo, Christopher J Smith, Marcio C Moschovas, Iman Hajirasouliha, Andrew J Hung, Srirama S Venkataraman, Vipul Patel
{"title":"AutoRadAI:一个多功能的人工智能框架,用于检测前列腺癌的囊外延伸。","authors":"Pegah Khosravi, Shady Saikali, Abolfazl Alipour, Saber Mohammadi, Maxwell Boger, Dalanda M Diallo, Christopher J Smith, Marcio C Moschovas, Iman Hajirasouliha, Andrew J Hung, Srirama S Venkataraman, Vipul Patel","doi":"10.1093/biomethods/bpaf032","DOIUrl":null,"url":null,"abstract":"<p><p>Preoperative identification of extracapsular extension (ECE) in prostate cancer (PCa) is crucial for effective treatment planning, as ECE presence significantly increases the risk of positive surgical margins and early biochemical recurrence following radical prostatectomy. AutoRadAI, an innovative artificial intelligence (AI) framework, was developed to address this clinical challenge while demonstrating broader potential for diverse medical imaging applications. The framework integrates T2-weighted MRI data with histopathology annotations, leveraging a dual convolutional neural network (multi-CNN) architecture. AutoRadAI comprises two key components: ProSliceFinder, which isolates prostate-relevant MRI slices, and ExCapNet, which evaluates ECE likelihood at the patient level. The system was trained and validated on a dataset of 1001 patients (510 ECE-positive, 491 ECE-negative cases). ProSliceFinder achieved an area under the ROC curve (AUC) of 0.92 (95% confidence interval [CI]: 0.89-0.94) for slice classification, while ExCapNet demonstrated robust performance with an AUC of 0.88 (95% CI: 0.83-0.92) for patient-level ECE detection. Additionally, AutoRadAI's modular design ensures scalability and adaptability for applications beyond ECE detection. Validated through a user-friendly web-based interface for seamless clinical integration, AutoRadAI highlights the potential of AI-driven solutions in precision oncology. This framework improves diagnostic accuracy and streamlines preoperative staging, offering transformative applications in PCa management and beyond.</p>","PeriodicalId":36528,"journal":{"name":"Biology Methods and Protocols","volume":"10 1","pages":"bpaf032"},"PeriodicalIF":1.3000,"publicationDate":"2025-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12119131/pdf/","citationCount":"0","resultStr":"{\"title\":\"AutoRadAI: a versatile artificial intelligence framework validated for detecting extracapsular extension in prostate cancer.\",\"authors\":\"Pegah Khosravi, Shady Saikali, Abolfazl Alipour, Saber Mohammadi, Maxwell Boger, Dalanda M Diallo, Christopher J Smith, Marcio C Moschovas, Iman Hajirasouliha, Andrew J Hung, Srirama S Venkataraman, Vipul Patel\",\"doi\":\"10.1093/biomethods/bpaf032\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Preoperative identification of extracapsular extension (ECE) in prostate cancer (PCa) is crucial for effective treatment planning, as ECE presence significantly increases the risk of positive surgical margins and early biochemical recurrence following radical prostatectomy. AutoRadAI, an innovative artificial intelligence (AI) framework, was developed to address this clinical challenge while demonstrating broader potential for diverse medical imaging applications. The framework integrates T2-weighted MRI data with histopathology annotations, leveraging a dual convolutional neural network (multi-CNN) architecture. AutoRadAI comprises two key components: ProSliceFinder, which isolates prostate-relevant MRI slices, and ExCapNet, which evaluates ECE likelihood at the patient level. The system was trained and validated on a dataset of 1001 patients (510 ECE-positive, 491 ECE-negative cases). ProSliceFinder achieved an area under the ROC curve (AUC) of 0.92 (95% confidence interval [CI]: 0.89-0.94) for slice classification, while ExCapNet demonstrated robust performance with an AUC of 0.88 (95% CI: 0.83-0.92) for patient-level ECE detection. Additionally, AutoRadAI's modular design ensures scalability and adaptability for applications beyond ECE detection. Validated through a user-friendly web-based interface for seamless clinical integration, AutoRadAI highlights the potential of AI-driven solutions in precision oncology. This framework improves diagnostic accuracy and streamlines preoperative staging, offering transformative applications in PCa management and beyond.</p>\",\"PeriodicalId\":36528,\"journal\":{\"name\":\"Biology Methods and Protocols\",\"volume\":\"10 1\",\"pages\":\"bpaf032\"},\"PeriodicalIF\":1.3000,\"publicationDate\":\"2025-04-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12119131/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biology Methods and Protocols\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1093/biomethods/bpaf032\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q3\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biology Methods and Protocols","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/biomethods/bpaf032","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/1 0:00:00","PubModel":"eCollection","JCR":"Q3","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
AutoRadAI: a versatile artificial intelligence framework validated for detecting extracapsular extension in prostate cancer.
Preoperative identification of extracapsular extension (ECE) in prostate cancer (PCa) is crucial for effective treatment planning, as ECE presence significantly increases the risk of positive surgical margins and early biochemical recurrence following radical prostatectomy. AutoRadAI, an innovative artificial intelligence (AI) framework, was developed to address this clinical challenge while demonstrating broader potential for diverse medical imaging applications. The framework integrates T2-weighted MRI data with histopathology annotations, leveraging a dual convolutional neural network (multi-CNN) architecture. AutoRadAI comprises two key components: ProSliceFinder, which isolates prostate-relevant MRI slices, and ExCapNet, which evaluates ECE likelihood at the patient level. The system was trained and validated on a dataset of 1001 patients (510 ECE-positive, 491 ECE-negative cases). ProSliceFinder achieved an area under the ROC curve (AUC) of 0.92 (95% confidence interval [CI]: 0.89-0.94) for slice classification, while ExCapNet demonstrated robust performance with an AUC of 0.88 (95% CI: 0.83-0.92) for patient-level ECE detection. Additionally, AutoRadAI's modular design ensures scalability and adaptability for applications beyond ECE detection. Validated through a user-friendly web-based interface for seamless clinical integration, AutoRadAI highlights the potential of AI-driven solutions in precision oncology. This framework improves diagnostic accuracy and streamlines preoperative staging, offering transformative applications in PCa management and beyond.