RSI-AI: Predicting clinically significant prostate cancer to guide biopsy decisions by combining advanced tissue microstructure imaging with deep learning

前列腺活检对于前列腺癌的诊断至关重要，但人们往往不清楚是谁。 应该进行活组织检查，以及在腺体中哪里应该有活组织检查的目标。这会导致错过 诊断，不必要的活组织检查，过度诊断和过度治疗癌症，这不是生命 具有威胁性。这项提议的目标是开发一套量化和非侵入性的工具， RSI-AI和RSI-AI，以帮助临床医生确定谁应该为前列腺癌进行活检 以及有临床意义的病变的位置。RSI-AI使用深度学习来预测 限制波谱成像对前列腺癌病变的定位和病理分级 (RSI)数据。RSI是一种先进的扩散磁共振成像(MRI)技术 对水分子的限制扩散进行建模，以改进微组织分类和 肿瘤检测。通过在深度学习模型中利用RSI数据，RSI-AI将产生 比用常规MRI训练的模型更准确的病理分级预测 数据。RSI-AI将RSI-AI的病理分级预测与临床数据相结合 包括年龄、家族史、遗传学和前列腺体积，以准确和全面地 量化前列腺癌的当前和未来风险。这项提案的第一阶段将制定和 验证RSI-AI和RSI-AI模型，并将其性能与使用 常规MRI数据。该提案的第二阶段将部署RSI-AI和RSI-AI CorTechs云平台，展示其临床可用性和实用性，并生成 提交510K FDA所需的材料。通过这种方式生成的临床软件 该提案最终将提高诊断效率，减少不必要的活检，并 过度治疗惰性前列腺癌，同时促进早期发现和适当 临床意义重大的前列腺癌的治疗

Prostate biopsies are critical for the diagnosis of prostate cancer, but it is often unclear who should be biopsied and where in the gland the biopsy should be targeted. This results in missed diagnoses, unnecessary biopsies, and overdiagnosis and overtreatment of cancer that is not life threatening. The goal of this proposal is to develop a set of quantitative and non-invasive tools, RSI-AI and RSI-AI+, to help clinicians determine who should be biopsied for prostate cancer and the locations of clinically significant lesions. RSI-AI uses deep learning to predict the location and pathological grade of prostate cancer lesions from restriction spectrum imaging (RSI) data. RSI is an advanced diffusion magnetic resonance imaging (MRI) technique that models the restricted diffusion of water molecules to improve microtissue classification and tumor detection. By utilizing RSI data in the deep learning model, RSI-AI will produce pathological grade predictions that are more accurate than models trained with conventional MRI data. RSI-AI+ integrates the pathological grade predictions from RSI-AI with clinical data including age, family history, genetics, and prostate volume to accurately and comprehensively quantify current and future risk for prostate cancer. Phase I of this proposal will develop and validate the RSI-AI and RSI-AI+ models and compare their performance to models trained with conventional MRI data. Phase II of this proposal will deploy RSI-AI and RSI-AI+ to the Cortechs cloud platform, demonstrate their clinical usability and utility, and generate the materials required for a 510K FDA submission. The clinical software generated through this proposal will ultimately improve diagnostic yields, reduce unnecessary biopsies and overtreatment of indolent prostate cancer, while facilitating early detection and appropriate treatment of clinically significant prostate cancer