Computational imaging approaches to personalized gastric cancer treatment

个性化胃癌治疗的计算成像方法

• 批准号: 10585301
• 负责人: Ruijiang Li
• 金额: $ 57.77万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-01 至 2028-02-29
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10585301
• 关键词: Address; Adjuvant Chemotherapy; Adopted; Aftercare; Age; Algorithms; Architecture; Biological; Blood; Cancer Etiology; Chemotherapy-Oncologic Procedure; Clinical; Clinical Data; Clinical Markers; Clinical Trials; Curative Surgery; Data; Development; Disease; Disease Surveillance; Distant; Evaluation; Failure; Gender; Goals; Habitats; Heterogeneity; Histologic; Histology; Image; Imaging technology; Immune; Immunotherapy; Incidence; Individual; Integration Host Factors; Knowledge; Localized Disease; Location; Machine Learning; Malignant Neoplasms; Minority Groups; Modality; Modeling; Monitor; Morphology; Neoadjuvant Therapy; Operative Surgical Procedures; PET/CT scan; Pathologic; Patient-Focused Outcomes; Patients; Performance; Population; Prediction of Response to Therapy; Process; Prognosis; Protocols documentation; Public Health; Recurrence; Reproducibility; Research; Risk; Risk Factors; Selection for Treatments; Serum Markers; Solid Neoplasm; Staging; Statistical Models; Systemic disease; Testing; Toxic effect; Training; Uncertainty; Underserved Population; Validation; Work; X-Ray Computed Tomography; advanced disease; burden of illness; cancer therapy; chemotherapy; clinical translation; clinically relevant; clinically significant; cohort; community setting; deep learning; deep learning model; design; effective therapy; follow-up; imaging approach; imaging biomarker; improved; improved outcome; individual patient; ineffective therapies; innovation; knowledge base; learning strategy; longitudinal analysis; malignant stomach neoplasm; minority communities; mortality; multimodal data; multitask; new therapeutic target; novel; performance tests; personalized medicine; predicting response; predictive modeling; prognostic; prognostic model; prognostic value; prospective; prospective test; radiological imaging; radiomics; response; risk prediction; risk stratification; serial imaging; side effect; standard care; success; survival outcome; survival prediction; therapy outcome; translational model; treatment response; tumor; tumor microenvironment

ABSTRACT Gastric cancer is a major global disease burden and leading cause of cancer mortality worldwide. Current treatment decision is made primarily on the basis of staging, which divides patients into several prognostic groups. For patients with localized and locally advanced disease, curative-intent surgery with chemotherapy is the standard treatment. However, survival outcomes vary widely, even among patients with disease of the same stage. Certain patients with early-stage disease have a sufficiently low risk of recurrence and may not benefit from, or could even be harmed by, chemotherapy given the associated toxicity and side effects. Conversely, many patients with aggressive tumors do not respond well to standard chemotherapy and still recur despite receiving extensive but ineffective treatment. Therefore, current one-size-fits-all approach is suboptimal, leading to over- and under-treatment in many patients. There is an unmet need for reliable prognostic and predictive models to guide personalized treatment of gastric cancer. To address this unmet need, we propose robust radiomics features of tumor morphology and spatial heterogeneity and establish their prognostic value. In addition, we will incorporate pathobiological knowledge into the design of deep learning models for predicting prognosis. Further, we will develop novel deep learning architecture to analyze longitudinal images for predicting pathologic response to neoadjuvant therapy. Finally, by leveraging the complementary value of imaging data, clinicopathologic variables and serial serum markers, we will construct integrative models to further improve prediction. If successful, the proposed models will be useful in two ways: (1), identify which patients with early gastric cancer may safely forego chemotherapy and avoid toxicity; (2), select the most effective chemotherapy regimen for a given patient. Further, the models can also identify patients with advanced disease who do not respond to standard chemotherapy and may benefit from novel targeted therapy or immunotherapy. The proposed computational imaging approaches are generally applicable for response monitoring and disease surveillance in many solid tumor types. Finally, the AI-based imaging technology developed here can bring benefit to underserved populations in minority groups and community settings. Progress made in gastric cancer will not only improve outcomes for patients in the US but also have global impact given its high incidence and mortality worldwide.

抽象的 胃癌是全球主要的全球疾病负担，是全球癌症死亡率的主要原因。 当前的治疗决策主要是基于分期制定的，该决定将患者分为 几个预后组。适用于局部疾病和局部晚期疾病的患者 化疗手术是标准治疗方法。但是，生存结果差异很大，甚至 在同一阶段的疾病患者中。某些早期疾病患者患有 复发风险足够低，可能不会受益，甚至可能受到损害 化学疗法鉴于相关的毒性和副作用。相反，许多患者 侵袭性肿瘤对标准化疗的反应不佳，尽管接受了 广泛但无效的治疗。因此，当前的一定大小的方法是次优的 导致许多患者的过度治疗。对可靠的预后有未满足的需求 以及指导性格治疗胃癌的预测模型。为了满足这种未满足的需求， 我们提出了肿瘤形态和空间异质性的强大放射素特征，并建立 他们的预后价值。此外，我们将将病理学知识纳入 预测预后的深度学习模型。此外，我们将发展新颖的深度学习 结构分析纵向图像，以预测对新辅助治疗的病理反应。 最后，通过利用成像数据的互补价值，临床病理变量和串行 血清标记，我们将构建综合模型以进一步改善预测。如果成功， 建议的模型将通过两种方式有用：（1）确定哪些早期胃癌患者可能 安全放弃化疗并避免毒性； （2），选择最有效的化疗方案 对于给定的患者。此外，模型还可以识别患有晚期疾病的患者 对标准化疗的反应，并可能受益于新颖的靶向疗法或免疫疗法。 提出的计算成像方法通常适用于响应监控 以及许多实体瘤类型的疾病监测。最后，基于AI的成像技术 这里开发的可以为少数群体和社区中服务不足的人口带来好处 设置。胃癌取得的进展不仅会改善美国患者的预后，还会改善 鉴于其全球的高发病率和死亡率很高，也会产生全球影响。