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 胃癌是全球主要疾病负担，也是全球癌症死亡的主要原因。 目前的治疗决策主要根据分期做出，将患者分为 几个预后组。对于患有局部和局部晚期疾病的患者，以治疗为目的 手术联合化疗是标准治疗方法。然而，生存结果差异很大，甚至 处于同一阶段疾病的患者之间。某些患有早期疾病的患者有 复发风险足够低，可能不会受益，甚至可能受到伤害， 化疗具有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反应不佳，尽管接受了化疗，但仍会复发 广泛但无效的治疗。因此，当前的一刀切方法并不是最理想的， 导致许多患者治疗过度或治疗不足。对可靠预后的需求尚未得到满足 和预测模型来指导胃癌的个性化治疗。为了解决这一未满足的需求， 我们提出了肿瘤形态和空间异质性的稳健放射组学特征，并建立了 他们的预后价值。此外，我们会将病理生物学知识融入到设计中 用于预测预后的深度学习模型。此外，我们将开发新颖的深度学习 分析纵向图像以预测新辅助治疗的病理反应的架构。 最后，通过利用影像数据、临床病理变量和序列的互补价值 血清标志物，我们将构建综合模型以进一步改进预测。如果成功的话， 所提出的模型将在两个方面发挥作用：（1）确定哪些早期胃癌患者可能 安全地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，这些模型还可以识别患有晚期疾病的患者，这些患者不 对标准化疗有反应，并可能受益于新型靶向治疗或免疫治疗。 所提出的计算成像方法通常适用于反应监测 以及许多实体瘤类型的疾病监测。最后，基于AI的成像技术 这里的开发可以为少数群体和社区中服务不足的人群带来好处 设置。胃癌方面取得的进展不仅会改善美国患者的预后， 鉴于其在世界范围内的高发病率和死亡率，也具有全球影响。