A Multifaceted Radiomics Model to Predict Cervical Lymph Node Metastasis for Involved Nodal Radiation Therapy

预测涉及淋巴结放射治疗的颈部淋巴结转移的多方面放射组学模型

• 批准号: 10654048
• 负责人: David Sher
• 金额: $ 43.56万
• 依托单位: UT SOUTHWESTERN MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2027-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10654048
• 关键词: Acute; Address; Algorithms; Area; Benign; Cancer Patient; Cervical; Cervical lymph node group; Characteristics; Classification; Clinical; Data; Databases; Deglutition Disorders; Development; Disease; Dose; Emission-Computed Tomography; Evaluation; External Beam Radiation Therapy; Failure; Goals; Head and Neck Cancer; Head and neck structure; Image; Intensity-Modulated Radiotherapy; Lead; Lung; Lymphoma; Malignant - descriptor; Malignant Neoplasms; Metastatic Neoplasm to Lymph Nodes; Modality; Modeling; Neck; Nodal; Operative Surgical Procedures; Pancreas; Pathology; Patients; Performance; Pharyngeal structure; Phase; Phase II Clinical Trials; Positron-Emission Tomography; Probability; Quality of life; Radiation Dose Unit; Radiation therapy; Randomized; Recurrence; Registries; Risk; Risk Estimate; Sensitivity and Specificity; Site; Source; Testing; Therapeutic; Toxic effect; Training; Tumor Volume; Uncertainty; Validation; X-Ray Computed Tomography; Xerostomia; arm; chemoradiation; clinical investigation; cohort; convolutional neural network; design; draining lymph node; efficacy evaluation; flexibility; follow-up; head and neck cancer patient; high risk; imaging modality; improved; improved outcome; lymph nodes; lymphadenopathy; machine learning method; multimodality; overtreatment; phase 2 study; predictive modeling; predictive tools; prophylactic; prospective; quantitative imaging; radiomics; randomized trial; soft tissue; sound; standard of care; tool

A Multifaceted Radiomics Model to Predict Cervical Lymph Node Metastasis for Involved Nodal Radiation Therapy PROJECT SUMMARY The majority of disease sites treated with radiation therapy (RT) no longer receive elective/prophylactic RT to clinically-negative areas, including lung, pancreas, and lymphoma. These disease sites now employ involved nodal radiotherapy (INRT), focusing on involved lymphadenopathy. However, in head and neck cancer (HNC), we still target the same lymph node regions as conventional 2D radiotherapy, despite our ability to tailor the radiotherapy volume and dose to specific areas using intensity modulated radiation therapy (IMRT). This approach leads to excessive acute and long-term toxicities for HNC patients after RT. Therefore, INRT is highly desirable for HNC. In INRT, one particular challenge during gross tumor volume (GTV) and clinical target volume (CTV) delineation is the identification of malignant lymphadenopathy. While some lymph nodes (LNs) are obviously malignant based on standard imaging modalities, there is often uncertainty about whether a LN is malignant and requires targeting. Treating benign nodes as malignant may cause a significantly higher risk of late complications, such as xerostomia and dysphagia. On the other hand, missing occult lymphadenopathy will lead to regional recurrence. The goal of this project is to develop, optimize, and test a multifaceted predictive model with both high sensitivity and specificity for LN metastasis classification to maximize the efficacy and minimize the toxicity of INRT for HNC. The proposed multifaced model presents a flexible framework and considers multiple aspects of a predictive model, including: 1) Evaluation criteria used in model training (multi- objective); 2) Different sources of information (multi-modality); and 3) Classifiers used for model construction (multi-classifier). By designing a multi-objective function, we will consider sensitivity and specificity simultaneously during model training and optimization. Instead of blindly combining features extracted from different modalities and empirically choosing one preferred classifier, the information extracted by modality- specific classifiers will be combined optimally through a reliable classifier fusion (RCF) strategy. We will develop a prospective registry database to train the multi-classifier, multi-objective and multi-modality (MCOM) model through prospectively collecting clinical characteristics and images of HNC patients who will undergo surgery at UTSW with pathology-confirmed LN metastasis status. The model will be validated on an independent UTSW patient cohort and patients who underwent outside imaging but operated at UTSW. The specific aims of the project are: 1) Develop and validate a multi-classifier, multi-objective and multi-modality (MCOM) LN metastasis prediction model for HNC patients. 2) Conduct a randomized phase II clinical trial to evaluate the efficacy and utility of INRT versus conventional radiotherapy for HNC using the MCOM model. Successful completion of this project will result in the development and validation of a strategy that can identify malignant LNs in HNC with high sensitivity and specificity, which will lead to improved outcomes for HNC patients who receive INRT.

预测受累颈部淋巴结转移的多方面放射组学模型 淋巴结放射治疗 项目概要 大多数接受放射治疗 (RT) 治疗的疾病部位不再接受选择性/预防性放射治疗 临床阴性区域，包括肺、胰腺和淋巴瘤。这些疾病现场现在雇用了相关人员 淋巴结放射治疗（INRT），重点关注受累淋巴结肿大。然而，在头颈癌 (HNC) 中， 尽管我们有能力定制治疗方案，但我们仍然针对与传统 2D 放射治疗相同的淋巴结区域 使用调强放射治疗 (IMRT) 对特定区域进行放射治疗的体积和剂量。这 这种方法会导致 HNC 患者放疗后出现过度的急性和长期毒性。因此，INRT 高度 HNC 的理想选择。在 INRT 中，大体肿瘤体积 (GTV) 和临床目标体积期间的一项特殊挑战 (CTV) 勾画是恶性淋巴结肿大的识别。虽然某些淋巴结 (LN) 根据标准成像方式，淋巴结显然是恶性的，因此常常不确定淋巴结是否是恶性的。 恶性，需要靶向治疗。将良性淋巴结视为恶性可能会导致显着更高的风险 晚期并发症，如口干和吞咽困难。另一方面，遗漏隐匿性淋巴结肿大将 导致局部复发。该项目的目标是开发、优化和测试多方面的预测 模型对淋巴结转移分类具有高敏感性和特异性，以最大限度地提高疗效和 最大限度地减少 INRT 对 HNC 的毒性。所提出的多面模型提供了一个灵活的框架和 考虑预测模型的多个方面，包括：1）模型训练中使用的评估标准（多方面） 客观的）; 2）不同的信息来源（多模态）； 3）用于模型构建的分类器 （多分类器）。通过设计多目标函数，我们将考虑敏感性和特异性 在模型训练和优化期间同时进行。而不是盲目地组合提取的特征 不同的模态并凭经验选择一个首选分类器，模态提取的信息 特定的分类器将通过可靠的分类器融合（RCF）策略进行最佳组合。我们将开发 用于训练多分类器、多目标和多模态 (MCOM) 模型的前瞻性注册数据库 通过前瞻性收集即将接受手术的 HNC 患者的临床特征和图像 UTSW 具有病理证实的淋巴结转移状态。该模型将在独立的 UTSW 上进行验证 患者队列和接受外部成像但在 UTSW 进行手术的患者。该计划的具体目标 项目包括： 1) 开发并验证多分类器、多目标和多模态 (MCOM) LN 转移 HNC 患者的预测模型。 2）进行随机II期临床试验以评估疗效和 使用 MCOM 模型比较 INRT 与传统放疗对 HNC 的效用。顺利完成本次 该项目将导致开发和验证一种策略，该策略可以通过以下方法识别 HNC 中的恶性 LN： 高敏感性和特异性，这将改善接受 INRT 的 HNC 患者的预后。