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.

一个多方面的放射素学模型，用于预测宫颈淋巴结转移 淋巴结疗法 项目摘要 接受放射治疗（RT）治疗的大多数疾病部位不再接受选择性/预防性RT 临床阴性区域，包括肺，胰腺和淋巴瘤。这些疾病部位现在涉及 淋巴结放射疗法（INRT），重点是涉及淋巴结肿大。但是，在头颈癌（HNC）中， 尽管我们能够量身定制 放射疗法的体积和剂量使用强度调制放射治疗（IMRT）到特定区域。这 方法导致RT后HNC患者过度急性和长期毒性。因此，INRT是高度的 HNC的理想。在INRT中，总肿瘤体积（GTV）和临床目标体积期间的一个特殊挑战 （CTV）描述是恶性淋巴结肿大的识别。虽然某些淋巴结（LN）是 显然，基于标准成像方式的恶性肿瘤，LN是否通常存在不确定性 恶性并需要定位。将良性淋巴结视为恶性，可能会导致明显更高的风险 晚期并发症，例如静脉和吞咽困难。另一方面，缺失神秘的淋巴结肿大将 导致区域复发。该项目的目的是开发，优化和测试多方面的预测 对LN转移分类具有高灵敏度和特异性的模型，以最大化功效和 最小化INRT对HNC的毒性。拟议的多割模型呈现一个灵活的框架， 考虑了预测模型的多个方面，包括：1）模型培训中使用的评估标准（多 客观的）; 2）不同信息来源（多模式）； 3）用于模型构建的分类器 （多分类器）。通过设计多目标功能，我们将考虑灵敏度和特异性 在模型培训和优化期间同时。而不是盲目组合从中提取的功能 不同的模态和经验选择一个首选分类器，以模态提取的信息 - 特定分类器将通过可靠的分类器融合（RCF）策略最佳组合。我们将发展 前瞻性注册表数据库，用于训练多分类剂，多目标和多模式（MCOM）模型 通过前瞻性收集HNC患者的临床特征和图像，他们将在 具有病理确认的LN转移状态的UTSW。该模型将在独立的UTSW上进行验证 患者队列和在UTSW进行了成像但经营的患者。特定目标 项目是：1）开发和验证多分类器，多目标和多模式（MCOM）LN转移 HNC患者的预测模型。 2）进行随机II期临床试验，以评估功效和 使用MCOM模型，INRT与常规放疗的实用性。成功完成 项目将导致制定和验证策略，该策略可以通过 高灵敏度和特异性，这将导致接受INRT的HNC患者的预后改善。