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

预测受累患者颈淋巴转移的多方面放射组学模型 结节放射治疗 项目总结 大多数接受放射治疗(RT)的疾病部位不再接受选择性/预防性RT以 临床阴性区域，包括肺、胰腺和淋巴瘤。这些疾病网站现在雇佣了相关人员 结节放疗(INRT)，重点是累及的淋巴结病。然而，在头颈癌(HNC)中， 我们仍然以与传统2D放射治疗相同的淋巴区域为靶点，尽管我们有能力定制 采用调强放射治疗(IMRT)对特定区域进行放射治疗的体积和剂量。这 方法导致HNC患者放疗后的过度急性和长期毒性反应。因此，INRT是高度的 对于HNC来说是可取的。在INRT中，大体肿瘤体积(GTV)和临床靶区体积之间的一个特殊挑战 (CTV)勾画是恶性淋巴结病的鉴别诊断。而一些淋巴结(LN)是 根据标准的成像方式，显然是恶性的，通常不确定LN是否是 恶性的，需要有针对性的。将良性结节治疗为恶性结节可能会导致显著更高的风险 晚期并发症，如口干和吞咽困难。另一方面，漏诊的隐匿性淋巴结病 导致地区性复发。该项目的目标是开发、优化和测试一个多方面的预测 同时具有高敏感性和高特异性的淋巴结转移分类模型，以最大化疗效和 将INRT对HNC的毒性降至最低。建议的多面模型提供了灵活的框架和 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(多个 目标)；2)不同的信息来源(多通道)；以及3)用于模型构建的分类器 (多分类器)。通过设计多目标函数，我们将考虑灵敏度和特异度 同时进行模型训练和优化。而不是盲目组合从 不同的模式并经验地选择一个首选的分类器，由模式提取的信息- 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。我们将发展 一个预期的登记数据库，用于训练多分类器、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，这些患者将在 经病理证实为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接受外部成像但手术的患者。《公约》的具体目标 项目包括：1)开发和验证多分类器、多目标、多模式(MCOM)的淋巴结转移 HNC患者的预测模型。2)进行随机二期临床试验，以评估疗效和 采用MCOM模型的鼻咽癌放射治疗与常规放射治疗的比较。成功完成这项工作 该项目将导致开发和验证一种策略，该策略可以识别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。

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.