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

多方位放射组学模型预测宫颈癌淋巴结转移 淋巴结放疗 项目摘要 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， 临床阴性区域，包括肺、胰腺和淋巴瘤。这些疾病地点现在雇用参与 淋巴结放疗（INRT），重点是涉及淋巴结病。然而，在头颈癌（HNC）中， 尽管我们有能力调整淋巴结的大小，但我们仍然像传统的2D放疗一样靶向相同的淋巴结区域。 使用调强放射治疗（IMRT）对特定区域的放射治疗体积和剂量。这 方法导致RT后HNC患者过度的急性和长期毒性。因此，INRT是高度 对于HNC来说是理想的。在INRT中，在大体肿瘤体积（GTV）和临床靶体积期间的一个特定挑战 (CTV)描绘是恶性淋巴结病的鉴定。虽然一些淋巴结（LN） 根据标准成像模式，LN明显恶性，通常不确定LN是否为恶性。 是恶性的，需要瞄准。将良性淋巴结视为恶性淋巴结可能会导致 晚期并发症，如口干和吞咽困难。另一方面，隐匿性淋巴结病的漏诊 导致区域复发。该项目的目标是开发，优化和测试多方面的预测 该模型对LN转移分类具有高灵敏度和特异性，以最大限度地提高疗效， 将INRT对HNC的毒性降至最低。所提出的多面模型提供了一个灵活的框架， 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） 2）不同的信息来源（多模态）;以及3）用于模型构建的分类器 （多分类器）。通过设计一个多目标函数，我们将考虑敏感性和特异性 同时进行模型训练和优化。而不是盲目地将从 不同的模态和经验地选择一个优选的分类器，通过模态提取的信息- 将通过可靠分类器融合（RCF）策略最佳地组合特定分类器。我们将开发 用于训练多分类器、多目标和多模态（MCOM）模型的前瞻性登记数据库 通过前瞻性地收集将接受手术的HNC患者的临床特征和图像， UTSW伴病理证实的LN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手术的患者。的具体目标 项目包括：1）开发和验证多分类器、多目标和多模态（MCOM）LN转移 HNC患者的预测模型。2)开展随机II期临床试验，评估疗效， 使用MCOM模型比较INRT与常规放疗对HNC的效用。成功完成本 该项目将导致制定和验证一项战略，可以识别HNC中的恶性淋巴结， 高灵敏度和特异性，这将改善接受INRT的HNC患者的结局。

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.