Deep learning (DL) 3D dose prediction has recently gained a lot of attention. However, the variability of plan quality in the training dataset, generated manually by planners with wide range of expertise, can dramatically effect the quality of the final predictions. Moreover, any changes in the clinical criteria requires a new set of manually generated plans by planners to build a new prediction model. In this work, we instead use consistent plans generated by our in-house automated planning system (named ``ECHO'') to train the DL model. ECHO (expedited constrained hierarchical optimization) generates consistent/unbiased plans by solving large-scale constrained optimization problems sequentially. If the clinical criteria changes, a new training data set can be easily generated offline using ECHO, with no or limited human intervention, making the DL-based prediction model easily adaptable to the changes in the clinical practice. We used 120 conventional lung patients (100 for training, 20 for testing) with different beam configurations and trained our DL-model using manually-generated as well as automated ECHO plans. We evaluated different inputs: (1) CT+(PTV/OAR)contours, and (2) CT+contours+beam configurations, and different loss functions: (1) MAE (mean absolute error), and (2) MAE+DVH (dose volume histograms). The quality of the predictions was compared using different DVH metrics as well as dose-score and DVH-score, recently introduced by the AAPM knowledge-based planning grand challenge. The best results were obtained using automated ECHO plans and CT+contours+beam as training inputs and MAE+DVH as loss function.
Reproducible Benchmarking for Lung Nodule Detection and Malignancy Classification Across Multiple Low-Dose CT Datasets
Duke University School of Medicine（杜克大学医学院）；Pratt School of Engineering, Duke University（杜克大学普拉特工程学院）；University of Colorado（科罗拉多大学）；Duke University（杜克大学）
Three-Dimensional Dose Prediction for Lung IMRT Patients with Deep Neural Networks: Robust Learning from Heterogeneous Beam Configurations
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）；Université catholique de Louvain（鲁汶天主教大学）；Cliniques universitaires Saint-Luc（圣吕克大学附属医院）；KU Leuven（鲁汶大学）
Can Unsupervised Methods Outperform Supervised Deep Learning When Ground Truth Is Sparse? A Case Study of Bronchovascular Bundle Segmentation in Low-Dose CT
Lung cancer screening with low-dose CT scans using a deep learning approach
Arkansas State University（阿肯色州立大学）；University of Michigan（密歇根大学）；Ann Arbor Algorithm（安阿伯算法公司）；University of Arkansas at Pine Bluff（阿肯色大学松崖分校）；University of Arkansas for Medical Sciences（阿肯色大学医学院）
A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans
Draper（德雷珀实验室）；Google（谷歌）
我们介绍了一种新的计算机辅助检测和诊断系统，肺癌筛查与低剂量CT扫描，产生有意义的概率评估。我们的系统完全基于3D卷积神经网络，并在公开的LUNA16和Kaggle Data Science Bowl挑战赛上实现了肺结节检测和恶性肿瘤分类任务的最新性能。虽然结节检测系统通常是自行设计和优化的，但我们发现，考虑检测和诊断组件之间的耦合是很重要的。利用这种耦合使我们能够开发一种端到端系统，该系统具有更高和更强大的性能，并消除了对结节检测假阳性减少阶段的需要。此外，我们在我们的深度学习系统中描述了模型的不确定性，这是肺部CT分析的第一个，并表明我们可以使用它来为结节检测和患者恶性肿瘤诊断提供校准良好的分类概率。这些由模型不确定性提供信息的校准概率可用于后续基于风险的诊断干预或疾病治疗决策，正如我们使用基于概率的患者转诊策略进一步改善我们的结果所证明的那样。
Direct Prediction of Cardiovascular Mortality from Low-dose Chest CT using Deep Learning
Image Sciences Institute, University Medical Center Utrecht（乌得勒支大学医学中心影像科学研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）；Utrecht University（乌得勒支大学）
Automatic calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions
Image Sciences Institute, University Medical Center Utrecht, Utrecht University（乌得勒支大学医学中心影像科学研究所，乌得勒支大学）；Diagnostic Image Analysis Group at the Department of Radiology and Nuclear Medicine, Radboud University Medical Center（拉德堡德大学医学中心放射学与核医学系诊断影像分析组）；Fraunhofer MEVIS（弗劳恩霍夫医学图像计算研究所）；Department of Radiology, University Medical Center Utrecht, Utrecht University（乌得勒支大学医学中心放射科，乌得勒支大学）
Novel Radiomic Feature for Survival Prediction of Lung Cancer Patients using Low-Dose CBCT Images
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；Tata Medical Center（塔塔医学中心）；Arden Cancer Centre（雅顿癌症中心）；University Hospitals Coventry and Warwickshire（考文垂与沃里克郡大学医院）
Explainable Cross-Disease Reasoning for Cardiovascular Risk Assessment from Low-Dose Computed Tomography
Department of Computer Science, Emory University（埃默里大学计算机科学系）；Department of Computer Science, Johns Hopkins University（约翰霍普金斯大学计算机科学系）；Department of Radiation Oncology（放射肿瘤学部）；Winship Cancer Institute, Emory University（埃默里大学Winship癌症研究所）
Parse and Recall: Towards Accurate Lung Nodule Malignancy Prediction like Radiologists
Hupan Lab（湖畔实验室）；DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；The First Affiliated Hospital of College of Medicine, Zhejiang University（浙江大学医学院附属第一医院）；Guangdong Provincial People’s Hospital（广东省人民医院）
LungEvaty: A Scalable, Open-Source Transformer-based Deep Learning Model for Lung Cancer Risk Prediction in LDCT Screening
Technical University of Munich (TUM)（慕尼黑技术大学）；TUM University Hospital（慕尼黑技术大学医院）；Imperial College London（伦敦帝国学院）；Munich Center for Machine Learning (MCML)（慕尼黑机器学习中心）
Reliability of PET/CT shape and heterogeneity features in functional and morphological components of Non-Small Cell Lung Cancer tumors: a repeatability analysis in a prospective multi-center cohort
INSERM, UMR 1101, LaTIM, University of Brest, IBSAM（法国国家健康与医学研究院，UMR 1101，LaTIM，布雷斯特大学，IBSAM）；Academic department of nuclear Medicine, CHU Milétrie（米莱特里大学医院核医学科）；Memorial Sloan Kettering Cancer Center（纪念斯隆·凯特琳癌症中心）；Mallinckrodt Institute of Radiology and the Siteman Cancer Center, Washington University School of Medicine（华盛顿大学医学院马林克罗特放射学研究所及Siteman癌症中心）
Automatic Lung Cancer Prediction from Chest X-ray Images Using Deep Learning Approach
Kamnoetvidya Science Academy（甘烹碧科学学院）；National Electronics and Computer Technology Center（泰国国家电子和计算机技术中心）；Vidyasirimedhi Institute of Science and Technology（瓦莱拉克科学技术研究院）
Enhancing Cancer Prediction in Challenging Screen-Detected Incident Lung Nodules Using Time-Series Deep Learning
University College London（伦敦大学学院）；MANAS AI（MANAS人工智能公司）；University College London Hospitals NHS Foundation Trust（伦敦大学学院医院国民保健服务基金会信托）；Royal Brompton and Harefield NHS Foundation Trust（皇家布朗普顿与哈菲尔德国民保健服务基金会信托）；Imperial College（帝国理工学院）
Adaptive Fusion of Radiomics and Deep Features for Lung Adenocarcinoma Subtype Recognition
Renmin University of China（中国人民大学）；Capital Medical University（首都医科大学）；Beijing Chao-Yang Hospital（北京朝阳医院）；Beijing Institute of Respiratory Medicine（北京呼吸疾病研究所）
High-risk Factor Prediction in Lung Cancer Using Thin CT Scans: An Attention-Enhanced Graph Convolutional Network Approach
Renmin University of China（中国人民大学）；Capital Medical University（首都医科大学）；Beijing Chao-Yang Hospital（北京朝阳医院）；Beijing Institute of Respiratory Medicine（北京呼吸疾病研究所）
Post-Radiotherapy PET Image Outcome Prediction by Deep Learning under Biological Model Guidance: A Feasibility Study of Oropharyngeal Cancer Application
University of California Los Angeles（加利福尼亚大学洛杉矶分校）；Duke University Medical Center（杜克大学医学中心）
Vision-Language Model-Based Semantic-Guided Imaging Biomarker for Lung Nodule Malignancy Prediction
organization= Medical \& Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA , city= Los Angeles , postcode= 90095 , state= CA , country= USA；organization= Department of Medicine, Division of Pulmonology；Critical Care, David Geffen School of Medicine at UCLA , city= Los Angeles , postcode= 90095 , state= CA , country= USA
