Article
Medicine, General & Internal
Brendan T. Heiden, Daniel B. Eaton, Kathryn E. Engelhardt, Su-Hsin Chang, Yan Yan, Mayank R. Patel, Daniel Kreisel, Ruben G. Nava, Bryan F. Meyers, Benjamin D. Kozower, Varun Puri
Summary: This study found that surgical procedures delayed more than 12 weeks were associated with increased risk of recurrence and worse survival in patients with clinical stage I NSCLC, suggesting that timely treatment is crucial for better outcomes.
Article
Oncology
Peter J. Kneuertz, Mahmoud Abdel-Rasoul, Desmond M. D'Souza, Jing Zhao, Robert E. Merritt
Summary: This study analyzed segmentectomy for clinical stage I NSCLC and found that VATS and robotic-assisted surgery had similar outcomes with lower morbidity and shorter length of stay compared to open thoracotomy.
Article
Oncology
Hong Yang, Lin Wang, Guoliang Shao, Baiqiang Dong, Fang Wang, Yuguo Wei, Pu Li, Haiyan Chen, Wujie Chen, Yao Zheng, Yiwei He, Yankun Zhao, Xianghui Du, Xiaojiang Sun, Zhun Wang, Yuezhen Wang, Xia Zhou, Xiaojing Lai, Wei Feng, Liming Shen, Guoqing Qiu, Yongling Ji, Jianxiang Chen, Youhua Jiang, Jinshi Liu, Jian Zeng, Changchun Wang, Qiang Zhao, Xun Yang, Xiao Hu, Honglian Ma, Qixun Chen, Ming Chen, Haitao Jiang, Yujin Xu
Summary: A combined predictive model based on pre-treatment CT radiomics features and clinical factors was established to accurately assess disease progression after Stereotactic Ablative Radiotherapy (SABR) of early-stage Non-Small Cell Lung Cancer (NSCLC). The model showed good prediction efficiency in both the training and validation cohorts, providing valuable information for individualized follow-up plans and treatment strategies.
FRONTIERS IN ONCOLOGY
(2022)
Article
Medicine, General & Internal
Zhi-Hui Wang, Lili Deng
Summary: This study aims to summarize high-risk prognostic factors in early-stage non-small cell lung cancer (NSCLC) and establish a new nomogram to predict the overall survival of individuals with stage I NSCLC after resection.
INTERNATIONAL JOURNAL OF GENERAL MEDICINE
(2022)
Article
Oncology
Zhe Ji, Yang Ni, Chuang He, Bin Huo, Shifeng Liu, Yanli Ma, Yuqing Song, Miaomiao Hu, Kaixian Zhang, Zhe Wang, Xinxin Zhao, Hongmei Han, Yufeng Wang, Ruoyu Wang, Shude Chai, Xiaokun Hu, Xuequan Huang, Xin Ye, Junjie Wang
Summary: This study compares the efficacy and safety of radioactive iodine-125 seed ablation brachytherapy (RSABT) and microwave ablation therapy (MWAT) for treating stage I non-small cell lung cancer (NSCLC). Results show that RSABT has better progression-free survival and overall survival rates, as well as lower incidence of adverse events.
AMERICAN JOURNAL OF CANCER RESEARCH
(2023)
Article
Oncology
Bin Yang, Chengxing Liu, Ren Wu, Jing Zhong, Ang Li, Lu Ma, Jian Zhong, Saisai Yin, Changsheng Zhou, Yingqian Ge, Xinwei Tao, Longjiang Zhang, Guangming Lu
Summary: A DeepSurv nomogram based on radiomic features and clinicopathological factors was developed and validated, which showed good predictive performance for overall survival in patients with non-small cell lung cancer and could guide personalized adjuvant chemotherapy.
FRONTIERS IN ONCOLOGY
(2022)
Article
Oncology
Yasuhiro Tsutani, Yoshihisa Shimada, Hiroyuki Ito, Yoshihiro Miyata, Norihiko Ikeda, Haruhiko Nakayama, Morihito Okada
Summary: In clinical stage I NSCLC, patients with a solid component size of >2 cm or pure solid type on HRCT were found to be at a high risk of recurrence. Dividing patients into high-risk and low-risk groups based on these factors showed a significant difference in recurrence-free survival.
FRONTIERS IN ONCOLOGY
(2021)
Article
Oncology
Olena Tankyevych, Flora Trousset, Claire Latappy, Moran Berraho, Julien Dutilh, Jean Pierre Tasu, Corinne Lamour, Catherine Cheze Le Rest
Summary: Immunotherapy has become a standard treatment for advanced NSCLC, but only a minority of patients benefit long-term. Analyzing radiomics features from PET/CT scans can predict treatment response and survival outcomes, aiding in personalized treatment management.
Article
Surgery
Brendan T. Heiden, Daniel B. Eaton, Su-Hsin Chang, Yan Yan, Martin W. Schoen, Mayank R. Patel, Daniel Kreisel, Ruben G. Nava, Bryan F. Meyers, Benjamin D. Kozower, Varun Puri
Summary: The aim of this study was to compare quality of care and outcomes between Veteran and non-Veteran patients undergoing surgery for clinical stage I non-small cell lung cancer (NSCLC). The study found that Veterans receive high quality care through the VHA for lung cancer and have similar outcomes compared to the general population.
Article
Medicine, Research & Experimental
Jian Feng, Li-Feng Wang, Ting-Yue Han, Yue Wang, Xiu-Yu Wu, Feng Lv, Yang Liu, Bing-Hui Chen
Summary: This meta-analysis compared survival outcomes of lobectomy versus segmentectomy in clinical stage I NSCLC and found no significant difference between the two procedures. Further large-scale, prospective, randomized trials are needed to explore reasonable surgical treatments for early-stage lung cancer.
ADVANCES IN THERAPY
(2021)
Article
Oncology
Runsheng Chang, Shouliang Qi, Yong Yue, Xiaoye Zhang, Jiangdian Song, Wei Qian
Summary: The study aimed to identify predictive imaging biomarkers from pre-treatment CT images to construct a radiomic model predicting chemotherapy response in NSCLC. The model achieved an accuracy of 85.7% and an AUC of 0.941 in an independent test cohort, showcasing its potential for better patient stratification and treatment outcomes.
FRONTIERS IN ONCOLOGY
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Kunfeng Liu, Kunwei Li, Tingfan Wu, Mingzhu Liang, Yinghua Zhong, Xiangyang Yu, Xin Li, Chuanmiao Xie, Lanjun Zhang, Xueguo Liu
Summary: In predicting prognosis of clinical stage I solid lung adenocarcinoma, a radiomics model based on a VOI of 6 mm crossing tumor border showed better accuracy compared to other models.
EUROPEAN RADIOLOGY
(2022)
Article
Multidisciplinary Sciences
Sergey P. Primakov, Abdalla Ibrahim, Janita E. van Timmeren, Guangyao Wu, Simon A. Keek, Manon Beuque, Renee W. Y. Granzier, Elizaveta Lavrova, Madeleine Scrivener, Sebastian Sanduleanu, Esma Kayan, Iva Halilaj, Anouk Lenaers, Jianlin Wu, Rene Monshouwer, Xavier Geets, Hester A. Gietema, Lizza E. L. Hendriks, Olivier Morin, Arthur Jochems, Henry C. Woodruff, Philippe Lambin
Summary: Accurate interpretation of CT scans is crucial for assessing a patient's disease. This study presents a system that automatically segments non-small cell lung cancer on CT images, demonstrating faster and more reproducible results compared to clinicians.
NATURE COMMUNICATIONS
(2022)
Article
Critical Care Medicine
Brendan T. Heiden, Daniel B. Eaton, Su-Hsin Chang, Yan Yan, Ana A. Baumann, Martin W. Schoen, Mayank R. Patel, Daniel Kreisel, Ruben G. Nava, Bryan F. Meyers, Benjamin D. Kozower, Varun Puri
Summary: This retrospective cohort study evaluated early-stage non-small cell lung cancer (NSCLC) outcomes following surgical treatment in the Veterans Health Administration (VHA) and found that Black veterans received comparable care with equivalent if not superior outcomes compared with White veterans.
Article
Radiology, Nuclear Medicine & Medical Imaging
Yifan Zhong, Yunlang She, Jiajun Deng, Shouyu Chen, Tingting Wang, Minglei Yang, Minjie Ma, Yongxiang Song, Haoyu Qi, Yin Wang, Jingyun Shi, Chunyan Wu, Dong Xie, Chang Chen
Summary: This study developed a deep learning signature for predicting N2 metastasis and prognosis stratification in clinical stage I non-small cell lung cancer. The proposed signature achieved high predictive efficacy and was associated with genetic mutations and tumor proliferation pathways. Higher deep learning scores were predictive of poorer overall survival and recurrence-free survival.
Article
Radiology, Nuclear Medicine & Medical Imaging
Wenhua Cao, Mary Gronberg, Adenike Olanrewaju, Thomas Whitaker, Karen Hoffman, Carlos Cardenas, Adam Garden, Heath Skinner, Beth Beadle, Laurence Court
Summary: This study investigates the feasibility of using a knowledge-based planning technique to detect poor quality VMAT plans for patients with head and neck cancer. The results show that the prediction models can accurately assess plan quality, identify suboptimal plans, and assist in the clinical workflow for individual patients.
JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Dong Joo Rhee, Chidinma P. Anakwenze Akinfenwa, Bastien Rigaud, Anuja Jhingran, Carlos E. Cardenas, Lifei Zhang, Surendra Prajapati, Stephen F. Kry, Kristy K. Brock, Beth M. Beadle, William Shaw, Frederika O'Reilly, Jeannette Parkes, Hester Burger, Nazia Fakie, Chris Trauernicht, Hannah Simonds, Laurence E. Court
Summary: This study aimed to determine the most accurate similarity metric when using an independent system to verify automatically generated contours. By comparing various similarity metrics, it was found that surface DSC with a thickness of 1, 2, or 3 mm can accurately distinguish clinically acceptable and unacceptable contours.
JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS
(2022)
Article
Oncology
Kelly A. Nealon, Peter A. Balter, Raphael J. Douglas, Danna K. Fullen, Paige L. Nitsch, Adenike M. Olanrewaju, Moaaz Soliman, Laurence E. Court
Summary: The study used the failure mode and effects analysis (FMEA) approach to evaluate and mitigate the risks in deploying an automated radiation therapy contouring and treatment planning tool. Through the analysis, specific errors and high-risk failure modes were identified, leading to workflow modifications and training resource enhancements. The findings demonstrate the effectiveness of FMEA in assessing and reducing risks associated with automated planning tools.
PRACTICAL RADIATION ONCOLOGY
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Hunter Mehrens, Raphael Douglas, Mary Gronberg, Kelly Nealon, Joy Zhang, Laurence Court
Summary: The study investigated the use of statistical process control (SPC) for quality assurance of the integrated web-based autoplanning tool RPA. By comparing the results from RayStation and Eclipse treatment planning systems, differences in mean dose and control limits were determined, demonstrating the flexibility and usefulness of SPC for monitoring complex automated systems like RPA.
JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS
(2022)
Article
Radiology, Nuclear Medicine & Medical Imaging
Kai Huang, Soleil Hernandez, Chenyang Wang, Callistus Nguyen, Tina Marie Briere, Carlos Cardenas, Laurence Court, Yao Xiao
Summary: We developed an automatic field-in-field (FIF) solution for whole-brain radiotherapy (WBRT) planning, which creates a homogeneous dose distribution by minimizing hotspots and results in clinically acceptable plans. The algorithm was tested on 17 whole-brain patients and produced high-quality plans in an average of 16 minutes without user intervention. 76.5% of the auto-plans were clinically acceptable, and all plans were clinically acceptable after minor edits.
JOURNAL OF APPLIED CLINICAL MEDICAL PHYSICS
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Yao Zhao, He Wang, Cenji Yu, Laurence E. Court, Xin Wang, Qianxia Wang, Tinsu Pan, Yao Ding, Jack Phan, Jinzhong Yang
Summary: The study proposed a novel Compensation-cycleGAN (Comp-cycleGAN) method to create synthetic CT (sCT) images and compensate for missing anatomy from truncated MR images. The results showed that this method can effectively generate sCT images with complete anatomy compensation from truncated MR images.
Article
Oncology
Soleil Hernandez, Callistus Nguyen, Jeannette Parkes, Hester Burger, Dong Joo Rhee, Tucker Netherton, Raymond Mumme, Jean Gumma-De La Vega, Jack Duryea, Alexandrea Leone, Arnold C. Paulino, Carlos Cardenas, Rebecca Howell, David Fuentes, Julianne Pollard-Larkin, Laurence Court
Summary: In resource-constrained settings, a 3D-conformal CSI autoplanning tool was developed and tested for pediatric patients with medulloblastoma in LMICs, aiming to reduce the complexity of treatment and planning.
PEDIATRIC BLOOD & CANCER
(2023)
Article
Radiology, Nuclear Medicine & Medical Imaging
Bishwambhar Sengupta, Kyuhak Oh, Patricia Sponseller, Peter Zaki, Boryana Eastman, Tru-Khang T. Dinh, Carlos E. Cardenas, Laurence E. Court, Upendra Parvathaneni, Eric Ford
Summary: The purpose of this study is to develop a reliable and cost-effective IMRT device based on cobalt compensators, which can deliver treatment plans of equal quality to linac-MLC devices. The study evaluates the quality of treatment plans using this device.
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS
(2023)
Article
Multidisciplinary Sciences
Cenji Yu, Chidinma P. Anakwenze, Yao Zhao, Rachael M. Martin, Ethan B. Ludmir, Joshua S. Niedzielski, Asad Qureshi, Prajnan Das, Emma B. Holliday, Ann C. Raldow, Callistus M. Nguyen, Raymond P. Mumme, Tucker J. Netherton, Dong Joo Rhee, Skylar S. Gay, Jinzhong Yang, Laurence E. Court, Carlos E. Cardenas
Summary: The article introduces a deep learning-based tool for automated segmentation of upper abdominal organs at risk, achieving satisfactory results and providing accurate contours for tumor treatment planning.
SCIENTIFIC REPORTS
(2022)
Article
Oncology
Hamidreza Ziyaee, Carlos E. Cardenas, Nana Yeboa, Jing Li, Sherise D. Ferguson, Jason Johnson, Zijian Zhou, Jeremiah Sanders, Raymond Mumme, Laurence Court, Tina Briere, Jinzhong Yang
Summary: An automated framework for contouring brain metastases in MRI was developed to assist treatment planning for stereotactic radiosurgery (SRS). The performance of the framework was evaluated and showed high accuracy and clinical acceptability. The tool can help radiologists and radiation oncologists detect and contour tumors from MRI, as well as potentially identify lesions at early stages.
ADVANCES IN RADIATION ONCOLOGY
(2023)
Article
Oncology
Hunter Mehrens, Andrea Molineu, Nadia Hernandez, Laurence Court, Rebecca Howell, David Jaffray, Christine B. Peterson, Julianne Pollard -Larkin, Stephen F. Kry
Summary: The aim of this study was to identify the key factors and their interactions that influence performance on IMRT phantoms from IROC. The findings revealed that the complexity of treatment and various parameters significantly affected the pass rates, and complexity metrics had high predictive accuracy for irradiation failures.
RADIOTHERAPY AND ONCOLOGY
(2023)
Review
Medicine, General & Internal
Hana Baroudi, Kristy K. Brock, Wenhua Cao, Xinru Chen, Caroline Chung, Laurence E. Court, Mohammad D. El Basha, Maguy Farhat, Skylar Gay, Mary P. Gronberg, Aashish Chandra Gupta, Soleil Hernandez, Kai Huang, David A. Jaffray, Rebecca Lim, Barbara Marquez, Kelly Nealon, Tucker J. Netherton, Callistus M. Nguyen, Brandon Reber, Dong Joo Rhee, Ramon M. Salazar, Mihir D. Shanker, Carlos Sjogreen, McKell Woodland, Jinzhong Yang, Cenji Yu, Yao Zhao
Summary: This paper discusses various aspects of assessing the clinical acceptability of AI-based tools for contouring and treatment planning in radiotherapy, and explores how to establish a standard for defining the clinical acceptability of new autocontouring and planning tools.
Article
Oncology
Dong Joo Rhee, Sam Beddar, Joseph Abi Jaoude, Gabriel Sawakuchi, Rachael Martin, Luis Perles, Cenji Yu, Yulun He, Laurence E. Court, Ethan B. Ludmir, Albert C. Koong, Prajnan Das, Eugene J. Koay, Cullen Taniguichi, Joshua S. Niedzielski
Summary: The purpose of this study was to determine the dosimetric limitations of daily online adaptive pancreas stereotactic body radiation treatment. By collecting planning and daily computed tomography scans from 18 patients, it was found that most patients require daily adaptation of the radiation planning process to maximize the delivered dose to the pancreatic tumor without exceeding organ at-risk constraints.
ADVANCES IN RADIATION ONCOLOGY
(2023)
Meeting Abstract
Oncology
P. Zaki, B. Sengupta, K. Oh, C. Cardenas, L. E. Court, E. C. Ford
INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS
(2022)
Article
Oncology
Gwendolyn J. McGinnis, Matthew S. Ning, Beth M. Beadle, Nanette Joubert, William Shaw, Christoph Trauernich, Hannah Simonds, Surbhi Grover, Carlos E. Cardenas, Laurence E. Court, Grace L. Smith
Summary: This study surveyed radiation therapy providers in low- and middle-income countries to assess their attitudes towards the deployment and adoption of the Radiation Planning Assistant (RPA). The majority of respondents expressed interest in RPA, while also identifying potential barriers and facilitators.
JCO GLOBAL ONCOLOGY
(2022)