Radiomics Model to Predict Early Progression of Nonmetastatic                     Nasopharyngeal Carcinoma after Intensity Modulation Radiation Therapy: A                     Multicenter Study

Du, Ruxu; Lee, Victor H; Yuan, Hui; Lam, Ka-On; Pang, Herbert; Chen, Yú; Lam, Edmund Y.; Khong, Pek‐Lan; Lee, Anne W; Kwong, Dora Lai-Wan; Vardhanabhuti, Varut

doi:10.1148/ryai.2019180075

Cited by 41 publications

(50 citation statements)

References 31 publications

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“…Right-censored data are quite common in cancer survival analysis, which means that follow-up ends before subjects experience a specific event, such as disease progression. As mentioned above, there are several studies that applied ML techniques for cancer prognosis prediction [ 16 , 17 , 18 , 19 , 20 ]. However, many ML approaches have an assumption that all patient outcomes are known (disease progression or no disease progression).…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, we also compared the performances of the three models using the log-rank test by stratifying the testing set into high- and low-risk groups. Many similar studies in the past only adopted conventional metrics, such as accuracy and AUROC [ 16 , 17 , 18 , 19 , 20 ], which are not suitable for censored data. Moreover, to the best of our knowledge, this study is the first one to compare CPH, CSF, and DeepSurv using three different evaluation metrics in NPC.…”

Section: Discussionmentioning

confidence: 99%

“…Recently, ML has been applied for prognosis prediction in some types of cancer [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Notably, series of study applying ML for survival prediction of NPC have reported good performance [ 16 , 17 , 18 , 19 , 20 ]. Du et al [ 18 ] built a ML model to predict 3-year disease progression based on 525 radiomics features extracted from magnetic resonance imaging (MRI) scan images and five clinical features.…”

Section: Introductionmentioning

confidence: 99%

“…Notably, series of study applying ML for survival prediction of NPC have reported good performance [ 16 , 17 , 18 , 19 , 20 ]. Du et al [ 18 ] built a ML model to predict 3-year disease progression based on 525 radiomics features extracted from magnetic resonance imaging (MRI) scan images and five clinical features. The study was conducted on 277 patients with non-metastatic NPC.…”

Section: Introductionmentioning

confidence: 99%

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Progression-Free Survival Prediction in Patients with Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy: Machine Learning vs. Traditional Statistics

Oei

Lyu

et al. 2021

JPM

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Background: The Cox proportional hazards (CPH) model is the most commonly used statistical method for nasopharyngeal carcinoma (NPC) prognostication. Recently, machine learning (ML) models are increasingly adopted for this purpose. However, only a few studies have compared the performances between CPH and ML models. This study aimed at comparing CPH with two state-of-the-art ML algorithms, namely, conditional survival forest (CSF) and DeepSurv for disease progression prediction in NPC. Methods: From January 2010 to March 2013, 412 eligible NPC patients were reviewed. The entire dataset was split into training cohort and testing cohort in a ratio of 90%:10%. Ten features from patient-related, disease-related, and treatment-related data were used to train the models for progression-free survival (PFS) prediction. The model performance was compared using the concordance index (c-index), Brier score, and log-rank test based on the risk stratification results. Results: DeepSurv (c-index = 0.68, Brier score = 0.13, log-rank test p = 0.02) achieved the best performance compared to CSF (c-index = 0.63, Brier score = 0.14, log-rank test p = 0.38) and CPH (c-index = 0.57, Brier score = 0.15, log-rank test p = 0.81). Conclusions: Both CSF and DeepSurv outperformed CPH in our relatively small dataset. ML-based survival prediction may guide physicians in choosing the most suitable treatment strategy for NPC patients.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Progression-Free Survival Prediction in Patients with Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy: Machine Learning vs. Traditional Statistics

Oei

Lyu

et al. 2021

JPM

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“…One key challenge of applying deep networks in clinical decision making is that deep networks are black box models with multilayer nonlinear operations, thus the reasoning behind the results from deep networks are very difficult to interpret clinically. Explainable AI is an emerging field of active research in trying to address this challenge [ 90 , 91 ].…”

Section: Machine Learning and Radiomics Workflow For Oncology Imagingmentioning

confidence: 99%

Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature

Xie

Pang

Chan

et al. 2021

Cancers

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Esophageal cancer (EC) is of public health significance as one of the leading causes of cancer death worldwide. Accurate staging, treatment planning and prognostication in EC patients are of vital importance. Recent advances in machine learning (ML) techniques demonstrate their potential to provide novel quantitative imaging markers in medical imaging. Radiomics approaches that could quantify medical images into high-dimensional data have been shown to improve the imaging-based classification system in characterizing the heterogeneity of primary tumors and lymph nodes in EC patients. In this review, we aim to provide a comprehensive summary of the evidence of the most recent developments in ML application in imaging pertinent to EC patient care. According to the published results, ML models evaluating treatment response and lymph node metastasis achieve reliable predictions, ranging from acceptable to outstanding in their validation groups. Patients stratified by ML models in different risk groups have a significant or borderline significant difference in survival outcomes. Prospective large multi-center studies are suggested to improve the generalizability of ML techniques with standardized imaging protocols and harmonization between different centers.

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Deep learning in MRI‐guided radiation therapy: A systematic review

Eidex,

Ding,

Wang

et al. 2023

J Applied Clin Med Phys

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Recent advances in MRI‐guided radiation therapy (MRgRT) and deep learning techniques encourage fully adaptive radiation therapy (ART), real‐time MRI monitoring, and the MRI‐only treatment planning workflow. Given the rapid growth and emergence of new state‐of‐the‐art methods in these fields, we systematically review 197 studies written on or before December 31, 2022, and categorize the studies into the areas of image segmentation, image synthesis, radiomics, and real time MRI. Building from the underlying deep learning methods, we discuss their clinical importance and current challenges in facilitating small tumor segmentation, accurate x‐ray attenuation information from MRI, tumor characterization and prognosis, and tumor motion tracking. In particular, we highlight the recent trends in deep learning such as the emergence of multi‐modal, visual transformer, and diffusion models.

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Radiomics Model to Predict Early Progression of Nonmetastatic Nasopharyngeal Carcinoma after Intensity Modulation Radiation Therapy: A Multicenter Study

Cited by 41 publications

References 31 publications

Progression-Free Survival Prediction in Patients with Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy: Machine Learning vs. Traditional Statistics

Progression-Free Survival Prediction in Patients with Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy: Machine Learning vs. Traditional Statistics

Machine Learning and Radiomics Applications in Esophageal Cancers Using Non-Invasive Imaging Methods—A Critical Review of Literature

Deep learning in MRI‐guided radiation therapy: A systematic review

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