Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma

Hu, Yihuai; Xie, Chenyi; Yang, Hong; Ho, Joshua W. K.; Wen, Jing; Han, Lujun; Lam, Ka-On; Wong, Ian Yu Hong; Law, Simon; Chiu, K.W.; Vardhanabhuti, Varut; Fu, Jianhua

doi:10.1016/j.radonc.2020.09.014

Cited by 107 publications

(96 citation statements)

References 41 publications

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“…Three articles were conducted by Beukinga et al (14)(15)(16), therefore we only chose one of these for further analysis. Two articles were conducted by Hu et al (17,18), therefore we selected only one of them for the subsequent analysis. As a result, seven articles were chosen for quantitative meta-analysis.…”

Section: Resultsmentioning

confidence: 99%

A Meta-Analysis for Using Radiomics to Predict Complete Pathological Response in Esophageal Cancer Patients Receiving Neoadjuvant Chemoradiation

Kao

Hsu

2021

In Vivo

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Background: Preservation of organ function is important in cancer treatment. The 'watch-and-wait' strategy is an important approach in management of esophageal cancer. However, clinical imaging cannot accurately evaluate the presence or absence of residual tumor after neoadjuvant chemoradiation. As a result, using radiomics to predict complete pathological response in esophageal cancer has gained in popularity in recent years. Given that the characteristics of patients and sites vary considerably, a meta-analysis is needed to investigate the predictive power of radiomics in esophageal cancer. Patients and Methods: PRISMA guidelines were used to conduct this study. PubMed, Cochrane, and Embase were searched for literature review. The quality of the selected studies was evaluated by the radiomics quality score. I 2 score and Cochran's Q test were used to evaluate heterogeneity between studies. A funnel plot was used for evaluation of publication bias. Results: A total of seven articles were collected for this meta-analysis. The pooled area under the receiver operating characteristics curve of the seven selected articles for predicting pathological complete response in eosphageal cancer patient was quite high, achieving a pooled value of 0.813 (95% confidence intervaI=0.761-0.866). The radiomics quality score ranged from −2 to 16 (maximum score: 36 points). Three out of the seven studies used machine learning algorithms, while the others used traditional biostatistics methods. One of the seven studies used morphology class features, while four studies used first-order features, and five used second-order features. Conclusion: Using radiomics to predict complete pathological response after neoadjuvant chemoradiotherapy in esophageal cancer is feasible. In the future, prospective, multicenter studies should be carried out for predicting pathological complete response in patients with esophageal cancer.

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Section: Resultsmentioning

confidence: 99%

A Meta-Analysis for Using Radiomics to Predict Complete Pathological Response in Esophageal Cancer Patients Receiving Neoadjuvant Chemoradiation

Kao

Hsu

2021

In Vivo

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“…Inception network [ 81 ] is another popular architecture [ 13 ], which has a more complex design with deeper architecture and multiple filters of different sizes operating on the same level. ResNet [ 82 ] is another recent CNN architecture with skip connections to benefit gradient backpropagation [ 83 ]. Besides using standard network architectures, some studies have designed specific CNNs for the targeted problem.…”

Section: Machine Learning and Radiomics Workflow For Oncology Imagingmentioning

confidence: 99%

“…Most published studies ( n = 12) focused on the prediction of treatment response for patients receiving chemoradiotherapy or nCRT [ 83 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 ]. The ML algorithms achieved an AUC of 0.78–1.00.…”

Section: A Review Of Literature Using Machine Learning and Radiomics Applications In Ecmentioning

confidence: 99%

“…Hu et al [ 101 ] found that a combination of peritumoral radiomics features appeared to improve the predictive performance of intratumoral radiomics for pre-treatment prediction of pCR (AUC = 0.85, 95% CI, 0.75–0.95) for nCRT using CT radiomics features, with a cohort of 231 patients and external validation of the results. The same cohort of patients was used for the exploration of the transfer learning approach and the results showed that ResNet50-based deep learning features had the predictive ability for treatment response in esophageal SCC [ 83 ]. Cao et al [ 100 ] used the limma method, which is commonly used for genetic analysis, for the identification of significant radiomics features.…”

Section: A Review Of Literature Using Machine Learning and Radiomics Applications In Ecmentioning

confidence: 99%

“…The selected features were then fitted into the least absolute shrinkage and selection operator (LASSO) logistic regression model and achieved an AUC of 0.84 in the test set to predict response for concurrent CRT. The majority of studies [ 83 , 101 , 102 , 104 , 106 , 107 , 109 , 110 ] evaluated pCR for nCRT before surgery and used histology results as the reference standard. Other studies [ 100 , 103 , 105 , 108 ] collected patients receiving chemoradiotherapy and adopted PET/CT/MRI imaging and follow-up information for pCR evaluation because surgical resection was not performed and, therefore, pathological reference was not available.…”

Section: A Review Of Literature Using Machine Learning and Radiomics Applications In Ecmentioning

confidence: 99%

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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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A prognostic nomogram for T3N0M0 esophageal squamous cell carcinoma patients undergoing radical surgery based on computed tomography radiomics and inflammatory nutritional biomarkers

Ma,

Liu,

et al. 2024

J Applied Clin Med Phys

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BackgroundThis study explores the significance of computed tomography (CT) radiomic features, along with inflammation and nutrition biomarkers, in the prognosis of postoperative patients with T3N0M0 esophageal squamous cell carcinoma (ESCC). The study aims to construct a related nomogram.MethodsA total of 114 patients were enrolled and randomly assigned to training and validation cohorts in a 7:3 ratio. Radiomic features were extracted from their preoperative chest‐enhanced CT arterial images of the primary tumor, and inflammatory and nutritional indices, including neutrophil‐to‐lymphocyte ratio (NLR), lymphocyte‐to‐monocyte ratio (LMR), platelet‐to‐lymphocyte ratio (PLR), systemic immune‐inflammation index (SII), and prognostic nutritional index (PNI), were calculated based on laboratory data from the 3 days before surgery. Intra‐class correlations coefficient (ICC) and least absolute shrinkage and selection operator (Lasso) were applied to screen valuable radiomics features predicting overall survival (OS), and the Rad‐score was calculated. In the training cohort, univariate and multivariate Cox regression analyses identified independent prognostic factors, which were adopted to establish the nomogram.ResultsEight radiomic features were selected for Rad‐score calculation. Multivariate Cox regression revealed Rad‐score, PNI, NLR, and PLR as independent prognostic factors for ESCC patients (p < 0.05). A nomogram was constructed based on these variables. The concordance index (C‐index) for the nomogram was 0.797 (95% CI: 0.726–0.868) in the training cohort and 0.796 (95% CI: 0.702–0.890) in the validation cohort. Calibration curves indicated good calibration ability, and the receiver operating characteristic (ROC) analysis demonstrated superior discriminative ability for the nomogram in comparison to the Rad‐score alone. Decision curve analysis (DCA) confirmed the clinical utility of the nomogram.ConclusionWe developed and validated a nomogram for predicting the OS of postoperative T3N0M0 ESCC patients, integrating nutritional, inflammatory markers, and radiomic signature. The combined nomogram can serve as a robust tool for risk stratification and clinical management.

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Computed tomography-based deep-learning prediction of neoadjuvant chemoradiotherapy treatment response in esophageal squamous cell carcinoma

Cited by 107 publications

References 41 publications

A Meta-Analysis for Using Radiomics to Predict Complete Pathological Response in Esophageal Cancer Patients Receiving Neoadjuvant Chemoradiation

A Meta-Analysis for Using Radiomics to Predict Complete Pathological Response in Esophageal Cancer Patients Receiving Neoadjuvant Chemoradiation

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

A prognostic nomogram for T3N0M0 esophageal squamous cell carcinoma patients undergoing radical surgery based on computed tomography radiomics and inflammatory nutritional biomarkers

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