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 86 publications

(70 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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Nomogram for predicting pathologic complete response to neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma

Liu,

Chen,

Zhang

et al. 2024

Cancer Medicine

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PurposeA pathologic complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) is seen in up to 40% of the patients with esophageal squamous cell carcinoma (ESCC). No nomogram has been constructed for the prediction of pCR for patients whose primary chemotherapy was a taxane‐based regimen. The aim is to identify characteristics associated with a pCR through analyzing multiple pre‐ and post‐nCRT variables and to develop a nomogram for the prediction of pCR for these patients by integrating clinicopathological characteristics and hematological biomarkers.Materials and MethodsWe analyzed 293 patients with ESCC who underwent nCRT followed by esophagectomy. Clinicopathological factors, hematological parameters before nCRT, and hematotoxicity during nCRT were collected. Univariate and multivariate logistic regression analyses were performed to identify predictive factors for pCR. A nomogram model was built and evaluated for both discrimination and calibration.ResultsAfter surgery, 37.88% of the study patients achieved pCR. Six variables were included in the nomogram: sex, cN stage, chemotherapy regimen, duration of nCRT, pre‐nCRT neutrophil‐to‐lymphocyte ratio (NLR), and pre‐nCRT platelet‐to‐lymphocyte ratio (PLR). The nomogram indicated good accuracy and consistency in predicting pCR, with a C‐index of 0.743 (95% confidence interval: 0.686, 0.800) and a p value of 0.600 (>0.05) in the Hosmer–Lemeshow goodness‐of‐fit test.ConclusionsFemale, earlier cN stage, duration of nCRT (< 62 days), chemotherapy regimen of taxane plus platinum, pre‐nCRT NLR (≥2.199), and pre‐nCRT PLR (≥99.302) were significantly associated with a higher pCR in ESCC patients whose primary chemotherapy was a taxane‐based regimen for nCRT. A nomogram was developed and internally validated, showing good accuracy and consistency.

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

Cited by 86 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

Nomogram for predicting pathologic complete response to neoadjuvant chemoradiotherapy in patients with esophageal squamous cell carcinoma

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