Using clinical and radiomic feature–based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma receiving neoadjuvant chemoradiation

Wang, J.; Zhu, Xiaodong; Zeng, Jing; Liu, Cheng; Shen, Wei; Sun, Xiaojiang; Lin, Qipu; Fang, Jun; Chen, Qixun; Ji, Yanjun

doi:10.1007/s00330-023-09884-7

Cited by 3 publications

(3 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the clinical application of radiomics to guide cancer treatment strategies is still needed further research and exploration. Prior to the present study, Wang J. et al (8) demonstrated that machine learning models based on pretreatment CT image radiomic features combined with clinical model could accurately predict response to therapy of esophageal squamous cell carcinoma patients after nCRT with an AUC of 0.891 (95% CI: 0.823-0.950) in the testing set. Frood R. et al (9) found that pre-treatment FDG PET-CT-based models could predict the survival outcomes of ESCC patients with a training c-index of 0.7 and an external testing c-index of 0.7.…”

Section: Introductionmentioning

confidence: 85%

“…And the authors incorporated radscores and hematological biomarkers into pCR predicting model with AUCs of 0.857 in the testing set. More recently, Wang J. et al ( 8 ) built two machine learning models for predicting primary tumor CR and total pCR of ESCC patients who underwent nCRT with an AUC of 0.891 and 0.814 in the testing set. However, most of these predicting models were trained based on retrospective ESCC cohorts treated with nCRT alone, Limited radiomics model could be obtained for predicting pCR of ESCC treated with NCRT and PD-1 inhibitors.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A machine learning approach using 18F-FDG PET and enhanced CT scan-based radiomics combined with clinical model to predict pathological complete response in ESCC patients after neoadjuvant chemoradiotherapy and anti-PD-1 inhibitors

Qi,

Li,

Xiao

et al. 2024

Front. Immunol.

View full text Add to dashboard Cite

BackgroundWe aim to evaluate the value of an integrated multimodal radiomics with machine learning model to predict the pathological complete response (pCR) of primary tumor in a prospective cohort of esophageal squamous cell carcinoma (ESCC) treated with neoadjuvant chemoradiotherapy (nCRT) and anti-PD-1 inhibitors.Materials and methodsClinical information of 126 ESCC patients were included for analysis. Radiomics features were extracted from 18F-FDG PET and enhanced plan CT images. Four machine learning algorithms, including SVM (Support Vector Machine), Random Forest (RF), and eXtreme Gradient Boosting (XGB) and logistic regression (LR), were applied using k-fold cross-validation to predict pCR after nCRT. The predictive ability of the models was assessed using receiver operating characteristics (ROC) curve analysis.ResultsA total of 842 features were extracted. Among the four machine learning algorithms, SVM achieved the most promising performance on the test set for PET(AUC:0.775), CT (AUC:0.710) and clinical model (AUC:0.722). For all combinations of various modalities-based models, the combination model of 18 F-FDG PET, CT and clinical features with SVM machine learning had the highest AUC of 0.852 in the test set when compared to single-modality models in various algorithms. The other combined models had AUC ranged 0.716 to 0.775.ConclusionMachine learning models utilizing radiomics features from 18F-FDG PET and enhanced plan CT exhibit promising performance in predicting pCR in ESCC after nCRT and anti-PD-1 inhibitors. The fusion of features from multiple modalities radiomics and clinical features enhances the better predictive performance compared to using a single modality alone.

show abstract

Section: Introductionmentioning

confidence: 85%

Section: Discussionmentioning

confidence: 99%

A machine learning approach using 18F-FDG PET and enhanced CT scan-based radiomics combined with clinical model to predict pathological complete response in ESCC patients after neoadjuvant chemoradiotherapy and anti-PD-1 inhibitors

Qi,

Li,

Xiao

et al. 2024

Front. Immunol.

View full text Add to dashboard Cite

show abstract

“…Another study integrated pretreatment CT and PET images of 68 patients to build a radiomics model with an AUC of 0.87 in internal cross-validation [ 27 ]. Wang et al [ 28 ] used contrast-enhanced CT images of 112 ESCC patients to develop a radiomics model that achieved an AUC of 0.817 in the testing set. These studies relied solely on traditional medical imaging, and enhancing accuracy has proven to be challenging.…”

Section: Discussionmentioning

confidence: 99%

Integrating MR radiomics and dynamic hematological factors predicts pathological response to neoadjuvant chemoradiotherapy in esophageal cancer

Liu,

Ma,

Bao

et al. 2024

Heliyon

View full text Add to dashboard Cite

Dynamic radiological features predict pathological response after neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma

Ruan,

Ma,

et al. 2024

J Transl Med

View full text Add to dashboard Cite

Background Neoadjuvant immunochemotherapy (NICT) plus esophagectomy has emerged as a promising treatment option for locally advanced esophageal squamous cell carcinoma (LA-ESCC). Pathologic complete response (pCR) is a key indicator associated with great efficacy and overall survival (OS). However, there are insufficient indicators for the reliable assessment of pCR. Methods 192 patients with LA-ESCC treated with NICT from December 2019 to October 2023 were recruited. According to pCR status, patients were categorized into pCR group (22.92%) and non-pCR group (77.08%). Radiological features of pretreatment and preoperative CT images were extracted. Logistic and COX regressions were trained to predict pathological response and prognosis, respectively. Results Four of the selected radiological features were combined to construct an ESCC preoperative imaging score (ECPI-Score). Logistic models revealed independent associations of ECPI-Score and vascular sign with pCR, with AUC of 0.918 in the training set and 0.862 in the validation set, respectively. After grouping by ECPI-Score, a higher proportion of pCR was observed among the high-ECPI group and negative vascular sign. Kaplan Meier analysis demonstrated that recurrence-free survival (RFS) with negative vascular sign was significantly better than those with positive (P = 0.038), but not for OS (P = 0.310). Conclusions This study demonstrates dynamic radiological features are independent predictors of pCR for LA-ESCC treated with NICT. It will guide clinicians to make accurate treatment plans.

show abstract

Using clinical and radiomic feature–based machine learning models to predict pathological complete response in patients with esophageal squamous cell carcinoma receiving neoadjuvant chemoradiation

Cited by 3 publications

References 25 publications

A machine learning approach using 18F-FDG PET and enhanced CT scan-based radiomics combined with clinical model to predict pathological complete response in ESCC patients after neoadjuvant chemoradiotherapy and anti-PD-1 inhibitors

A machine learning approach using 18F-FDG PET and enhanced CT scan-based radiomics combined with clinical model to predict pathological complete response in ESCC patients after neoadjuvant chemoradiotherapy and anti-PD-1 inhibitors

Integrating MR radiomics and dynamic hematological factors predicts pathological response to neoadjuvant chemoradiotherapy in esophageal cancer

Dynamic radiological features predict pathological response after neoadjuvant immunochemotherapy in esophageal squamous cell carcinoma

Contact Info

Product

Resources

About