Your browser doesn't support javascript.
loading
CT-based delta-radiomics nomogram to predict pathological complete response after neoadjuvant chemoradiotherapy in esophageal squamous cell carcinoma patients.
Fan, Liyuan; Yang, Zhe; Chang, Minghui; Chen, Zheng; Wen, Qiang.
Afiliação
  • Fan L; Department of Thoracic Surgery, Qilu Hospital of Shandong University, Jinan, 250012, Shandong, China.
  • Yang Z; Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Road, Jinan, 250021, Shandong, China.
  • Chang M; Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Road, Jinan, 250021, Shandong, China.
  • Chen Z; Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Road, Jinan, 250021, Shandong, China.
  • Wen Q; Department of Radiation Oncology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, 324 Jingwu Road, Jinan, 250021, Shandong, China. wq890425@126.com.
J Transl Med ; 22(1): 579, 2024 Jun 18.
Article em En | MEDLINE | ID: mdl-38890720
ABSTRACT

BACKGROUND:

This study developed a nomogram model using CT-based delta-radiomics features and clinical factors to predict pathological complete response (pCR) in esophageal squamous cell carcinoma (ESCC) patients receiving neoadjuvant chemoradiotherapy (nCRT).

METHODS:

The study retrospectively analyzed 232 ESCC patients who underwent pretreatment and post-treatment CT scans. Patients were divided into training (n = 186) and validation (n = 46) sets through fivefold cross-validation. 837 radiomics features were extracted from regions of interest (ROIs) delineations on CT images before and after nCRT to calculate delta values. The LASSO algorithm selected delta-radiomics features (DRF) based on classification performance. Logistic regression constructed a nomogram incorporating DRFs and clinical factors. Receiver operating characteristic (ROC) and area under the curve (AUC) analyses evaluated nomogram performance for predicting pCR.

RESULTS:

No significant differences existed between the training and validation datasets. The 4-feature delta-radiomics signature (DRS) demonstrated good predictive accuracy for pCR, with α-binormal-based and empirical AUCs of 0.871 and 0.869. T-stage (p = 0.001) and differentiation degree (p = 0.018) were independent predictors of pCR. The nomogram combined the DRS and clinical factors improved the classification performance in the training dataset (AUCαbin = 0.933 and AUCemp = 0.941). The validation set showed similar performance with AUCs of 0.958 and 0.962.

CONCLUSIONS:

The CT-based delta-radiomics nomogram model with clinical factors provided high predictive accuracy for pCR in ESCC patients after nCRT.
Assuntos
Palavras-chave

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Esofágicas / Tomografia Computadorizada por Raios X / Curva ROC / Terapia Neoadjuvante / Nomogramas / Quimiorradioterapia / Carcinoma de Células Escamosas do Esôfago Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Neoplasias Esofágicas / Tomografia Computadorizada por Raios X / Curva ROC / Terapia Neoadjuvante / Nomogramas / Quimiorradioterapia / Carcinoma de Células Escamosas do Esôfago Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Ano de publicação: 2024 Tipo de documento: Article