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Research Domains
Biomedical And Clinical Sciences
Oncology And Carcinogenesis
Predictive And Prognostic Markers
Multiple Diffusion Models For Predicting Pathologic Response Of Esophageal Squamous Cell Carcinoma To Neoadjuvant Chemotherapy.

Home
Research Domains
Biomedical And Clinical Sciences
Oncology And Carcinogenesis
Predictive And Prognostic Markers
Multiple Diffusion Models For Predicting Pathologic Response Of Esophageal Squamous Cell Carcinoma To Neoadjuvant Chemotherapy.

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Multiple diffusion models for predicting pathologic response of esophageal squamous cell carcinoma to neoadjuvant chemotherapy.

Bingmei Bai¹, Long Cui², Funing Chu¹

¹Department of Radiology, The Affiliated Cancer Hospital of Zhengzhou University & Henan Cancer Hospital, No. 127 Dongming Road, Zhengzhou, 450008, Henan, China.

Abdominal Radiology (New York)

|July 2, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

The continuous-time random-walk (CTRW) and fractional order calculus (FROC) models show promise for predicting neoadjuvant chemotherapy (NACT) response in esophageal squamous cell carcinoma (ESCC) patients. These advanced MRI models can guide treatment decisions for ESCC.

Keywords:

Esophageal Neoplasms Magnetic Resonance Imaging Neoadjuvant Chemotherapy

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Area of Science:

Radiology
Oncology
Medical Imaging Analysis

Background:

Esophageal squamous cell carcinoma (ESCC) treatment response prediction is crucial.
Neoadjuvant chemotherapy (NACT) is a standard treatment for ESCC.
Accurate prediction of NACT response can optimize patient management.

Purpose of the Study:

To evaluate advanced MRI models for predicting NACT response in ESCC.
To assess the diagnostic performance of fractional order calculus (FROC), continuous-time random-walk (CTRW), diffusion kurtosis imaging (DKI), intravoxel incoherent motion (IVIM), mono-exponential (MEM), and stretched exponential models (SEM).

Main Methods:

Prospective study of 90 ESCC patients undergoing NACT and MRI.
Patients categorized into pathological complete response (pCR) and non-pCR groups.

18 predictive models (Pre-, Post-, Delta-treatment) developed using six models and cross-validation.

Model performance compared using Area Under the Curve (AUC) via DeLong method.

Main Results:

The CTRW models (Pre-, Post-, Delta-) demonstrated good predictive efficacy (AUCs 0.722-0.833).
The Post-FROC model showed excellent diagnostic performance (AUC 0.907).
Both CTRW and Post-FROC models proved effective in predicting treatment outcomes.

Conclusions:

CTRW and Post-FROC models show significant promise for predicting NACT efficacy in ESCC.
These advanced imaging models can aid in tailoring treatment strategies for ESCC patients.