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article · The Egyptian Journal of Radiology and Nuclear Medicine

Role of diffusion-weighted images with adc mapping in assessment of response of colorectal liver metastases to systemic therapy

2026Open accessCairo University

Abstract

Abstract Background The study aimed to evaluate the role of diffusion-weighted imaging (DWI) magnetic resonance imaging (MRI) in assessment of response of colorectal hepatic metastases to systemic therapy, improving diagnostic accuracy and patient management. Methods Seventy hepatic focal lesions were evaluated in forty patients using MRI with DWI, before and 3 months after treatment. Between June 2023 and June 2024, the study was conducted at the National Cancer Institute, Cairo University. Two radiologists independently measured Apparent diffusion coefficient (ADC) values and evaluated the response to therapy. In comparison to RECIST 1.1 criteria, the diagnostic accuracy of DWI was assessed and the inter observer agreement. Results All hepatic lesions that showed regressive course by RECIST criteria (7, 10% of all lesions) showed a reduced ADC value after 3-months therapy while lesions that showed progressive course by RECIST criteria (44, 62.9% of all lesions) showed different response to therapy, with 56.8% of them showed an increased ADC value after therapy. Some lesions showed mixed response to therapy in the same patient. There was a reduction in ADC value in all responding lesions (n = 7, 10% of all lesions) after 3-months therapy. A significant inter observer agreement was observed. Conclusion DWI with ADC mapping demonstrated limited diagnostic accuracy and poor agreement with RECIST criteria for predicting early treatment response in colorectal liver metastases. The low proportion of responding lesions and variable ADC patterns in progressive disease limit the clinical utility of DWI as a standalone method. Integration with other functional imaging modalities is recommended.

Research topics

  • MRI in cancer diagnosis
  • Hepatocellular Carcinoma Treatment and Prognosis
  • Radiomics and Machine Learning in Medical Imaging

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DOI: 10.1186/s43055-026-01763-7

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