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Purpose <p>Management of colorectal cancer (CRC) is determined by the stage of the disease and molecular features, such as microsatellite instability (MSI). MSI-high/deficient mismatch repair (MSI-H/dMMR) tumors respond better to immunotherapy but poorly to 5-FU-based treatments. With increasing use of neoadjuvant chemotherapy there is interest in developing non-invasive, radiomics models based on preoperative contrast-enhanced CT scans to predict MSI status and support personalized therapy.</p> Material and Methods <p>Adult patients diagnosed with CRC who underwent pre-treatment staging with contrast-enhanced CT and had known MSI status were retrospectively analyzed. Portal venous phase images were assessed. Two radiologists, blinded to MSI status, manually segmented tumor regions on CT images. Radiomic features and statistical modeling were used to develop a predictive model for identifying the MSI-H phenotype.</p> Results <p>Analysis was conducted on 54 adult CRC patients who had undergone staging CT scans with known MSI status. Two different models were built considering different brands of CT machines. Twenty statistically significant radiomic features from the portal venous phase of CT images able to differentiate MSI from microsatellite stable (MSS) patients were selected for each model. LASSO regression was applied, selecting features for model construction. The best model’s performance demonstrated an area under the ROC curve of 0.844 (95% CI = 0.73–0.96 DeLong, <i>p</i> &lt; 0,05).</p> Conclusion <p>The results demonstrate the potential of the radiomics model as a non-invasive, cost-effective tool for MSI evaluation, guiding CRC therapy. It aids in identifying patients who would benefit from immunotherapy or chemotherapy, supporting the therapeutic shift from postoperative to preoperative treatment.</p>

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Radiomics-based prediction of microsatellite instability in colorectal cancer: a non-invasive approach to treatment stratification

  • 期刊论文

  • 发表日期：

    2025年9月2日

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  • Davide Mallardi,
  • Ginevra Danti,
  • Antonio Galluzzo,
  • Linda Calistri,
  • Diletta Cozzi,
  • Daniele Lavacchi,
  • Daniele Rossini,
  • Lorenzo Antonuzzo,
  • Sebastiano Paolucci,
  • Simone Busoni,
  • Francesca Castiglione,
  • Luca Messerini,
  • Fabio Cianchi,
  • Vittorio Miele

摘要

Purpose

Management of colorectal cancer (CRC) is determined by the stage of the disease and molecular features, such as microsatellite instability (MSI). MSI-high/deficient mismatch repair (MSI-H/dMMR) tumors respond better to immunotherapy but poorly to 5-FU-based treatments. With increasing use of neoadjuvant chemotherapy there is interest in developing non-invasive, radiomics models based on preoperative contrast-enhanced CT scans to predict MSI status and support personalized therapy.

Material and Methods

Adult patients diagnosed with CRC who underwent pre-treatment staging with contrast-enhanced CT and had known MSI status were retrospectively analyzed. Portal venous phase images were assessed. Two radiologists, blinded to MSI status, manually segmented tumor regions on CT images. Radiomic features and statistical modeling were used to develop a predictive model for identifying the MSI-H phenotype.

Results

Analysis was conducted on 54 adult CRC patients who had undergone staging CT scans with known MSI status. Two different models were built considering different brands of CT machines. Twenty statistically significant radiomic features from the portal venous phase of CT images able to differentiate MSI from microsatellite stable (MSS) patients were selected for each model. LASSO regression was applied, selecting features for model construction. The best model’s performance demonstrated an area under the ROC curve of 0.844 (95% CI = 0.73–0.96 DeLong, p < 0,05).

Conclusion

The results demonstrate the potential of the radiomics model as a non-invasive, cost-effective tool for MSI evaluation, guiding CRC therapy. It aids in identifying patients who would benefit from immunotherapy or chemotherapy, supporting the therapeutic shift from postoperative to preoperative treatment.