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Objective <p>Clinical staging on CT has several biases, and a radiogenomics approach could be proposed. The study aimed to test the performance of a radiogenomics approach in identifying high-risk colon cancer.</p> Material and methods <p>ATTRACT is a multicentric trial, registered in ClinicalTrials.gov (NCT06108310). Three hundred non-metastatic colon cancer patients were retrospectively enrolled and divided into two groups, high-risk and no-risk, according to the pathological staging. Radiological evaluations were performed by two abdominal radiologists. For 151 patients, we achieved genomics. The baseline CT scans were used to evaluate the radiological assessment and to perform 3D cancer segmentation. One expert radiologist used open-source software to perform the volumetric cancer segmentations on baseline CT scans in the portal phase (3DSlicer v4.10.2). Implementing the classical LASSO with a machine-learning library method was used to select the optimal features to build Model 1 (clinical-radiological plus radiomic feature, 300 patients) and Model 2 (Model 1 plus genomics, 151 patients). The performance of clinical-radiological interpretation was assessed regarding the area under the curve (AUC), sensitivity, specificity, and accuracy. The average performance of Models 1 and 2 was also calculated.</p> Results <p>In total, 262/300 were classified as high-risk and 38/300 as no-risk. Clinical-radiological interpretation by the two radiologists achieved an AUC of 0.58–0.82 (95% CI: 0.52–0.63 and 0.76–0.85, <i>p</i> &lt; 0.001, respectively), sensitivity: 67.9–93.8%, specificity: 47.4–68.4%, and accuracy: 65.3–90.7%, respectively. Model 1 yielded AUC: 0.74 (95% CI: 0.61–0.88, <i>p</i> &lt; 0.005), sensitivity: 86%, specificity: 48%, and accuracy: 81%. Model2 reached AUC: 0.84, (95% CI: 0.68–0.99, <i>p</i> &lt; 0.005), sensitivity: 88%, specificity: 63%, and accuracy: 84%.</p> Conclusion <p>The radiogenomics model outperformed radiological interpretation in identifying high-risk colon cancer.</p> Key Points <p><Emphasis Type="BoldItalic">Question</Emphasis> <i>Can this radiogenomic model identify high-risk stages II and III colon cancer in a preoperative clinical setting</i>?</p> <p><Emphasis Type="BoldItalic">Findings</Emphasis> <i>This radiogenomics model outperformed both the radiomics and radiological interpretations, reducing the risk of improper staging and incorrect treatment options</i>.</p> <p><Emphasis Type="BoldItalic">Clinical relevance</Emphasis> <i>The radiogenomics model was demonstrated to be superior to radiological interpretation and radiomics in identifying high-risk colon cancer, and could therefore be promising in stratifying high-risk and low-risk patients</i>.</p> Graphical Abstract <p></p>

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CT-based radiogenomic analysis to predict high-risk colon cancer (ATTRACT): a multicentric trial

  • 期刊论文

  • 发表日期：

    2025年6月5日

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  • Damiano Caruso,
  • Michela Polici,
  • Marta Zerunian,
  • Andrea Monterubbiano,
  • Mariarita Tarallo,
  • Emanuela Pilozzi,
  • Laura Belloni,
  • Giorgia Scafetta,
  • Daniela Valanzuolo,
  • Dominga Pugliese,
  • Domenico De Santis,
  • Andrea Vecchione,
  • Paolo Mercantini,
  • Elsa Iannicelli,
  • Enrico Fiori,
  • Andrea Laghi

摘要

Objective

Clinical staging on CT has several biases, and a radiogenomics approach could be proposed. The study aimed to test the performance of a radiogenomics approach in identifying high-risk colon cancer.

Material and methods

ATTRACT is a multicentric trial, registered in ClinicalTrials.gov (NCT06108310). Three hundred non-metastatic colon cancer patients were retrospectively enrolled and divided into two groups, high-risk and no-risk, according to the pathological staging. Radiological evaluations were performed by two abdominal radiologists. For 151 patients, we achieved genomics. The baseline CT scans were used to evaluate the radiological assessment and to perform 3D cancer segmentation. One expert radiologist used open-source software to perform the volumetric cancer segmentations on baseline CT scans in the portal phase (3DSlicer v4.10.2). Implementing the classical LASSO with a machine-learning library method was used to select the optimal features to build Model 1 (clinical-radiological plus radiomic feature, 300 patients) and Model 2 (Model 1 plus genomics, 151 patients). The performance of clinical-radiological interpretation was assessed regarding the area under the curve (AUC), sensitivity, specificity, and accuracy. The average performance of Models 1 and 2 was also calculated.

Results

In total, 262/300 were classified as high-risk and 38/300 as no-risk. Clinical-radiological interpretation by the two radiologists achieved an AUC of 0.58–0.82 (95% CI: 0.52–0.63 and 0.76–0.85, p < 0.001, respectively), sensitivity: 67.9–93.8%, specificity: 47.4–68.4%, and accuracy: 65.3–90.7%, respectively. Model 1 yielded AUC: 0.74 (95% CI: 0.61–0.88, p < 0.005), sensitivity: 86%, specificity: 48%, and accuracy: 81%. Model2 reached AUC: 0.84, (95% CI: 0.68–0.99, p < 0.005), sensitivity: 88%, specificity: 63%, and accuracy: 84%.

Conclusion

The radiogenomics model outperformed radiological interpretation in identifying high-risk colon cancer.

Key Points

Question Can this radiogenomic model identify high-risk stages II and III colon cancer in a preoperative clinical setting?

Findings This radiogenomics model outperformed both the radiomics and radiological interpretations, reducing the risk of improper staging and incorrect treatment options.

Clinical relevance The radiogenomics model was demonstrated to be superior to radiological interpretation and radiomics in identifying high-risk colon cancer, and could therefore be promising in stratifying high-risk and low-risk patients.

Graphical Abstract