Prognostic significance of PET/CT-derived metabolic heterogeneity indices in wild-type metastatic colorectal cancer

Purpose: Tumor heterogeneity is one of the important factors affecting treatment response in malignant diseases, and this heterogeneity can be estimated by positron emission tomography-computed tomography (PET/CT) metabolic parameters. This study aimed to evaluate the efficacy of intratumoral metabolic heterogeneity indices in predicting survival in patients with wild-type metastatic colorectal cancer (mCRC). Materials and Methods: This retrospective analysis included 79 patients with wild-type mCRC who were followed between 2018 and 2023 and who underwent PET/CT at the time of diagnosis for staging. The relationship between heterogeneity indices and survival and mortality was assessed. Results: The median heterogeneity index-1 (HI-1) value was higher in the non-survivor group compared to the survivor group, but did not reach a statistically significant level (3.32 vs. 2.91). The median heterogeneity index-2 (HI-2) value was also higher in the non-survivor group compared to the survivor group, and there was a statistically significant difference between the groups (10.36 vs. 7.65). The median overall survival (OS) of the patients was 42.2 (95% CI 28.9-55.5) months, and the median progression-free survival (PFS) was 19.6±3.9 months. Median OS was longer in patients with lower HI-2 (61.8±15.3 months vs. 33.9±5.2 months). Conclusion: Our findings support the potential of PET/CT-based calculated heterogeneity indices as a noninvasive prognostic marker in wild-type mCRC patients. In wild-type mCRC patients, HI-2 can be used to predict patients who will benefit less from treatment.

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Journal
Çukurova medical journal (Online)/Çukurova medical journal
Published
2026-02-27
DOI
https://doi.org/10.17826/cumj.1839084
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
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article

Prognostic significance of PET/CT-derived metabolic heterogeneity indices in wild-type metastatic colorectal cancer

Aygül Polat Kelle, Ali OĞUL, Beytullah Aktaş
Çukurova medical journal (Online)/Çukurova medical journal
Radiomics and Machine Learning in Medical Imaging
article

Prognostic significance of PET/CT-derived metabolic heterogeneity indices in wild-type metastatic colorectal cancer

Aygül Polat Kelle, Ali OĞUL, Beytullah Aktaş
article en

Abstract

Purpose: Tumor heterogeneity is one of the important factors affecting treatment response in malignant diseases, and this heterogeneity can be estimated by positron emission tomography-computed tomography (PET/CT) metabolic parameters. This study aimed to evaluate the efficacy of intratumoral metabolic heterogeneity indices in predicting survival in patients with wild-type metastatic colorectal cancer (mCRC). Materials and Methods: This retrospective analysis included 79 patients with wild-type mCRC who were followed between 2018 and 2023 and who underwent PET/CT at the time of diagnosis for staging. The relationship between heterogeneity indices and survival and mortality was assessed. Results: The median heterogeneity index-1 (HI-1) value was higher in the non-survivor group compared to the survivor group, but did not reach a statistically significant level (3.32 vs. 2.91). The median heterogeneity index-2 (HI-2) value was also higher in the non-survivor group compared to the survivor group, and there was a statistically significant difference between the groups (10.36 vs. 7.65). The median overall survival (OS) of the patients was 42.2 (95% CI 28.9-55.5) months, and the median progression-free survival (PFS) was 19.6±3.9 months. Median OS was longer in patients with lower HI-2 (61.8±15.3 months vs. 33.9±5.2 months). Conclusion: Our findings support the potential of PET/CT-based calculated heterogeneity indices as a noninvasive prognostic marker in wild-type mCRC patients. In wild-type mCRC patients, HI-2 can be used to predict patients who will benefit less from treatment.

Çukurova medical journal (Online)/Çukurova medical journalVol. 51(1)
State Hospital (GB), Massachusetts Eye Research and Surgery Institute (US), Mersin Üniversitesi (TR)
Good health and well-being
Openalex Percentile: Top 41%
Radiomics and Machine Learning in Medical Imaging
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