Relationship between tumor mutational burden and maximum standardized uptake value in 2-[18F]FDG PET (positron emission tomography) scan in cancer patients

Jahromi, Amin Haghighat; Barkauskas, Donald A.; Zabel, Matthew; Goodman, Aaron M.; Frampton, Garrett M.; Nikanjam, Mina; Hoh, Carl K.; Kurzrock, Razelle

doi:10.21203/rs.3.rs-68828/v2

Cited by 1 publication

(2 citation statements)

References 23 publications

(30 reference statements)

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“…Furthermore, tumor volume and TLG were significant factors to predict high TMB. Although there have been previous studies to suggest a correlation between maximum SUV or other texture parameters and TMB [20,21], to the best of our knowledge, no previous study has demonstrated a relationship between volumetric parameters and TMB. This result is almost the same as a previous result suggesting that more genetic mutation corresponds with large tumor volume [31].…”

Section: Discussionmentioning

confidence: 71%

“…A previous study investigated the correlation between PET-based radiomics and TMB in lung cancer [20]. Another pan-cancer analysis, including gastrointestinal cancer, showed an association between the standardized uptake value (SUV) on FDG PET/CT and TMB [21]. However, no prior study has attempted to predict TMB status using PET-based radiomics in CRC.…”

Section: Introductionmentioning

confidence: 99%

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A Machine Learning Approach Using FDG PET-Based Radiomics for Prediction of Tumor Mutational Burden and Prognosis in Stage IV Colorectal Cancer

Lee

Moon

Hong

et al. 2023

Cancers

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Introduction: We assessed the performance of F-18 fluorodeoxyglucose positron emission tomography (FDG PET)-based radiomics for the prediction of tumor mutational burden (TMB) and prognosis using a machine learning (ML) approach in patients with stage IV colorectal cancer (CRC). Methods: Ninety-one CRC patients who underwent pretreatment FDG PET/computed tomography (CT) and palliative chemotherapy were retrospectively included. PET-based radiomics were extracted from the primary tumor on PET imaging using the software LIFEx. For feature selection, PET-based radiomics associated with TMB were selected by logistic regression analysis. The performances of seven ML algorithms to predict high TMB were compared by the area under the receiver’s operating characteristic curves (AUCs) and validated by five-fold cross-validation. A PET radiomic score was calculated by averaging the z-score of each radiomic feature. The prognostic power of the PET radiomic score was assessed using Cox proportional hazards regression analysis. Results: Ten significant radiomic features associated with TMB were selected: surface-to-volume ratio, total lesion glycolysis, tumor volume, area, compacity, complexity, entropy, correlation, coarseness, and zone size non-uniformity. The k-nearest neighbors model obtained the good performance for prediction of high TMB (AUC: 0.791, accuracy: 0.814, sensitivity: 0.619, specificity: 0.871). On multivariable Cox regression analysis, the PET radiomic score (Hazard ratio = 4.498, 95% confidential interval = 1.024–19.759; p = 0.046) was a significant independent prognostic factor for OS. Conclusions: This study demonstrates that PET-based radiomics are useful image biomarkers for the prediction of TMB status in stage IV CRC. PET radiomic score, which integrates significant radiomic features, has the potential to predict survival in stage IV CRC patients.

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Section: Discussionmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%