CT texture analysis as a predictor of favorable response to anti-PD1 monoclonal antibodies in metastatic skin melanoma

Bonnin, A; Durot, Carole; Barat, Maxime; Djelouah, Manel; Grange, Florent; Mulé, Sebastien; Soyer, Philippe; Hoeffel, Christine

doi:10.1016/j.diii.2021.09.009

Cited by 13 publications

(8 citation statements)

References 44 publications

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“…A recent study in patients with advanced NSCLC who were treated with ICI presented an approach that evaluates dynamic changes in specific tumor radiophenotypic attributes between baseline and post-treatment CT scans [40]. In addition, a few studies have extracted imaging biomarkers to assess the correlation between pretreatment CT texture parameters and survival prediction in patients with metastatic melanoma who received anti-PD-1 monoclonal antibodies [41][42][43][44]. Study findings by Trebeschi et al on a small melanoma cohort proved an association between tumor textural radiomic patterns and responses to immunotherapy [45].…”

Section: Discussionmentioning

confidence: 99%

Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma

Tabari

Cox

D'Amore

et al. 2023

Cancers

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Pretreatment LDH is a standard prognostic biomarker for advanced melanoma and is associated with response to ICI. We assessed the role of machine learning-based radiomics in predicting responses to ICI and in complementing LDH for prognostication of metastatic melanoma. From 2008–2022, 79 patients with 168 metastatic hepatic lesions were identified. All patients had arterial phase CT images 1-month prior to initiation of ICI. Response to ICI was assessed on follow-up CT at 3 months using RECIST criteria. A machine learning algorithm was developed using radiomics. Maximum relevance minimum redundancy (mRMR) was used to select features. ROC analysis and logistic regression analyses evaluated performance. Shapley additive explanations were used to identify the variables that are the most important in predicting a response. mRMR selection revealed 15 features that are associated with a response to ICI. The machine learning model combining both radiomics features and pretreatment LDH resulted in better performance for response prediction compared to models that included radiomics or LDH alone (AUC of 0.89 (95% CI: [0.76–0.99]) vs. 0.81 (95% CI: [0.65–0.94]) and 0.81 (95% CI: [0.72–0.91]), respectively). Using SHAP analysis, LDH and two GLSZM were the most predictive of the outcome. Pre-treatment CT radiomic features performed equally well to serum LDH in predicting treatment response.

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Section: Discussionmentioning

confidence: 99%

Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma

Tabari

Cox

D'Amore

et al. 2023

Cancers

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“…However, the quantitative information that may be extracted from associated CT data has not been exploited so far in the field of FL. Yet, CT TA is now considered as a new technique that allows a quantitative assessment of tumor heterogeneity, and many studies have recently shown its ability to predict PFS and OS in several types of cancer, as well as response to treatment [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 35 ].…”

Section: Discussionmentioning

confidence: 99%

“…CT texture analysis (TA) is a relatively recently emerging technique for quantifying tumor heterogeneity based on an analysis of the distribution and relationship of pixel gray levels in the image [ 21 , 22 ]. CT TA can provide information regarding survival and response to treatment for many solid cancer types, such as colorectal [ 23 ], melanoma [ 24 , 25 ], esophageal [ 26 ], head and neck [ 27 ], non-small-cell lung [ 28 , 29 ], cerebral [ 30 ], or hepatocellular carcinoma [ 31 , 32 ]. However, data regarding hematological malignancies are scarce.…”

Section: Introductionmentioning

confidence: 99%

Pretreatment CT Texture Parameters as Predictive Biomarkers of Progression-Free Survival in Follicular Lymphoma Treated with Immunochemotherapy and Rituximab Maintenance

Durot¹,

Durot²,

Mulé

et al. 2023

Diagnostics

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The purpose of this study was to determine whether texture analysis features present on pretreatment unenhanced computed tomography (CT) images, derived from 18F-fluorodeoxyglucose positron emission/computed tomography (18-FDG PET/CT), can predict progression-free survival (PFS), progression-free survival at 24 months (PFS 24), time to next treatment (TTNT), and overall survival in patients with high-tumor-burden follicular lymphoma treated with immunochemotherapy and rituximab maintenance. Seventy-two patients with follicular lymphoma were retrospectively included. Texture analysis was performed on unenhanced CT images extracted from 18-FDG PET/CT examinations that were obtained within one month before treatment. Skewness at a fine texture scale (SSF = 2) was an independent predictor of PFS (hazard ratio = 3.72 (95% CI: 1.15, 12.11), p = 0.028), PFS 24 (hazard ratio = 13.38; 95% CI: 1.29, 138.13; p = 0.029), and TTNT (hazard ratio = 5.11; 95% CI: 1.18, 22.13; p = 0.029). Skewness values above -0.015 at SSF = 2 were significantly associated with lower PFS, PFS 24, and TTNT. Kurtosis without filtration was an independent predictor of PFS (SSF = 0; HR = 1.22 (95% CI: 1.04, 1.44), p = 0.013), and TTNT (SSF = 0; hazard ratio = 1.23; 95% CI: 1.04, 1.46; p = 0.013). This study shows that pretreatment unenhanced CT texture analysis-derived tumor skewness and kurtosis may be used as predictive biomarkers of PFS and TTNT in patients with high-tumor-burden follicular lymphoma treated with immunochemotherapy and rituximab maintenance.

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“…First order features included mean values, standard deviation, entropy, mean of positive pixels, skewness, and kurtosis. For each parameter, the segmented regions of interest were filtered using a Laplacian of the Gaussian transformation, and the features were measured at six different spatial scale filters (0, 2, 3, 4, 5, 6) [ 16 ].…”

Section: Methodsmentioning

confidence: 99%

Radiomic analysis of abdominal organs during sepsis of digestive origin in a French intensive care unit

Boutin,

Morisson,

Riché

et al. 2023

ACC

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Background: Sepsis is a severe and common cause of admission to the intensive care unit (ICU). Radiomic analysis (RA) may predict organ failure and patient outcomes. The objective of this study was to assess a model of RA and to evaluate its performance in predicting in-ICU mortality and acute kidney injury (AKI) during abdominal sepsis. Methods: This single-center, retrospective study included patients admitted to the ICU for abdominal sepsis. To predict in-ICU mortality or AKI, elastic net regularized logistic regression and the random forest algorithm were used in a five-fold cross-validation set repeated 10 times.Results: Fifty-five patients were included. In-ICU mortality was 25.5%, and 76.4% of patients developed AKI. To predict in-ICU mortality, elastic net and random forest models, respectively, achieved areas under the curve (AUCs) of 0.48 (95% confidence interval [CI], 0.43–0.54) and 0.51 (95% CI, 0.46–0.57) and were not improved combined with Simplified Acute Physiology Score (SAPS) II. To predict AKI with RA, the AUC was 0.71 (95% CI, 0.66–0.77) for elastic net and 0.69 (95% CI, 0.64–0.74) for random forest, and these were improved combined with SAPS II, respectively; AUC of 0.94 (95% CI, 0.91–0.96) and 0.75 (95% CI, 0.70–0.80) for elastic net and random forest, respectively.Conclusions: This study suggests that RA has poor predictive performance for in-ICU mortality but good predictive performance for AKI in patients with abdominal sepsis. A secondary validation cohort is needed to confirm these results and the assessed model.

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CT texture analysis as a predictor of favorable response to anti-PD1 monoclonal antibodies in metastatic skin melanoma

Cited by 13 publications

References 44 publications

Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma

Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma

Pretreatment CT Texture Parameters as Predictive Biomarkers of Progression-Free Survival in Follicular Lymphoma Treated with Immunochemotherapy and Rituximab Maintenance

Radiomic analysis of abdominal organs during sepsis of digestive origin in a French intensive care unit

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