Delta radiomics model for the prediction of progression-free survival time in advanced non-small-cell lung cancer patients after immunotherapy

Xie, Dong; Xu, Fangyi; Zhu, Wenchao; Pu, Cailing; ShaoYu, Huang; Lou, Kaihua; Wu, Yan; Huang, Dingpin; He, Cong; Hu, Hongjie

doi:10.3389/fonc.2022.990608

Cited by 16 publications

(21 citation statements)

References 51 publications

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“…Delta-radiomics predictive performances were significantly better than clinical and baseline radiomics-only models both for treatment response and survival predictions. This finding is in line with the recent literature on immunotherapy response prediction, in which models incorporating feature variations over time have systematically outperformed models based on pre-treatment CT alone [ 19 , 21 , 29 ]. These results are comprehensible if the delta-radiomics models, as in our case, include volume-related features assessing early tumor shrinkage, which is the hallmark of tumor sensitivity to treatment.…”

Section: Discussionsupporting

confidence: 87%

“…This could be explained by the heterogeneity of our population, gathering patients presenting different tumor stages and receiving PD-1/PD-L1 inhibitors as first-line or further lines of treatment. Regarding delta-radiomics, to date, only one study from—Xie et al—has tried to incorporate clinical parameters into the delta-radiomics model in the setting of NSCLC treated with ICI [ 29 ]. Their combined signature showed good performances in predicting PFS and the combined models provided better clinical utility than delta-radiomics models within a reasonable threshold probability range.…”

Section: Discussionmentioning

confidence: 99%

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Radiomics and Delta-Radiomics Signatures to Predict Response and Survival in Patients with Non-Small-Cell Lung Cancer Treated with Immune Checkpoint Inhibitors

Cousin

Louis²,

Dheur³

et al. 2023

Cancers

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The aim of our study was to determine the potential role of CT-based radiomics in predicting treatment response and survival in patients with advanced NSCLC treated with immune checkpoint inhibitors. We retrospectively included 188 patients with NSCLC treated with PD-1/PD-L1 inhibitors from two independent centers. Radiomics analysis was performed on pre-treatment contrast-enhanced CT. A delta-radiomics analysis was also conducted on a subset of 160 patients who underwent a follow-up contrast-enhanced CT after 2 to 4 treatment cycles. Linear and random forest (RF) models were tested to predict response at 6 months and overall survival. Models based on clinical parameters only and combined clinical and radiomics models were also tested and compared to the radiomics and delta-radiomics models. The RF delta-radiomics model showed the best performance for response prediction with an AUC of 0.8 (95% CI: 0.65−0.95) on the external test dataset. The Cox regression delta-radiomics model was the most accurate at predicting survival with a concordance index of 0.68 (95% CI: 0.56−0.80) (p = 0.02). The baseline CT radiomics signatures did not show any significant results for treatment response prediction or survival. In conclusion, our results demonstrated the ability of a CT-based delta-radiomics signature to identify early on patients with NSCLC who were more likely to benefit from immunotherapy.

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Section: Discussionsupporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Radiomics and Delta-Radiomics Signatures to Predict Response and Survival in Patients with Non-Small-Cell Lung Cancer Treated with Immune Checkpoint Inhibitors

Cousin

Louis²,

Dheur³

et al. 2023

Cancers

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“…The ICC test was conducted between the datasets obtained by the two radiologists. The value exceeding 0.75 was deemed indicative of robust reproducibility and reliability, leading to the exclusion of features with ICC<0.75 from subsequent analysis ( 37 , 38 ). Furthermore, Pearson’s rank correlation coefficient was employed to evaluate the correlation between feature pairs, with one feature randomly excluded from each pair exhibiting a correlation coefficient > 0.9.…”

Section: Methodsmentioning

confidence: 99%

MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study

Tan,

Sui,

Wang

et al. 2024

Front. Oncol.

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BackgroundAccurate preoperative prediction of glioma is crucial for developing individualized treatment decisions and assessing prognosis. In this study, we aimed to establish and evaluate the value of integrated models by incorporating the intratumoral and peritumoral features from conventional MRI and clinical characteristics in the prediction of glioma grade.MethodsA total of 213 glioma patients from two centers were included in the retrospective analysis, among which, 132 patients were classified as the training cohort and internal validation set, and the remaining 81 patients were zoned as the independent external testing cohort. A total of 7728 features were extracted from MRI sequences and various volumes of interest (VOIs). After feature selection, 30 radiomic models depended on five sets of machine learning classifiers, different MRI sequences, and four different combinations of predictive feature sources, including features from the intratumoral region only, features from the peritumoral edema region only, features from the fusion area including intratumoral and peritumoral edema region (VOI-fusion), and features from the intratumoral region with the addition of features from peritumoral edema region (feature-fusion), were established to select the optimal model. A nomogram based on the clinical parameter and optimal radiomic model was constructed for predicting glioma grade in clinical practice.ResultsThe intratumoral radiomic models based on contrast-enhanced T1-weighted and T2-flair sequences outperformed those based on a single MRI sequence. Moreover, the internal validation and independent external test underscored that the XGBoost machine learning classifier, incorporating features extracted from VOI-fusion, showed superior predictive efficiency in differentiating between low-grade gliomas (LGG) and high-grade gliomas (HGG), with an AUC of 0.805 in the external test. The radiomic models of VOI-fusion yielded higher prediction efficiency than those of feature-fusion. Additionally, the developed nomogram presented an optimal predictive efficacy with an AUC of 0.825 in the testing cohort.ConclusionThis study systematically investigated the effect of intratumoral and peritumoral radiomics to predict glioma grading with conventional MRI. The optimal model was the XGBoost classifier coupled radiomic model based on VOI-fusion. The radiomic models that depended on VOI-fusion outperformed those that depended on feature-fusion, suggesting that peritumoral features should be rationally utilized in radiomic studies.

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“…Finally, some studies are focusing on the possibility of applying radiomics to daily clinical practice in order to improve not only the detection of lung lesions but also the ability to distinguish malignant from benign lesions, characterize their histology, stage, and genotype, as well as the response to treatments and the possibility of applying the models to lung cancer screening in order to overcome the limitation of over-diagnosis of indeterminate nodules [ 129 , 130 , 131 , 132 , 133 , 134 ].…”

Section: Lung Cancermentioning

confidence: 99%

Gender Medicine in Clinical Radiology Practice

Giacobbe

Granata

Trovato

et al. 2023

JPM

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Gender Medicine is rapidly emerging as a branch of medicine that studies how many diseases common to men and women differ in terms of prevention, clinical manifestations, diagnostic-therapeutic approach, prognosis, and psychological and social impact. Nowadays, the presentation and identification of many pathological conditions pose unique diagnostic challenges. However, women have always been paradoxically underestimated in epidemiological studies, drug trials, as well as clinical trials, so many clinical conditions affecting the female population are often underestimated and/or delayed and may result in inadequate clinical management. Knowing and valuing these differences in healthcare, thus taking into account individual variability, will make it possible to ensure that each individual receives the best care through the personalization of therapies, the guarantee of diagnostic-therapeutic pathways declined according to gender, as well as through the promotion of gender-specific prevention initiatives. This article aims to assess potential gender differences in clinical-radiological practice extracted from the literature and their impact on health and healthcare. Indeed, in this context, radiomics and radiogenomics are rapidly emerging as new frontiers of imaging in precision medicine. The development of clinical practice support tools supported by artificial intelligence allows through quantitative analysis to characterize tissues noninvasively with the ultimate goal of extracting directly from images indications of disease aggressiveness, prognosis, and therapeutic response. The integration of quantitative data with gene expression and patient clinical data, with the help of structured reporting as well, will in the near future give rise to decision support models for clinical practice that will hopefully improve diagnostic accuracy and prognostic power as well as ensure a more advanced level of precision medicine.

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Delta radiomics model for the prediction of progression-free survival time in advanced non-small-cell lung cancer patients after immunotherapy

Cited by 16 publications

References 51 publications

Radiomics and Delta-Radiomics Signatures to Predict Response and Survival in Patients with Non-Small-Cell Lung Cancer Treated with Immune Checkpoint Inhibitors

Radiomics and Delta-Radiomics Signatures to Predict Response and Survival in Patients with Non-Small-Cell Lung Cancer Treated with Immune Checkpoint Inhibitors

MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study

Gender Medicine in Clinical Radiology Practice

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