Radiomics in lung cancer for oncologists

Pinta, Carolina de la; Barrios-Campo, Nuria; Sevillano, D. Escribano

doi:10.18053/jctres.06.2020s4.002

Cited by 3 publications

(2 citation statements)

References 65 publications

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“…These results are in line with previous literature that has demonstrated tumor heterogeneity in CT and PET to be associated with progression and treatment failure. 11,[26][27][28][29] The most common models used in radiomic studies for time-to-event analysis are the Cox proportional hazards and RSF models. RSF models have been shown to provide comparable results to Cox models and have been used to predict distant metastases and risk stratify lung cancer patients.…”

Section: Discussionmentioning

confidence: 99%

Predicting recurrence risks in lung cancer patients using multimodal radiomics and random survival forests

et al. 2022

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.PurposeWe developed a model integrating multimodal quantitative imaging features from tumor and nontumor regions, qualitative features, and clinical data to improve the risk stratification of patients with resectable non-small cell lung cancer (NSCLC).ApproachWe retrospectively analyzed 135 patients [mean age, 69 years (43 to 87, range); 100 male patients and 35 female patients] with NSCLC who underwent upfront surgical resection between 2008 and 2012. The tumor and peritumoral regions on both preoperative CT and FDG PET-CT and the vertebral bodies L3 to L5 on FDG PET were segmented to assess the tumor and bone marrow uptake, respectively. Radiomic features were extracted and combined with clinical and CT qualitative features. A random survival forest model was developed using the top-performing features to predict the time to recurrence/progression in the training cohort (n = 101), validated in the testing cohort (n = 34) using the concordance, and compared with a stage-only model. Patients were stratified into high- and low-risks of recurrence/progression using Kaplan–Meier analysis.ResultsThe model, consisting of stage, three wavelet texture features, and three wavelet first-order features, achieved a concordance of 0.78 and 0.76 in the training and testing cohorts, respectively, significantly outperforming the baseline stage-only model results of 0.67 (p < 0.005) and 0.60 (p = 0.008), respectively. Patients at high- and low-risks of recurrence/progression were significantly stratified in both the training (p < 0.005) and the testing (p = 0.03) cohorts.ConclusionsOur radiomic model, consisting of stage and tumor, peritumoral, and bone marrow features from CT and FDG PET-CT significantly stratified patients into low- and high-risk of recurrence/progression.

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Section: Discussionmentioning

confidence: 99%

Predicting recurrence risks in lung cancer patients using multimodal radiomics and random survival forests

et al. 2022

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“…With the continuous development of artificial intelligence, radiomics, as a noninvasive imaging tool, plays a nonnegligible role in the diagnosis and clinical management of cancer [ 12 ]. Radiomics can extract numerous quantitative features from medical images with high throughput and apply automatic or semiautomatic analysis methods to transform imaging data into mineable data.…”

Section: Introductionmentioning

confidence: 99%

Predicting PD-L1 expression status in patients with non-small cell lung cancer using [18F]FDG PET/CT radiomics

Zhao

Zhang

et al. 2023

EJNMMI Res

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Background In recent years, immune checkpoint inhibitor (ICI) therapy has greatly changed the treatment prospects of patients with non-small cell lung cancer (NSCLC). Among the available ICI therapy strategies, programmed death-1 (PD-1)/programmed death ligand-1 (PD-L1) inhibitors are the most widely used worldwide. At present, immunohistochemistry (IHC) is the main method to detect PD-L1 expression levels in clinical practice. However, given that IHC is invasive and cannot reflect the expression of PD-L1 dynamically and in real time, it is of great clinical significance to develop a new noninvasive, accurate radiomics method to evaluate PD-L1 expression levels and predict and filter patients who will benefit from immunotherapy. Therefore, the aim of our study was to assess the predictive power of pretherapy [18F]-fluorodeoxyglucose ([18F]FDG) positron emission tomography/computed tomography (PET/CT)-based radiomics features for PD-L1 expression status in patients with NSCLC. Methods A total of 334 patients with NSCLC who underwent [18F]FDG PET/CT imaging prior to treatment were analyzed retrospectively from September 2016 to July 2021. The LIFEx7.0.0 package was applied to extract 63 PET and 61 CT radiomics features. In the training group, the least absolute shrinkage and selection operator (LASSO) regression model was employed to select the most predictive radiomics features. We constructed and validated a radiomics model, clinical model and combined model. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used to evaluate the predictive performance of the three models in the training group and validation group. In addition, a radiomics nomogram to predict PD-L1 expression status was established based on the optimal predictive model. Results Patients were randomly assigned to a training group (n = 233) and a validation group (n = 101). Two radiomics features were selected to construct the radiomics signature model. Multivariate analysis showed that the clinical stage (odds ratio [OR] 1.579, 95% confidence interval [CI] 0.220–0.703, P < 0.001) was a significant predictor of different PD-L1 expression statuses. The AUC of the radiomics model was higher than that of the clinical model in the training group (0.706 vs. 0.638) and the validation group (0.761 vs. 0.640). The AUCs in the training group and validation group of the combined model were 0.718 and 0.769, respectively. Conclusion PET/CT-based radiomics features demonstrated strong potential in predicting PD-L1 expression status and thus could be used to preselect patients who may benefit from PD-1/PD-L1-based immunotherapy.

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LncRNA MRPL39 inhibits cell proliferation and migration by regulating miR-130/TSC1 axis in non-small cell lung cancer

Fan,

Bao,

Zhao

et al. 2024

3 Biotech

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Radiomics in lung cancer for oncologists

Cited by 3 publications

References 65 publications

Predicting recurrence risks in lung cancer patients using multimodal radiomics and random survival forests

Predicting recurrence risks in lung cancer patients using multimodal radiomics and random survival forests

Predicting PD-L1 expression status in patients with non-small cell lung cancer using [18F]FDG PET/CT radiomics

LncRNA MRPL39 inhibits cell proliferation and migration by regulating miR-130/TSC1 axis in non-small cell lung cancer

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