Exploring Response to Immunotherapy in Non-Small Cell Lung Cancer Using Delta-Radiomics

Barabino, Emanuele; Rossi, Giovanni; Pamparino, Silvia; Fiannacca, Martina; Caprioli, Simone; Fedeli, Alessandro; Zullo, Lodovica; Vagge, Stefano; Cittadini, Giuseppe; Genova, Carlo

doi:10.3390/cancers14020350

Cited by 32 publications

(19 citation statements)

References 26 publications

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“…Combined with clinicopathological information, they successfully predicted the patients who would benefit from ICI treatment (the AUCs of the training and validation groups were 0.848 and 0.795, respectively). Similarly, Barabino et al (37) extracted the radiomics features of lung lesions from CT scans at baseline and the first evaluation and calculated their changes by absolute difference and relative reduction (Delta, D). After feature screening and model construction, 27 delta features were identified, which were able to distinguish the response to NSCLC immunotherapy with statistically significant accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, Barabino et al. ( 37 ) extracted the radiomics features of lung lesions from CT scans at baseline and the first evaluation and calculated their changes by absolute difference and relative reduction (Delta, Δ). After feature screening and model construction, 27 delta features were identified, which were able to distinguish the response to NSCLC immunotherapy with statistically significant accuracy.…”

Section: Discussionmentioning

confidence: 99%

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CT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy

Song

et al. 2022

Front. Oncol.

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ObjectivesIn radiomics, high-throughput algorithms extract objective quantitative features from medical images. In this study, we evaluated CT-based radiomics features, clinical features, in-depth learning features, and a combination of features for predicting a good pathological response (GPR) in non-small cell lung cancer (NSCLC) patients receiving immunotherapy-based neoadjuvant therapy (NAT).Materials and methodsWe reviewed 62 patients with NSCLC who received surgery after immunotherapy-based NAT and collected clinicopathological data and CT images before and after immunotherapy-based NAT. A series of image preprocessing was carried out on CT scanning images: tumor segmentation, conventional radiomics feature extraction, deep learning feature extraction, and normalization. Spearman correlation coefficient, principal component analysis (PCA), and least absolute shrinkage and selection operator (LASSO) were used to screen features. The pretreatment traditional radiomics combined with clinical characteristics (before_rad_cil) model and pretreatment deep learning characteristics (before_dl) model were constructed according to the data collected before treatment. The data collected after NAT created the after_rad_cil model and after_dl model. The entire model was jointly constructed by all clinical features, conventional radiomics features, and deep learning features before and after neoadjuvant treatment. Finally, according to the data obtained before and after treatment, the before_nomogram and after_nomogram were constructed.ResultsIn the before_rad_cil model, four traditional radiomics features (“original_shape_flatness,” “wavelet hhl_firer_skewness,” “wavelet hlh_firer_skewness,” and “wavelet lll_glcm_correlation”) and two clinical features (“gender” and “N stage”) were screened out to predict a GPR. The average prediction accuracy (ACC) after modeling with k-nearest neighbor (KNN) was 0.707. In the after_rad_cil model, nine features predictive of GPR were obtained after feature screening, among which seven were traditional radiomics features: “exponential_firer_skewness,” “exponential_glrlm_runentropy,” “log- sigma-5-0-mm-3d_firer_kurtosis,” “logarithm_skewness,” “original_shape_elongation,” “original_shape_brilliance,” and “wavelet llh_glcm_clustershade”; two were clinical features: “after_CRP” and “after lymphocyte percentage.” The ACC after modeling with support vector machine (SVM) was 0.682. The before_dl model and after_dl model were modeled by SVM, and the ACC was 0.629 and 0.603, respectively. After feature screening, the entire model was constructed by multilayer perceptron (MLP), and the ACC of the GPR was the highest, 0.805. The calibration curve showed that the predictions of the GPR by the before_nomogram and after_nomogram were in consensus with the actual GPR.ConclusionCT-based radiomics has a good predictive ability for a GPR in NSCLC patients receiving immunotherapy-based NAT. Among the radiomics features combined with the clinicopathological information model, deep learning feature model, and the entire model, the entire model had the highest prediction accuracy.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

CT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy

Song

et al. 2022

Front. Oncol.

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“…The study by Liu et al [18] showed that relative variations in CT texture following treatment have the potential to assess the pathologic response to chemotherapy in patients with CRLMs and may be stronger indicators of response than changes in lesion size or volume. E. Barabino et al [19] showed that Delta-radiomic characteristics have the ability to overcome the limitations of iRECIST in immunotherapy and, potentially, quantify treatment response and predict treatment course. Lin X et al [20]found that the CT radiomics features were signi cantly changed in both the response group and the nonresponse group after two cycles of chemotherapy, and Post-chemotherapy radiomics features, delta features were helpful to evaluate the early chemotherapeutic effect.…”

Section: Discussionmentioning

confidence: 99%

CT delta-radiomics for identification of advanced EGFR-Mutated lung adenocarcinoma Sensitive to Therapies Targeting EGFR Pathway

Zou

Mao

Zhu

et al. 2022

Preprint

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Purpose: In this study, a non-invasive radiomics method was used to extract a large amount of valuable information from patient CT images (including pre-treatment and follow-up images), and the image features were effectively combined with treatment effect evaluation, aiming to solve the problem of lung adenocarcinoma in patients with EGFR mutation positive. The early efficacy evaluation of cancer EGFR-TKI targeted drugs provides a scientific basis for clinical drug selection and adjustment of treatment plans. Methods: It is planned to include 106 patients with EGFR-TKI targeted therapy for lung adenocarcinoma in Shaoxing People's Hospital (pathologically confirmed EGFR mutation positive), and randomly divide them into a training group and a validation group in a ratio of 7:3. The CT plain scan images of patients before and 2 months after treatment were collected, and the images were segmented by ITK-SNAP software. Perform feature screening and modeling, and draw nomograms, calibration curves, and decision curves. Results: After Lasso dimensionality reduction, three features with non-zero coefficients are extracted between groups, sumEntropy,LongRunLowGreyLevelEmphasis_angle0_offset1, LongRunLowGreyLevelEmphasis_angle0_offset4. The average area under the curve (AUC) of the logistic regression model in the training group and the validation group was 0.778 and 0.773, respectively; the Nomogram chart showed that after the three radiomics parameters were scored, the total score was 0-130, and the corresponding effective The risk was 0.1-0.9; the calibration curve showed that the predicted value and the actual clinical observation value approached a 45° slope, indicating that the model was well calibrated; while the decision curve radiomics model was used to evaluate EGFR-TKI in lung adenocarcinoma Targeted drugs have better clinical benefits when they have early efficacy. Conclusions: The radiomics model established based on the radiomics features of CT unenhanced images has good performance in evaluating the efficacy of EGFR-TKI targeted drugs in the treatment of lung adenocarcinoma, and the final Nomogram map can quantitatively evaluate the curative effect, providing individual patient information.

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“…An AUC of 0.81 with radiomics alone jumped to 0.89 with the addition of MGMT values. In a study of patients with NSCLC treated with immune checkpoint inhibitors, Barabino et al 99 measured the changes in radiomic features and identified a set of nine textural features that differentiated PsP from true progression. They also observed that these features were different from those that distinguished patients who responded to therapy from those who experience progression.…”

Section: Using Radiomics To Predict and Distinguish Typical And Atypi...mentioning

confidence: 99%

Novel Imaging Biomarkers to Assess Oncologic Treatment–Related Changes

Viswanathan

Gupta

Madabhushi

2022

American Society of Clinical Oncology Educational Book

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Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such as pseudoprogression may be misinterpreted as treatment nonresponse, other changes, such as hyperprogressive disease seen with immunotherapy, must be recognized early for timely management. Radiation necrosis in the brain is a known response to radiotherapy and must be distinguished from local tumor recurrence. Radiotherapy can also cause adverse effects such as pneumonitis and local tissue toxicity. Systemic therapies, like chemotherapy and targeted therapies, are known to cause long-term cardiovascular effects. Thus, there is a need for robust biomarkers to identify, distinguish, and predict cancer treatment–related changes. Radiomics, which refers to the high-throughput extraction of subvisual features from radiologic images, has been widely explored for disease classification, risk stratification, and treatment-response prediction. Lately, there has been much interest in investigating the role of radiomics to assess oncologic treatment–related changes. We review the utility and various applications of radiomics in identifying and distinguishing atypical responses to treatments, as well as in predicting adverse effects. Although artificial intelligence tools show promise, several challenges—including multi-institutional clinical validation, deployment in health care settings, and artificial-intelligence bias—must be addressed for seamless clinical translation of these tools.

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Exploring Response to Immunotherapy in Non-Small Cell Lung Cancer Using Delta-Radiomics

Cited by 32 publications

References 26 publications

CT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy

CT-based radiomics in predicting pathological response in non-small cell lung cancer patients receiving neoadjuvant immunotherapy

CT delta-radiomics for identification of advanced EGFR-Mutated lung adenocarcinoma Sensitive to Therapies Targeting EGFR Pathway

Novel Imaging Biomarkers to Assess Oncologic Treatment–Related Changes

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