Prognostic and predictive value of histogram analysis in patients with non-small cell lung cancer refractory to platinum treated by nivolumab: A multicentre retrospective study

Ravanelli, Marco; Agazzi, Giorgio Maria; Milanese, Gianluca; Roca, Elisa; Silva, Mario; Tiseo, Marcello; Rondi, Paolo; Baggi, Alice; Ganeshan, Balaji; Muri, Margherita; Panni, Stefano; Botti, Camilla; Sverzellati, Nicola; Maroldi, Roberto; Berruti, Alfredo; Farina, Davide

doi:10.1016/j.ejrad.2019.07.019

Cited by 12 publications

(5 citation statements)

References 21 publications

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“…The last decade has seen a rapid growth of CTTA, especially applied in oncologic imaging, aiming to assess solid tumors' heterogeneity and aggressiveness [28][29][30][31][32][33][34]. In thoracic oncology, CTTA has demonstrated the feasibility in predicting survival [35] and response to anti-angiogenic chemotherapy and immunotherapy [36][37][38] in lung cancer, in distinguishing lung cancer recurrence from postradiation fibrosis [39], and the ability to assess pulmonary sub-nodules aggressiveness [40].…”

Section: Discussionmentioning

confidence: 99%

Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia

et al. 2021

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Purpose To evaluate the potential role of texture-based radiomics analysis in differentiating Coronavirus Disease-19 (COVID-19) pneumonia from pneumonia of other etiology on Chest CT. Materials and methods One hundred and twenty consecutive patients admitted to Emergency Department, from March 8, 2020, to April 25, 2020, with suspicious of COVID-19 that underwent Chest CT, were retrospectively analyzed. All patients presented CT findings indicative for interstitial pneumonia. Sixty patients with positive COVID-19 real-time reverse transcription polymerase chain reaction (RT-PCR) and 60 patients with negative COVID-19 RT-PCR were enrolled. CT texture analysis (CTTA) was manually performed using dedicated software by two radiologists in consensus and textural features on filtered and unfiltered images were extracted as follows: mean intensity, standard deviation (SD), entropy, mean of positive pixels (MPP), skewness, and kurtosis. Nonparametric Mann–Whitney test assessed CTTA ability to differentiate positive from negative COVID-19 patients. Diagnostic criteria were obtained from receiver operating characteristic (ROC) curves. Results Unfiltered CTTA showed lower values of mean intensity, MPP, and kurtosis in COVID-19 positive patients compared to negative patients (p = 0.041, 0.004, and 0.002, respectively). On filtered images, fine and medium texture scales were significant differentiators; fine texture scale being most significant where COVID-19 positive patients had lower SD (p = 0.004) and MPP (p = 0.004) compared to COVID-19 negative patients. A combination of the significant texture features could identify the patients with positive COVID-19 from negative COVID-19 with a sensitivity of 60% and specificity of 80% (p = 0.001). Conclusions Preliminary evaluation suggests potential role of CTTA in distinguishing COVID-19 pneumonia from other interstitial pneumonia on Chest CT.

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Section: Discussionmentioning

confidence: 99%

Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia

et al. 2021

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“…Prior studies have previously utilised filtrationbased first-order histogram features for evaluating tumour grade, genotyping, treatment response and survival. [11][12][13][14] However, the effectiveness of this technique to differentiate between glioblastoma and PCNSL has not been studied before. We aimed to determine the diagnostic performance of texture features extracted from T1 contrast-enhanced (CE) sequence in differentiating glioblastoma from PCNSL.…”

Section: Introductionmentioning

confidence: 99%

Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis – a machine learning study

et al. 2021

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Objectives To evaluate the diagnostic performance of multiple machine learning classifier models derived from first-order histogram texture parameters extracted from T1-weighted contrast-enhanced images in differentiating glioblastoma and primary central nervous system lymphoma. Methods Retrospective study with 97 glioblastoma and 46 primary central nervous system lymphoma patients. Thirty-six different combinations of classifier models and feature selection techniques were evaluated. Five-fold nested cross-validation was performed. Model performance was assessed for whole tumour and largest single slice using receiver operating characteristic curve. Results The cross-validated model performance was relatively similar for the top performing models for both whole tumour and largest single slice (area under the curve 0.909–0.924). However, there was a considerable difference between the worst performing model (logistic regression with full feature set, area under the curve 0.737) and the highest performing model for whole tumour (least absolute shrinkage and selection operator model with correlation filter, area under the curve 0.924). For single slice, the multilayer perceptron model with correlation filter had the highest performance (area under the curve 0.914). No significant difference was seen between the diagnostic performance of the top performing model for both whole tumour and largest single slice. Conclusions T1 contrast-enhanced derived first-order texture analysis can differentiate between glioblastoma and primary central nervous system lymphoma with good diagnostic performance. The machine learning performance can vary significantly depending on the model and feature selection methods. Largest single slice and whole tumour analysis show comparable diagnostic performance.

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“…Nevertheless, as PD-L1 expression was available only in a few patients, a comparison of their model with the PD-L1 status was not possible, representing an important limitation for the study. Likewise, Ravanelli and colleagues [ 23 ] demonstrated on CT images that lung lesions with homogeneous enhancement, expressed by negative values of kurtosis, were less responsive to nivolumab. Intraclass correlation coefficient, ranging between 0.83 and 0.86, demonstrated a good reliability for repeatability of histogram features between the two operators, although retrospective design as well as the absence of an external validation cohort require further studies to confirm these preliminary results.…”

Section: Resultsmentioning

confidence: 99%

The Role of Radiomics in the Era of Immune Checkpoint Inhibitors: A New Protagonist in the Jungle of Response Criteria

Castello

Castellani

Florimonte

et al. 2022

JCM

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Immune checkpoint inhibitors (ICI) have demonstrated encouraging results in terms of durable clinical benefit and survival in several malignancies. Nevertheless, the search to identify an “ideal” biomarker for predicting response to ICI is still far from over. Radiomics is a new translational field of study aiming to extract, by dedicated software, several features from a given medical image, ranging from intensity distribution and spatial heterogeneity to higher-order statistical parameters. Based on these premises, our review aims to summarize the current status of radiomics as a potential predictor of clinical response following immunotherapy treatment. A comprehensive search of PubMed results was conducted. All studies published in English up to and including December 2021 were selected, comprising those that explored computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) for radiomic analyses in the setting of ICI. Several studies have demonstrated the potential applicability of radiomic features in the monitoring of the therapeutic response beyond the traditional morphologic and metabolic criteria, as well as in the prediction of survival or non-invasive assessment of the tumor microenvironment. Nevertheless, important limitations emerge from our review in terms of standardization in feature selection, data sharing, and methods, as well as in external validation. Additionally, there is still need for prospective clinical trials to confirm the potential significant role of radiomics during immunotherapy.

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Prognostic and predictive value of histogram analysis in patients with non-small cell lung cancer refractory to platinum treated by nivolumab: A multicentre retrospective study

Cited by 12 publications

References 21 publications

Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia

Chest CT texture-based radiomics analysis in differentiating COVID-19 from other interstitial pneumonia

Glioblastoma and primary central nervous system lymphoma: differentiation using MRI derived first-order texture analysis – a machine learning study

The Role of Radiomics in the Era of Immune Checkpoint Inhibitors: A New Protagonist in the Jungle of Response Criteria

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