Quantifying intratumor heterogeneity of lung neoplasms with radiomics

Ferreira, José Raniery; Koenigkam-Santos, Marcel; Graves, Catharine de Vita; Correia, Natália Santana Chiari; Cipriano, Federico Enrique Garcia; Fabro, Alexandre Todorovic; Azevedo-Marques, Paulo Mazzoncini de

doi:10.1016/j.clinimag.2020.12.017

Cited by 3 publications

(3 citation statements)

References 6 publications

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“…This may be also related to lower heterogeneity as confirmed also by lower grey level co‐occurrence matrix (GLCM) cluster prominence in responders (Table 3b). More heterogeneous tumors are generally considered more aggressive, 58 while less sharp tumor edges could be related to higher density of the peritumoral space due to tumor invasion into the peripheral normal tissues on the cellular level, which is typical of more infiltrating tumors 59 . In summary, these results suggest that lesions with denser areas and less heterogeneous texture may have a more favorable response.…”

Section: Discussionmentioning

confidence: 82%

“…This may be also related to lower heterogeneity as confirmed also by lower grey level co-occurrence matrix (GLCM) cluster prominence in responders (Table 3b). More heterogeneous tumors are generally considered more aggressive, 58 while less TA B L E 3 Comparison of values of features between the not responding (a) and responding patients (b) in order to interpret models. In order to enhance value differences and facilitate interpretability, the not responding patients with the lowest posterior probability and the responding patients with the highest posterior probability are shown sharp tumor edges could be related to higher density of the peritumoral space due to tumor invasion into the peripheral normal tissues on the cellular level, which is typical of more infiltrating tumors.…”

Section: Discussionmentioning

confidence: 99%

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Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy

Avanzo

Gagliardi

Stancanello³

et al. 2021

Medical Physics

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Purpose The aim of this study is to improve the performance of machine learning (ML) models in predicting response of non‐small cell lung cancer (NSCLC) to stereotactic body radiation therapy (SBRT) by integrating image features from pre‐treatment computed tomography (CT) with features from the biologically effective dose (BED) distribution. Materials and methods Image features, consisting of crafted radiomic features or machine‐learned features extracted using a convolutional neural network, were calculated from pre‐treatment CT data and from dose distributions converted into BED for 80 NSCLC lesions over 76 patients treated with robotic guided SBRT. ML models using different combinations of features were trained to predict complete or partial response according to response criteria in solid tumors, including radiomics CT (RadCT), radiomics CT and BED (RadCT,BED), deep learning (DL) CT (DLCT), and DL CT and BED (DLCT,BED). Training of ML included feature selection by neighborhood component analysis followed by ensemble ML using robust boosting. A model was considered as acceptable when the sum of average sensitivity and specificity on test data in repeated cross validations was at least 1.5. Results Complete or partial response occurred in 58 out of 80 lesions. The best models to predict the tumor response were those using BED variables, achieving significantly better area under curve (AUC) and accuracy than those using only features from CT, including a RadCT,BED model using three radiomic features from BED, which scored an accuracy of 0.799 (95% confidence intervals (0.75–0.85)) and AUC of 0.773 (0.688–0.846), and a DLCT,BED model also using three variables with an accuracy of 0.798 (0.649–0.829) and AUC of 0.812 (0.755–0.867). Conclusion According to our results, the inclusion of BED features improves the response prediction of ML models for lung cancer patients undergoing SBRT, regardless of the use of radiomic or DL features.

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Section: Discussionmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 99%

Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy

Avanzo

Gagliardi

Stancanello³

et al. 2021

Medical Physics

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“…In this study, we considered CT-based radiomics with non-small cell lung cancer (NSCLC) as the disease model of choice due to its high incidence and disease-related mortality (28). Moreover, several studies have suggested the potentials of radiomics in this clinical setting, with preliminary evidence associating features with tumor heterogeneity (29,30), gene expression (31,32) and clinical outcomes, also in response to radiotherapy (33). Moreover, the incorporation of radiomic signatures into prognostic and predictive models has yielded better performances compared to models built with clinical parameters only (34).…”

Section: Introductionmentioning

confidence: 99%

Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer

Volpe

Isaksson

Zaffaroni

et al. 2022

Transl Lung Cancer Res

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Background: No evidence supports the choice of specific imaging filtering methodologies in radiomics. As the volume of the primary tumor is a well-recognized prognosticator, our purpose is to assess how filtering may impact the feature/volume dependency in computed tomography (CT) images of non-small cell lung cancer (NSCLC), and if such impact translates into differences in the performance of survival modeling. The role of lesion volume in model performances was also considered and discussed.Methods: Four-hundred seventeen CT images NSCLC patients were retrieved from the NSCLC-Radiomics public repository. Pre-processing and features extraction were implemented using Pyradiomics v3.0.1. Features showing high correlation with volume across original and filtered images were excluded.Cox PH with LASSO regularization and CatBoost models were built with and without volume, and their concordance (C-) indices were compared using Wilcoxon signed-ranked test. The Mann Whitney U test was used to assess model performances after stratification into two groups based on low-and high-volume lesions.Results: Radiomic models significantly outperformed models built on only clinical variables and volume.However, the exclusion/inclusion of volume did not generally alter the performances of radiomic models.Overall, performances were not substantially affected by the choice of either imaging filter (overall C-index 0.539-0.590 for Cox PH and 0.589-0.612 for CatBoost). The separation of patients with high-volume lesions resulted in significantly better performances in 2/10 and 7/10 cases for Cox PH and CatBoost models, respectively. Both low-and high-volume models performed significantly better with the inclusion of *, affiliation at the time of the study.

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CT-based radiomics prediction of complete response after stereotactic body radiation therapy for patients with lung metastases

Cilla,

Pistilli,

Romano

et al. 2023

Strahlenther Onkol

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Quantifying intratumor heterogeneity of lung neoplasms with radiomics

Cited by 3 publications

References 6 publications

Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy

Combining computed tomography and biologically effective dose in radiomics and deep learning improves prediction of tumor response to robotic lung stereotactic body radiation therapy

Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer

CT-based radiomics prediction of complete response after stereotactic body radiation therapy for patients with lung metastases

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