Inflammatory Microenvironment in Early Non-Small Cell Lung Cancer: Exploring the Predictive Value of Radiomics

Perrone, Mariasole; E, Raimondi; Costa, Matilde; Rasetto, Gianluca; Lanza, Giovanni; Gafà, Roberta; Cavallesco, Giorgio; Tamburini, Nicola; Maniscalco, Pio; Mantovani, Maria Cristina; Tebano, Umberto; Coeli, Manuela; Missiroli, Sonia; Tilli, Massimo; Pinton, Paolo; Giorgi, Carlotta; Fiorica, Francesco

doi:10.3390/cancers14143335

Cited by 6 publications

(4 citation statements)

References 31 publications

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“…The AUCs of the intratumoral and peritumoral model testing sets were 0.76 and 0.72, respectively, which were low compared with the present study. Perrone et al ( 28 ) developed and validated a model to extract quantitative radiomics features from CT images to predict the evolution of non-small cell lung cancer tumors. Nevertheless, Perrone et al used the “virtual biopsy” concept to extract and analyse only part of the peritumoral area, which may miss more essential parameters.…”

Section: Discussionmentioning

confidence: 99%

The value of intratumoral and peritumoral radiomics features in differentiating early-stage lung invasive adenocarcinoma (≤3 cm) subtypes

Zhou,

Yang,

Xiong

et al. 2024

Transl Cancer Res

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Background The identification of different subtypes of early-stage lung invasive adenocarcinoma before surgery contributes to the precision treatment. Radiomics could be one of the effective and noninvasive identification methods. The value of peritumoral radiomics in predicting the subtypes of early-stage lung invasive adenocarcinoma perhaps clinically useful. Methods This retrospective study included 937 lung adenocarcinomas which were randomly divided into the training set (n=655) and testing set (n=282) with a ratio of 7:3. This study used the univariate and multivariate analysis to choose independent clinical predictors. Radiomics features were extracted from 18 regions of interest (1 intratumoral region and 17 peritumoral regions). Independent and conjoint prediction models were constructed based on radiomics and clinical features. The performance of the models was evaluated using receiver operating characteristic (ROC) curves, accuracy (ACC), sensitivity (SEN), and specificity (SPE). Significant differences between areas under the ROC (AUCs) were estimated using in the Delong test. Results Patient age, smoking history, carcinoembryonic antigen (CEA), lesion location, length, width and clinic behavior were the independent predictors of differentiating early-stage lung invasive adenocarcinoma (≤3 cm) subtypes. The highest AUC value among the 19 independent models was obtained for the PTV 0~+3 radiomics model with 0.849 for the training set and 0.854 for the testing set. As the peritumoral distance increased, the predictive power of the models decreased. The radiomics-clinical conjoint model was statistically significantly different from the other models in the Delong test (P<0.05). Conclusions The intratumoral and peritumoral regions contained a wealth of clinical information. The diagnostic efficacy of intra-peritumoral radiomics combined clinical model was further improved, which was particularly important for preoperative staging and treatment decision-making.

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Section: Discussionmentioning

confidence: 99%

The value of intratumoral and peritumoral radiomics features in differentiating early-stage lung invasive adenocarcinoma (≤3 cm) subtypes

Zhou,

Yang,

Xiong

et al. 2024

Transl Cancer Res

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“…There was a lot of research focusing on radiomic-based predicting model to predict TIME (Jiang et al 2020 ; Perrone et al 2022 ; Sun et al 2018 ; Zheng et al 2022 ), microvascular invasion (Yang et al 2019b ), the grade of HCC (Wu et al 2019 ), recurrence (Wen et al 2021 ), and prognosis of HCC (Long et al 2019 ). In one study, Immunoscore was developed based on 27 immune features (Jiang et al 2018 ), and later predicted Immunoscore based on CT images (Jiang et al 2020 ).…”

Section: Discussionmentioning

confidence: 99%

A Noninvasive Approach to Evaluate Tumor Immune Microenvironment and Predict Outcomes in Hepatocellular Carcinoma

Wu,

Liu,

Qiu

et al. 2023

Phenomics

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It is widely recognized that tumor immune microenvironment (TIME) plays a crucial role in tumor progression, metastasis, and therapeutic response. Despite several noninvasive strategies have emerged for cancer diagnosis and prognosis, there are still lack of effective radiomic-based model to evaluate TIME status, let alone predict clinical outcome and immune checkpoint inhibitor (ICIs) response for hepatocellular carcinoma (HCC). In this study, we developed a radiomic model to evaluate TIME status within the tumor and predict prognosis and immunotherapy response. A total of 301 patients who underwent magnetic resonance imaging (MRI) examinations were enrolled in our study. The intra-tumoral expression of 17 immune-related molecules were evaluated using co-detection by indexing (CODEX) technology, and we construct Immunoscore (IS) with the least absolute shrinkage and selection operator (LASSO) algorithm and Cox regression method to evaluate TIME. Of 6115 features extracted from MRI, five core features were filtered out, and the Radiomic Immunoscore (RIS) showed high accuracy in predicting TIME status in testing cohort (area under the curve = 0.753). More importantly, RIS model showed the capability of predicting therapeutic response to anti-programmed cell death 1 (PD-1) immunotherapy in an independent cohort with advanced HCC patients (area under the curve = 0.731). In comparison with previously radiomic-based models, our integrated RIS model exhibits not only higher accuracy in predicting prognosis but also the potential guiding significance to HCC immunotherapy.

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“…Perrone et al were able to distinguish the level of inflammation in patients by CT radiomic features based on the expression of CD68 and IL-1β. Additionally, they developed and validated a radiomic model based on quantitative inflammatory features in CT images that could predict the prognosis of patients with non-small cell lung cancer (NSCLC) [ 93 ]. Wang et al were able to effectively predict the expression level of CD27 in head and neck squamous cell carcinoma (HNSCC) patients by CT radiomics modeling [ 94 ].…”

Section: Radiomics Predicts Important Components Of the Tumor Microen...mentioning

confidence: 99%

Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis

Kang,

Qiu,

Luo

et al. 2023

J Transl Med

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The advent of immunotherapy, a groundbreaking advancement in cancer treatment, has given rise to the prominence of the tumor microenvironment (TME) as a critical area of research. The clinical implications of an improved understanding of the TME are significant and far-reaching. Radiomics has been increasingly utilized in the comprehensive assessment of the TME and cancer prognosis. Similarly, the advancement of pathomics, which is based on pathological images, can offer additional insights into the panoramic view and microscopic information of tumors. The combination of pathomics and radiomics has revolutionized the concept of a “digital biopsy”. As genomics and transcriptomics continue to evolve, integrating radiomics with genomic and transcriptomic datasets can offer further insights into tumor and microenvironment heterogeneity and establish correlations with biological significance. Therefore, the synergistic analysis of digital image features (radiomics, pathomics) and genetic phenotypes (genomics) can comprehensively decode and characterize the heterogeneity of the TME as well as predict cancer prognosis. This review presents a comprehensive summary of the research on important radiomics biomarkers for predicting the TME, emphasizing the interplay between radiomics, genomics, transcriptomics, and pathomics, as well as the application of multiomics in decoding the TME and predicting cancer prognosis. Finally, we discuss the challenges and opportunities in multiomics research. In conclusion, this review highlights the crucial role of radiomics and multiomics associations in the assessment of the TME and cancer prognosis. The combined analysis of radiomics, pathomics, genomics, and transcriptomics is a promising research direction with substantial research significance and value for comprehensive TME evaluation and cancer prognosis assessment.

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Inflammatory Microenvironment in Early Non-Small Cell Lung Cancer: Exploring the Predictive Value of Radiomics

Cited by 6 publications

References 31 publications

The value of intratumoral and peritumoral radiomics features in differentiating early-stage lung invasive adenocarcinoma (≤3 cm) subtypes

The value of intratumoral and peritumoral radiomics features in differentiating early-stage lung invasive adenocarcinoma (≤3 cm) subtypes

A Noninvasive Approach to Evaluate Tumor Immune Microenvironment and Predict Outcomes in Hepatocellular Carcinoma

Application of radiomics-based multiomics combinations in the tumor microenvironment and cancer prognosis

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