Inflammatory Microenvironment in Early Non-Small Cell Lung Cancer: Exploring the Predictive Value of Radiomics
Patient prognosis is a critical consideration in the treatment decision-making process. Conventionally, patient outcome is related to tumor characteristics, the cancer spread, and the patients’ conditions. However, unexplained differences in survival time are often observed, even among patients with...
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| Veröffentlicht in: | Cancers 2022-07, Vol.14 (14), p.3335 |
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| creator | Perrone, Mariasole Raimondi, Edoardo Costa, Matilde Rasetto, Gianluca Rizzati, Roberto 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 |
| description | Patient prognosis is a critical consideration in the treatment decision-making process. Conventionally, patient outcome is related to tumor characteristics, the cancer spread, and the patients’ conditions. However, unexplained differences in survival time are often observed, even among patients with similar clinical and molecular tumor traits. This study investigated how inflammatory radiomic features can correlate with evidence-based biological analyses to provide translated value in assessing clinical outcomes in patients with NSCLC. We analyzed a group of 15 patients with stage I NSCLC who showed extremely different OS outcomes despite apparently harboring the same tumor characteristics. We thus analyzed the inflammatory levels in their tumor microenvironment (TME) either biologically or radiologically, focusing our attention on the NLRP3 cancer-dependent inflammasome pathway. We determined an NLRP3-dependent peritumoral inflammatory status correlated with the outcome of NSCLC patients, with markedly increased OS in those patients with a low rate of NLRP3 activation. We consistently extracted specific radiomic signatures that perfectly discriminated patients’ inflammatory levels and, therefore, their clinical outcomes. We developed and validated a radiomic model unleashing quantitative inflammatory features from CT images with an excellent performance to predict the evolution pattern of NSCLC tumors for a personalized and accelerated patient management in a non-invasive way. |
| doi_str_mv | 10.3390/cancers14143335 |
| format | Article |
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Conventionally, patient outcome is related to tumor characteristics, the cancer spread, and the patients’ conditions. However, unexplained differences in survival time are often observed, even among patients with similar clinical and molecular tumor traits. This study investigated how inflammatory radiomic features can correlate with evidence-based biological analyses to provide translated value in assessing clinical outcomes in patients with NSCLC. We analyzed a group of 15 patients with stage I NSCLC who showed extremely different OS outcomes despite apparently harboring the same tumor characteristics. We thus analyzed the inflammatory levels in their tumor microenvironment (TME) either biologically or radiologically, focusing our attention on the NLRP3 cancer-dependent inflammasome pathway. We determined an NLRP3-dependent peritumoral inflammatory status correlated with the outcome of NSCLC patients, with markedly increased OS in those patients with a low rate of NLRP3 activation. We consistently extracted specific radiomic signatures that perfectly discriminated patients’ inflammatory levels and, therefore, their clinical outcomes. We developed and validated a radiomic model unleashing quantitative inflammatory features from CT images with an excellent performance to predict the evolution pattern of NSCLC tumors for a personalized and accelerated patient management in a non-invasive way.</description><identifier>ISSN: 2072-6694</identifier><identifier>EISSN: 2072-6694</identifier><identifier>DOI: 10.3390/cancers14143335</identifier><identifier>PMID: 35884397</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Biological markers ; Biopsy ; Cancer therapies ; Carcinogenesis ; Comorbidity ; Computed tomography ; CT imaging ; Decision making ; Development and progression ; Identification and classification ; IL-1β ; Inflammasomes ; Inflammation ; Lung cancer ; Lung cancer, Non-small cell ; Medical prognosis ; Non-small cell lung carcinoma ; Pathology ; Patients ; Prognosis ; Radiation therapy ; Radiomics ; Small cell lung carcinoma ; Software ; Statistical analysis ; Surgery ; Tumor microenvironment ; Tumors</subject><ispartof>Cancers, 2022-07, Vol.14 (14), p.3335</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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Conventionally, patient outcome is related to tumor characteristics, the cancer spread, and the patients’ conditions. However, unexplained differences in survival time are often observed, even among patients with similar clinical and molecular tumor traits. This study investigated how inflammatory radiomic features can correlate with evidence-based biological analyses to provide translated value in assessing clinical outcomes in patients with NSCLC. We analyzed a group of 15 patients with stage I NSCLC who showed extremely different OS outcomes despite apparently harboring the same tumor characteristics. We thus analyzed the inflammatory levels in their tumor microenvironment (TME) either biologically or radiologically, focusing our attention on the NLRP3 cancer-dependent inflammasome pathway. We determined an NLRP3-dependent peritumoral inflammatory status correlated with the outcome of NSCLC patients, with markedly increased OS in those patients with a low rate of NLRP3 activation. 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We developed and validated a radiomic model unleashing quantitative inflammatory features from CT images with an excellent performance to predict the evolution pattern of NSCLC tumors for a personalized and accelerated patient management in a non-invasive way.</description><subject>Biological markers</subject><subject>Biopsy</subject><subject>Cancer therapies</subject><subject>Carcinogenesis</subject><subject>Comorbidity</subject><subject>Computed tomography</subject><subject>CT imaging</subject><subject>Decision making</subject><subject>Development and progression</subject><subject>Identification and classification</subject><subject>IL-1β</subject><subject>Inflammasomes</subject><subject>Inflammation</subject><subject>Lung cancer</subject><subject>Lung cancer, Non-small cell</subject><subject>Medical prognosis</subject><subject>Non-small cell lung carcinoma</subject><subject>Pathology</subject><subject>Patients</subject><subject>Prognosis</subject><subject>Radiation 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| subjects | Biological markers Biopsy Cancer therapies Carcinogenesis Comorbidity Computed tomography CT imaging Decision making Development and progression Identification and classification IL-1β Inflammasomes Inflammation Lung cancer Lung cancer, Non-small cell Medical prognosis Non-small cell lung carcinoma Pathology Patients Prognosis Radiation therapy Radiomics Small cell lung carcinoma Software Statistical analysis Surgery Tumor microenvironment Tumors |
| title | Inflammatory Microenvironment in Early Non-Small Cell Lung Cancer: Exploring the Predictive Value of Radiomics |
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