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Nardone V, Belfiore MP, De Chiara M, De Marco G, Patanè V, Balestrucci G, Buono M, Salvarezza M, Di Guida G, D'Angiolella D, Grassi R, D'Onofrio I, Cimmino G, Della Corte CM, Gambardella A, Morgillo F, Ciardiello F, Reginelli A, Cappabianca S. CARdioimaging in Lung Cancer PatiEnts Undergoing Radical RadioTherapy: CARE-RT Trial. Diagnostics (Basel) 2023; 13:diagnostics13101717. [PMID: 37238201 DOI: 10.3390/diagnostics13101717] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Revised: 04/06/2023] [Accepted: 04/12/2023] [Indexed: 05/28/2023] Open
Abstract
BACKGROUND Non-small-cell lung cancer (NSCLC) is a common, steady growing lung tumour that is often discovered when a surgical approach is forbidden. For locally advanced inoperable NSCLC, the clinical approach consists of a combination of chemotherapy and radiotherapy, eventually followed by adjuvant immunotherapy, a treatment that is useful but may cause several mild and severe adverse effect. Chest radiotherapy, specifically, may affect the heart and coronary artery, impairing heart function and causing pathologic changes in myocardial tissues. The aim of this study is to evaluate the damage coming from these therapies with the aid of cardiac imaging. METHODS This is a single-centre, prospective clinical trial. Patients with NSCLC who are enrolled will undergo computed tomography (CT) and magnetic resonance imaging (MRI) before chemotherapy 3 months, 6 months, and 9-12 months after the treatment. We expect to enrol 30 patients in 2 years. CONCLUSIONS Our clinical trial will be an opportunity not only to highlight the timing and the radiation dose needed for pathological cardiac tissue changes to happen but will also provide useful data to set new follow-up schedules and strategies, keeping in mind that, more often than not, patients affected by NSCLC may present other heart- and lung-related pathological conditions.
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Affiliation(s)
- Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Maria Paola Belfiore
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Marco De Chiara
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Giuseppina De Marco
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Vittorio Patanè
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Giovanni Balestrucci
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Mauro Buono
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Maria Salvarezza
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Gaetano Di Guida
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Domenico D'Angiolella
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Ida D'Onofrio
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
- Radiotherapy Unit, Ospedale del Mare, ASL Napoli 1 Centro, 80138 Naples, Italy
| | - Giovanni Cimmino
- Department of Translational Medical Science, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | | | - Antonio Gambardella
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Floriana Morgillo
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Fortunato Ciardiello
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138 Naples, Italy
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Nardone V, Reginelli A, De Marco G, Natale G, Patanè V, De Chiara M, Buono M, Russo GM, Monti R, Balestrucci G, Salvarezza M, Di Guida G, D’Ippolito E, Sangiovanni A, Grassi R, D’Onofrio I, Belfiore MP, Cimmino G, Della Corte CM, Vicidomini G, Fiorelli A, Gambardella A, Morgillo F, Cappabianca S. Role of Cardiac Biomarkers in Non-Small Cell Lung Cancer Patients. Diagnostics (Basel) 2023; 13:diagnostics13030400. [PMID: 36766506 PMCID: PMC9914841 DOI: 10.3390/diagnostics13030400] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/17/2023] [Accepted: 01/18/2023] [Indexed: 01/24/2023] Open
Abstract
Treatment-induced cardiac toxicity represents an important issue in non-small cell lung cancer (NSCLC) patients, and no biomarkers are currently available in clinical practice. A novel and easy-to-calculate marker is the quantitative analysis of calcium plaque in the coronary, calculated on CT. It is called the Agatston score (or CAD score). At the same time, other potential predictors include cardiac ultrasonography and anamnesis of the patients. Our work aimed to correlate cardiac biomarkers with overall survival (OS) in NSCLC patients. We retrospectively analyzed patients with NSCLC discussed in the Multidisciplinary Tumor Board of our Institute for the present analysis between January 2018 and July 2022. Inclusion criteria were the availability of basal CT imaging of the thorax, cardiac ultrasonography with the calculation of ejection fraction (EF), and complete anamnesis, including assessment of co-pathologies and pharmacological drugs. The clinical data of the patients were retrospectively collected, and the CAD scores was calculated on a CT scan. All of these parameters were correlated with overall survival (OS) with univariate analysis (Kaplan-Meier analysis) and multivariate analysis (Cox regression analysis). Following the above-mentioned inclusion criteria, 173 patients were included in the present analysis. Of those, 120 patients died in the follow-up period (69.6%), and the median overall survival (OS) was 28 months (mean 47.2 months, 95% CI, 36-57 months). In univariate analysis, several parameters that significantly correlated with lower OS were the stage (p < 0.001), the CAD grading (p < 0.001), history of ischemic heart disease (p: 0.034), use of beta blocker drugs (p: 0.036), and cardiac ejection fraction (p: 0.005). In multivariate analysis, the only parameters that remained significant were as follows: CAD score (p: 0.014, OR 1.56, 95% CI: 1.04-1.83), stage (p: 0.016, OR: 1.26, 95% CI: 1.05-1.53), and cardiac ejection fraction (p: 0.011, OR 0.46, 95% CI: 0.25-0.84). Both CAD score and ejection fraction are correlated with survival in NSCLC patients at all stages of the disease. Independently from the treatment choice, a cardiological evaluation is mandatory for patients with NSCLC.
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Affiliation(s)
- Valerio Nardone
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
- Correspondence:
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Giuseppina De Marco
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Giovanni Natale
- Department of Translational Medical Science, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Vittorio Patanè
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Marco De Chiara
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Mauro Buono
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Gaetano Maria Russo
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Riccardo Monti
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Giovanni Balestrucci
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Maria Salvarezza
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Gaetano Di Guida
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Emma D’Ippolito
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Angelo Sangiovanni
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Ida D’Onofrio
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
- Radiotherapy Unit, Ospedale del Mare, ASL Napoli 1 Centro, 80138 Naples, Italy
| | - Maria Paola Belfiore
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Giovanni Cimmino
- Department of Translational Medical Science, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | | | - Giovanni Vicidomini
- Department of Translational Medical Science, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Alfonso Fiorelli
- Department of Translational Medical Science, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Antonio Gambardella
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Floriana Morgillo
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy
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Robustness of Radiomics in Pre-Surgical Computer Tomography of Non-Small-Cell Lung Cancer. J Pers Med 2022; 13:jpm13010083. [PMID: 36675744 PMCID: PMC9864775 DOI: 10.3390/jpm13010083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 12/20/2022] [Indexed: 12/31/2022] Open
Abstract
Background: Radiomic features are increasingly used in CT of NSCLC. However, their robustness with respect to segmentation variability has not yet been demonstrated. The aim of this study was to assess radiomic features agreement across three kinds of segmentation. Methods: We retrospectively included 48 patients suffering from NSCLC who underwent pre-surgery CT. Two expert radiologists in consensus manually delineated three 3D-ROIs on each patient. To assess robustness for each feature, the intra-class correlation coefficient (ICC) across segmentations was evaluated. The ‘sensitivity’ of ICC upon some parameters affecting features computation (such as bin-width for first-order features and pixel-distances for second-order features) was also evaluated. Moreover, an assessment with respect to interpolator and isotropic resolution was also performed. Results: Our results indicate that ‘shape’ features tend to have excellent agreement (ICC > 0.9) across segmentations; moreover, they have approximately zero sensitivity to other parameters. ‘First-order’ features are in general sensitive to parameters variation; however, a few of them showed excellent agreement and low sensitivity (below 0.1) with respect to bin-width and pixel-distance. Similarly, a few second-order features showed excellent agreement and low sensitivity. Conclusions: Our results suggest that a limited number of radiomic features can achieve a high level of reproducibility in CT of NSCLC.
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Inflammatory Microenvironment in Early Non-Small Cell Lung Cancer: Exploring the Predictive Value of Radiomics. Cancers (Basel) 2022; 14:cancers14143335. [PMID: 35884397 PMCID: PMC9323656 DOI: 10.3390/cancers14143335] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 06/30/2022] [Accepted: 07/05/2022] [Indexed: 01/27/2023] Open
Abstract
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.
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Ability of Delta Radiomics to Predict a Complete Pathological Response in Patients with Loco-Regional Rectal Cancer Addressed to Neoadjuvant Chemo-Radiation and Surgery. Cancers (Basel) 2022; 14:cancers14123004. [PMID: 35740669 PMCID: PMC9221458 DOI: 10.3390/cancers14123004] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 05/27/2022] [Accepted: 06/15/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary The present study aimed to investigate the possible use of MRI delta texture analysis (D-TA) in order to predict the extent of pathological response in patients with locally advanced rectal cancer addressed to neoadjuvant chemo-radiotherapy (C-RT) followed by surgery. We found that D-TA may really predict the frequency of pCR in this patient setting and, thus, it may be investigated as a potential item to identify candidate patients who may benefit from an aggressive radical surgery. Abstract We performed a pilot study to evaluate the use of MRI delta texture analysis (D-TA) as a methodological item able to predict the frequency of complete pathological responses and, consequently, the outcome of patients with locally advanced rectal cancer addressed to neoadjuvant chemoradiotherapy (C-RT) and subsequently, to radical surgery. In particular, we carried out a retrospective analysis including 100 patients with locally advanced rectal adenocarcinoma who received C-RT and then radical surgery in three different oncological institutions between January 2013 and December 2019. Our experimental design was focused on the evaluation of the gross tumor volume (GTV) at baseline and after C-RT by means of MRI, which was contoured on T2, DWI, and ADC sequences. Multiple texture parameters were extracted by using a LifeX Software, while D-TA was calculated as percentage of variations in the two time points. Both univariate and multivariate analysis (logistic regression) were, therefore, carried out in order to correlate the above-mentioned TA parameters with the frequency of pathological responses in the examined patients’ population focusing on the detection of complete pathological response (pCR, with no viable cancer cells: TRG 1) as main statistical endpoint. ROC curves were performed on three different datasets considering that on the 21 patients, only 21% achieved an actual pCR. In our training dataset series, pCR frequency significantly correlated with ADC GLCM-Entropy only, when univariate and binary logistic analysis were performed (AUC for pCR was 0.87). A confirmative binary logistic regression analysis was then repeated in the two remaining validation datasets (AUC for pCR was 0.92 and 0.88, respectively). Overall, these results support the hypothesis that D-TA may have a significant predictive value in detecting the occurrence of pCR in our patient series. If confirmed in prospective and multicenter trials, these results may have a critical role in the selection of patients with locally advanced rectal cancer who may benefit form radical surgery after neoadjuvant chemoradiotherapy.
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Clinical Value of CT-Guided Fine Needle Aspiration and Tissue-Core Biopsy of Thoracic Masses in the Dog and Cat. Animals (Basel) 2021; 11:ani11030883. [PMID: 33808888 PMCID: PMC8003793 DOI: 10.3390/ani11030883] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Revised: 03/16/2021] [Accepted: 03/18/2021] [Indexed: 12/21/2022] Open
Abstract
Simple Summary Diagnostic imaging is of paramount importance in the diagnosis of thoracic lesions. Radiology has traditionally been considered the diagnostic procedure of choice for these diseases in addition to a correct cytological and histopathologic diagnosis. In human medicine, Computed Tomography (CT) and CT-guided biopsy are used in the presence of lesions which are not adequately diagnosed with other procedures. In the present study, thoracic lesions from 52 dogs and 10 cats of different sex, breed and size underwent both CT-guided fine-needle aspiration (FNAB) and tissue-core biopsy (TCB). In this study, 59 of 62 histopathological samples were diagnostic (95.2%). Cytology was diagnostic in 43 of 62 samples (69.4%). General accuracy for FNAB and TCB were 67.7% and 95.2%, respectively. Combining the two techniques, the overall mean accuracy for diagnosis was 98.4%. CT-guided FNAB cytology can be considered a useful and reliable technique, especially for small lesions or lesions located close to vital organs and therefore dangerous to biopsy in any other way. Abstract Diagnosis of thoracic lesions on the basis of history and physical examination is often challenging. Diagnostic imaging is therefore of paramount importance in this field. Radiology has traditionally been considered the diagnostic procedure of choice for these diseases. Nevertheless, it is often not possible to differentiate inflammatory/infectious lesions from neoplastic diseases. A correct cytological and histopathologic diagnosis is therefore needed for an accurate diagnosis and subsequent prognostic and therapeutic approach. In human medicine, Computed Tomography (CT) and CT-guided biopsy are used in the presence of lesions which are not adequately diagnosed with other procedures. In the present study, thoracic lesions from 52 dogs and 10 cats of different sex, breed and size underwent both CT-guided fine-needle aspiration (FNAB) and tissue-core biopsy (TCB). Clinical examination, hematobiochemical analysis and chest radiography were performed on all animals. In this study, 59 of 62 histopathological samples were diagnostic (95.2%). Cytology was diagnostic in 43 of 62 samples (69.4%). General sensitivity, accuracy and PPV for FNAB and TCB were 67.7%, 67.7% and 100% and 96.7%, 95.2% and 98.3%, respectively. Combining the two techniques, the overall mean accuracy for diagnosis was 98.4%. Nineteen of 62 cases showed complications (30.6%). Mild pneumothorax was seen in 16 cases, whereas mild hemorrhage occurred in three cases. No major complications were encountered. CT-guided FNAB cytology can be considered a useful and reliable technique, especially for small lesions or lesions located close to vital organs and therefore dangerous to biopsy in other way.
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COVID-19 pneumonia: computer-aided quantification of healthy lung parenchyma, emphysema, ground glass and consolidation on chest computed tomography (CT). Radiol Med 2020; 126:553-560. [PMID: 33206301 PMCID: PMC7673247 DOI: 10.1007/s11547-020-01305-9] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2020] [Accepted: 10/29/2020] [Indexed: 01/08/2023]
Abstract
Objective To calculate by means of a computer-aided tool the volumes of healthy residual lung parenchyma, of emphysema, of ground glass opacity (GGO) and of consolidation on chest computed tomography (CT) in patients with suspected viral pneumonia by COVID-19.
Materials and methods This study included 116 patients that for suspected COVID-19 infection were subjected to the reverse transcription real-time fluorescence polymerase chain reaction (RT-PCR) test. A computer-aided tool was used to calculate on chest CT images healthy residual lung parenchyma, emphysema, GGO and consolidation volumes for both right and left lung. Expert radiologists, in consensus, assessed the CT images using a structured report and attributed a radiological severity score at the disease pulmonary involvement using a scale of five levels. Nonparametric test was performed to assess differences statistically significant among groups.
Results GGO was the most represented feature in suspected CT by COVID-19 infection; it is present in 102/109 (93.6%) patients with a volume percentage value of 19.50% and a median value of 0.64 L, while the emphysema and consolidation volumes were low (0.01 L and 0.03 L, respectively). Among quantified volume, only GGO volume had a difference statistically significant between the group of patients with suspected versus non-suspected CT for COVID-19 (p < < 0.01). There were differences statistically significant among the groups based on radiological severity score in terms of healthy residual parenchyma volume, of GGO volume and of consolidations volume (p < < 0.001).
Conclusion We demonstrated that, using a computer-aided tool, the COVID-19 pneumonia was mirrored with a percentage median value of GGO of 19.50% and that only GGO volume had a difference significant between the patients with suspected or non-suspected CT for COVID-19 infection.
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