1
|
Majumder S, Katz S, Kontos D, Roshkovan L. State of the art: radiomics and radiomics-related artificial intelligence on the road to clinical translation. BJR Open 2024; 6:tzad004. [PMID: 38352179 PMCID: PMC10860524 DOI: 10.1093/bjro/tzad004] [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: 02/15/2023] [Revised: 09/15/2023] [Accepted: 10/30/2023] [Indexed: 02/16/2024] Open
Abstract
Radiomics and artificial intelligence carry the promise of increased precision in oncologic imaging assessments due to the ability of harnessing thousands of occult digital imaging features embedded in conventional medical imaging data. While powerful, these technologies suffer from a number of sources of variability that currently impede clinical translation. In order to overcome this impediment, there is a need to control for these sources of variability through harmonization of imaging data acquisition across institutions, construction of standardized imaging protocols that maximize the acquisition of these features, harmonization of post-processing techniques, and big data resources to properly power studies for hypothesis testing. For this to be accomplished, it will be critical to have multidisciplinary and multi-institutional collaboration.
Collapse
Affiliation(s)
- Shweta Majumder
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
| | - Sharyn Katz
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
| | - Despina Kontos
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
| | - Leonid Roshkovan
- Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States
| |
Collapse
|
2
|
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.
Collapse
|
3
|
Preliminary Report on Computed Tomography Radiomics Features as Biomarkers to Immunotherapy Selection in Lung Adenocarcinoma Patients. Cancers (Basel) 2021; 13:cancers13163992. [PMID: 34439148 PMCID: PMC8393664 DOI: 10.3390/cancers13163992] [Citation(s) in RCA: 40] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2021] [Revised: 08/03/2021] [Accepted: 08/04/2021] [Indexed: 12/29/2022] Open
Abstract
PURPOSE To assess the efficacy of radiomics features obtained by computed tomography (CT) examination as biomarkers in order to select patients with lung adenocarcinoma who would benefit from immunotherapy. METHODS Seventy-four patients (median age 63 years, range 42-86 years) with histologically confirmed lung cancer who underwent immunotherapy as first- or second-line therapy and who had baseline CT studies were enrolled in this approved retrospective study. As a control group, we selected 50 patients (median age 66 years, range 36-86 years) from 2005 to 2013 with histologically confirmed lung adenocarcinoma who underwent chemotherapy alone or in combination with targeted therapy. A total of 573 radiomic metrics were extracted: 14 features based on Hounsfield unit values specific for lung CT images; 66 first-order profile features based on intensity values; 43 second-order profile features based on lesion shape; 393 third-order profile features; and 57 features with higher-order profiles. Univariate and multivariate statistical analysis with pattern recognition approaches and the least absolute shrinkage and selection operator (LASSO) method were used to assess the capability of extracted radiomics features to predict overall survival (OS) and progression free survival (PFS) time. RESULTS A total of 38 patients (median age 61; range 41-78 years) with confirmed lung adenocarcinoma and subjected to immunotherapy satisfied inclusion criteria, and 50 patients in a control group were included in the analysis The shift in the center of mass of the lesion due to image intensity was significant both to predict OS in patients subjected to immunotherapy and to predict PFS in patients subjected to immunotherapy and in patients in the control group. With univariate analysis, low diagnostic accuracy was reached to stratify patients based on OS and PFS time. Regarding multivariate analysis, considering the robust (two morphological features, three textural features and three higher-order statistical metrics) application of the LASSO approach and all patients, a support vector machine reached the best results for stratifying patients based on OS (area under curve (AUC) of 0.89 and accuracy of 81.6%). Alternatively, considering the robust predictors (six textural features and one higher-order statistical metric) and application of the LASSO approach including all patients, a decision tree reached the best results for stratifying patients based on PFS time (AUC of 0.96 and accuracy of 94.7%). CONCLUSIONS Specific radiomic features could be used to select patients with lung adenocarcinoma who would benefit from immunotherapy because a subset of imaging radiomic features useful to predict OS or PFS time were different between the control group and the immunotherapy group.
Collapse
|
4
|
Fusco R, Granata V, Mazzei MA, Meglio ND, Roscio DD, Moroni C, Monti R, Cappabianca C, Picone C, Neri E, Coppola F, Montanino A, Grassi R, Petrillo A, Miele V. Quantitative imaging decision support (QIDS TM) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan. Cancer Control 2021; 28:1073274820985786. [PMID: 33567876 PMCID: PMC8482708 DOI: 10.1177/1073274820985786] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Objective: To evaluate the consistency of the quantitative imaging decision support (QIDSTM) tool and radiomic analysis using 594 metrics in lung carcinoma on chest CT scan. Materials and Methods: We included, retrospectively, 150 patients with histologically confirmed lung cancer who underwent chemotherapy and baseline and follow-ups CT scans. Using the QIDSTM platform, 3 radiologists segmented each lesion and automatically collected the longest diameter and the density mean value. Inter-observer variability, Bland Altman analysis and Spearman’s correlation coefficient were performed. QIDSTM tool consistency was assessed in terms of agreement rate in the treatment response classification. Kruskal Wallis test and the least absolute shrinkage and selection operator (LASSO) method with 10-fold cross validation were used to identify radiomic metrics correlated with lesion size change. Results: Good and significant correlation was obtained between the measurements of largest diameter and of density among the QIDSTM tool and the radiologists measurements. Inter-observer variability values were over 0.85. HealthMyne QIDSTM tool quantitative volumetric delineation was consistent and matched with each radiologist measurement considering the RECIST classification (80-84%) while a lower concordance among QIDSTM and the radiologists CHOI classification was observed (58-63%). Among 594 extracted metrics, significant and robust predictors of RECIST response were energy, histogram entropy and uniformity, Kurtosis, coronal long axis, longest planar diameter, surface, Neighborhood Grey-Level Different Matrix (NGLDM) dependence nonuniformity and low dependence emphasis as Volume, entropy of Log(2.5 mm), wavelet energy, deviation and root man squared. Conclusion: In conclusion, we demonstrated that HealthMyne quantitative volumetric delineation was consistent and that several radiomic metrics extracted by QIDSTM were significant and robust predictors of RECIST response.
Collapse
Affiliation(s)
- Roberta Fusco
- Radiology Division, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | - Vincenza Granata
- Radiology Division, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | - Maria Antonietta Mazzei
- Department of Radiological Sciences, Diagnostic Imaging Unit, "Azienda Ospedaliera Universitaria Senese," Siena, Italy
| | - Nunzia Di Meglio
- Department of Radiological Sciences, Diagnostic Imaging Unit, "Azienda Ospedaliera Universitaria Senese," Siena, Italy
| | - Davide Del Roscio
- Department of Radiological Sciences, Diagnostic Imaging Unit, "Azienda Ospedaliera Universitaria Senese," Siena, Italy
| | - Chiara Moroni
- Division of Radiodiagnostic, 18561"Azienda Ospedaliero-Universitaria Careggi," Firenze, Italy
| | - Riccardo Monti
- Division of Radiodiagnostic, "Università degli Studi della Campania Luigi Vanvitelli," Naples, Italy
| | - Carlotta Cappabianca
- Division of Radiodiagnostic, "Università degli Studi della Campania Luigi Vanvitelli," Naples, Italy
| | - Carmine Picone
- Radiology Division, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | - Emanuele Neri
- Division of Radiodiagnostic, 9257"Azienda Ospedaliera Universitaria Pisana," Pisa, Italy
| | - Francesca Coppola
- Radiology Unit, Department of Specialized, Diagnostic and Experimental Medicine (DIMES), "S. Orsola Hospital, University of Bologna," Bologna, Italy
| | - Agnese Montanino
- Thoracic Medical Oncology, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli," Naples, Italy
| | - Roberta Grassi
- Division of Radiodiagnostic, "Università degli Studi della Campania Luigi Vanvitelli," Naples, Italy
| | - Antonella Petrillo
- Radiology Division, "Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli", Naples, Italy
| | - Vittorio Miele
- Division of Radiodiagnostic, 18561"Azienda Ospedaliero-Universitaria Careggi," Firenze, Italy
| |
Collapse
|