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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.
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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
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Ren Q, Li Q, Shao C, Zhang P, Hu Z, Li J, Wang W, Yu Y. Establishing a prognostic model based on immune-related genes and identification of BIRC5 as a potential biomarker for lung adenocarcinoma patients. BMC Cancer 2023; 23:897. [PMID: 37741993 PMCID: PMC10517491 DOI: 10.1186/s12885-023-11249-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2023] [Accepted: 08/03/2023] [Indexed: 09/25/2023] Open
Abstract
BACKGROUND Lung adenocarcinoma (LUAD) is an extraordinarily malignant tumor, with rapidly increasing morbidity and poor prognosis. Immunotherapy has emerged as a hopeful therapeutic modality for lung adenocarcinoma. Furthermore, a prognostic model (based on immune genes) can fulfill the purpose of early diagnosis and accurate prognostic prediction. METHODS Immune-related mRNAs (IRmRNAs) were utilized to construct a prognostic model that sorted patients into high- and low-risk groups. Then, the prediction efficacy of our model was evaluated using a nomogram. The differences in overall survival (OS), the tumor mutation landscape, and the tumor microenvironment were further explored between different risk groups. In addition, the immune genes comprising the prognostic model were subjected to single-cell RNA sequencing to investigate the expression of these immune genes in different cells. Finally, the functions of BIRC5 were validated through in vitro experiments. RESULTS Patients in different risk groups exhibited sharply significant variations in OS, pathway activity, immune cell infiltration, mutation patterns, and immune response. Single-cell RNA sequencing revealed that the expression level of BIRC5 was significantly high in T cells. Cell experiments further revealed that BIRC5 knockdown markedly reduced LUAD cell proliferation. CONCLUSION This model can function as an instrumental variable in the prognostic, molecular, and therapeutic prediction of LUAD, shedding new light on the optimal clinical practice guidelines for LUAD patients.
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Affiliation(s)
- Qianhe Ren
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Qifan Li
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Chenye Shao
- Department of Thoracic Surgery, The First Affiliated Hospital of Soochow University, Suzhou, China
| | - Pengpeng Zhang
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China
| | - Zhuangzhuang Hu
- Department of Urology, Shuyang First People's Hospital, Suqian, China
| | - Jun Li
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
| | - Wei Wang
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
| | - Yue Yu
- Department of Thoracic Surgery, The First Affiliated Hospital of Nanjing Medical University, Nanjing, China.
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Yolchuyeva S, Giacomazzi E, Tonneau M, Ebrahimpour L, Lamaze FC, Orain M, Coulombe F, Malo J, Belkaid W, Routy B, Joubert P, Manem VSK. A Radiomics-Clinical Model Predicts Overall Survival of Non-Small Cell Lung Cancer Patients Treated with Immunotherapy: A Multicenter Study. Cancers (Basel) 2023; 15:3829. [PMID: 37568646 PMCID: PMC10417039 DOI: 10.3390/cancers15153829] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Revised: 07/14/2023] [Accepted: 07/24/2023] [Indexed: 08/13/2023] Open
Abstract
BACKGROUND Immune checkpoint inhibitors (ICIs) are a great breakthrough in cancer treatments and provide improved long-term survival in a subset of non-small cell lung cancer (NSCLC) patients. However, prognostic and predictive biomarkers of immunotherapy still remain an unmet clinical need. In this work, we aim to leverage imaging data and clinical variables to develop survival risk models among advanced NSCLC patients treated with immunotherapy. METHODS This retrospective study includes a total of 385 patients from two institutions who were treated with ICIs. Radiomics features extracted from pretreatment CT scans were used to build predictive models. The objectives were to predict overall survival (OS) along with building a classifier for short- and long-term survival groups. We employed the XGBoost learning method to build radiomics and integrated clinical-radiomics predictive models. Feature selection and model building were developed and validated on a multicenter cohort. RESULTS We developed parsimonious models that were associated with OS and a classifier for short- and long-term survivor groups. The concordance indices (C-index) of the radiomics model were 0.61 and 0.57 to predict OS in the discovery and validation cohorts, respectively. While the area under the curve (AUC) values of the radiomic models for short- and long-term groups were found to be 0.65 and 0.58 in the discovery and validation cohorts. The accuracy of the combined radiomics-clinical model resulted in 0.63 and 0.62 to predict OS and in 0.77 and 0.62 to classify the survival groups in the discovery and validation cohorts, respectively. CONCLUSIONS We developed and validated novel radiomics and integrated radiomics-clinical survival models among NSCLC patients treated with ICIs. This model has important translational implications, which can be used to identify a subset of patients who are not likely to benefit from immunotherapy. The developed imaging biomarkers may allow early prediction of low-group survivors, though additional validation of these radiomics models is warranted.
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Affiliation(s)
- Sevinj Yolchuyeva
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois-Rivières, QC G8Z 4M3, Canada
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (M.O.)
| | - Elena Giacomazzi
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois-Rivières, QC G8Z 4M3, Canada
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (M.O.)
| | - Marion Tonneau
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, Canada
- Université de Médecine de Lille—Université Henri Warembourg, 59020 Lille, France
| | - Leyla Ebrahimpour
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (M.O.)
- Department of Physics, Engineering Physics and Optics, Laval University, Quebec City, QC G1V 4G5, Canada
| | - Fabien C. Lamaze
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (M.O.)
| | - Michele Orain
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (M.O.)
| | - François Coulombe
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (M.O.)
| | - Julie Malo
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, Canada
| | - Wiam Belkaid
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, Canada
| | - Bertrand Routy
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, QC H2X 0A9, Canada
| | - Philippe Joubert
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (M.O.)
- Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University, Québec City, QC G1V 0A6, Canada
| | - Venkata S. K. Manem
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois-Rivières, QC G8Z 4M3, Canada
- Quebec Heart & Lung Institute Research Center, Québec City, QC G1V 4G5, Canada (M.O.)
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Yolchuyeva S, Giacomazzi E, Tonneau M, Lamaze F, Orain M, Coulombe F, Malo J, Belkaid W, Routy B, Joubert P, Manem VSK. Radiomics approaches to predict PD-L1 and PFS in advanced non-small cell lung patients treated with immunotherapy: a multi-institutional study. Sci Rep 2023; 13:11065. [PMID: 37422576 PMCID: PMC10329671 DOI: 10.1038/s41598-023-38076-y] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 07/03/2023] [Indexed: 07/10/2023] Open
Abstract
With the increasing use of immune checkpoint inhibitors (ICIs), there is an urgent need to identify biomarkers to stratify responders and non-responders using programmed death-ligand (PD-L1) expression, and to predict patient-specific outcomes such as progression free survival (PFS). The current study is aimed to determine the feasibility of building imaging-based predictive biomarkers for PD-L1 and PFS through systematically evaluating a combination of several machine learning algorithms with different feature selection methods. A retrospective, multicenter study of 385 advanced NSCLC patients amenable to ICIs was undertaken in two academic centers. Radiomic features extracted from pretreatment CT scans were used to build predictive models for PD-L1 and PFS (short-term vs. long-term survivors). We first employed the LASSO methodology followed by five feature selection methods and seven machine learning approaches to build the predictors. From our analyses, we found several combinations of feature selection methods and machine learning algorithms to achieve a similar performance. Logistic regression with ReliefF feature selection (AUC = 0.64, 0.59 in discovery and validation cohorts) and SVM with Anova F-test feature selection (AUC = 0.64, 0.63 in discovery and validation datasets) were the best-performing models to predict PD-L1 and PFS. This study elucidates the application of suitable feature selection approaches and machine learning algorithms to predict clinical endpoints using radiomics features. Through this study, we identified a subset of algorithms that should be considered in future investigations for building robust and clinically relevant predictive models.
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Affiliation(s)
- Sevinj Yolchuyeva
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois Rivières, Canada
| | - Elena Giacomazzi
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois Rivières, Canada
| | - Marion Tonneau
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, Canada
- Université de médecine de Lille, Lille, France
| | - Fabien Lamaze
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Quebec, Canada
| | - Michele Orain
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Quebec, Canada
| | - François Coulombe
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Quebec, Canada
| | - Julie Malo
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, Canada
| | - Wiam Belkaid
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, Canada
| | - Bertrand Routy
- Centre de Recherche du Centre Hospitalier Universitaire de Montréal, Montréal, Canada
| | - Philippe Joubert
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Quebec, Canada
- Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University, Quebec, Canada
| | - Venkata S K Manem
- Department of Mathematics and Computer Science, Université du Québec à Trois Rivières, Trois Rivières, Canada.
- Institut Universitaire de Cardiologie et de Pneumologie de Québec, Quebec, Canada.
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Ventura D, Schindler P, Masthoff M, Görlich D, Dittmann M, Heindel W, Schäfers M, Lenz G, Wardelmann E, Mohr M, Kies P, Bleckmann A, Roll W, Evers G. Radiomics of Tumor Heterogeneity in 18F-FDG-PET-CT for Predicting Response to Immune Checkpoint Inhibition in Therapy-Naïve Patients with Advanced Non-Small-Cell Lung Cancer. Cancers (Basel) 2023; 15:cancers15082297. [PMID: 37190228 DOI: 10.3390/cancers15082297] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/31/2023] [Accepted: 04/11/2023] [Indexed: 05/17/2023] Open
Abstract
We aimed to evaluate the predictive and prognostic value of baseline 18F-FDG-PET-CT (PET-CT) radiomic features (RFs) for immune checkpoint-inhibitor (CKI)-based first-line therapy in advanced non-small-cell lung cancer (NSCLC) patients. In this retrospective study 44 patients were included. Patients were treated with either CKI-monotherapy or combined CKI-based immunotherapy-chemotherapy as first-line treatment. Treatment response was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST). After a median follow-up of 6.4 months patients were stratified into "responder" (n = 33) and "non-responder" (n = 11). RFs were extracted from baseline PET and CT data after segmenting PET-positive tumor volume of all lesions. A Radiomics-based model was developed based on a Radiomics signature consisting of reliable RFs that allow classification of response and overall progression using multivariate logistic regression. These RF were additionally tested for their prognostic value in all patients by applying a model-derived threshold. Two independent PET-based RFs differentiated well between responders and non-responders. For predicting response, the area under the curve (AUC) was 0.69 for "PET-Skewness" and 0.75 predicting overall progression for "PET-Median". In terms of progression-free survival analysis, patients with a lower value of PET-Skewness (threshold < 0.2014; hazard ratio (HR) 0.17, 95% CI 0.06-0.46; p < 0.001) and higher value of PET-Median (threshold > 0.5233; HR 0.23, 95% CI 0.11-0.49; p < 0.001) had a significantly lower probability of disease progression or death. Our Radiomics-based model might be able to predict response in advanced NSCLC patients treated with CKI-based first-line therapy.
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Affiliation(s)
- David Ventura
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany
- West German Cancer Center (WTZ), 48149 Muenster, Germany
| | - Philipp Schindler
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Clinic for Radiology, University and University Hospital Muenster, 48149 Muenster, Germany
| | - Max Masthoff
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Clinic for Radiology, University and University Hospital Muenster, 48149 Muenster, Germany
| | - Dennis Görlich
- Institute of Biostatistics and Clinical Research, University of Muenster, 48149 Muenster, Germany
| | - Matthias Dittmann
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany
- West German Cancer Center (WTZ), 48149 Muenster, Germany
| | - Walter Heindel
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Clinic for Radiology, University and University Hospital Muenster, 48149 Muenster, Germany
| | - Michael Schäfers
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany
- West German Cancer Center (WTZ), 48149 Muenster, Germany
| | - Georg Lenz
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Department of Medicine A-Hematology, Oncology, Hemostaseology and Pneumology, University Hospital Muenster, 48149 Muenster, Germany
| | - Eva Wardelmann
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Gerhard-Domagk-Institute of Pathology, University Hospital Muenster, 48149 Muenster, Germany
| | - Michael Mohr
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Department of Medicine A-Hematology, Oncology, Hemostaseology and Pneumology, University Hospital Muenster, 48149 Muenster, Germany
| | - Peter Kies
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany
- West German Cancer Center (WTZ), 48149 Muenster, Germany
| | - Annalen Bleckmann
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Department of Medicine A-Hematology, Oncology, Hemostaseology and Pneumology, University Hospital Muenster, 48149 Muenster, Germany
| | - Wolfgang Roll
- Department of Nuclear Medicine, University Hospital Muenster, 48149 Muenster, Germany
- West German Cancer Center (WTZ), 48149 Muenster, Germany
| | - Georg Evers
- West German Cancer Center (WTZ), 48149 Muenster, Germany
- Department of Medicine A-Hematology, Oncology, Hemostaseology and Pneumology, University Hospital Muenster, 48149 Muenster, Germany
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