1
|
Yeghaian M, Bodalal Z, Tareco Bucho T, Kurilova I, Blank C, Smit E, van der Heijden M, Nguyen-Kim T, van den Broek D, Beets-Tan R, Trebeschi S. Integrated noninvasive diagnostics for prediction of survival in immunotherapy. IMMUNO-ONCOLOGY TECHNOLOGY 2024; 24:100723. [PMID: 39185322 PMCID: PMC11342748 DOI: 10.1016/j.iotech.2024.100723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Background Integrating complementary diagnostic data sources promises enhanced robustness in the predictive performance of artificial intelligence (AI) models, a crucial requirement for future clinical validation/implementation. In this study, we investigate the potential value of integrating data from noninvasive diagnostic modalities, including chest computed tomography (CT) imaging, routine laboratory blood tests, and clinical parameters, to retrospectively predict 1-year survival in a cohort of patients with advanced non-small-cell lung cancer, melanoma, and urothelial cancer treated with immunotherapy. Patients and methods The study included 475 patients, of whom 444 had longitudinal CT scans and 475 had longitudinal laboratory data. An ensemble of AI models was trained on data from each diagnostic modality, and subsequently, a model-agnostic integration approach was adopted for combining the prediction probabilities of each modality and producing an integrated decision. Results Integrating different diagnostic data demonstrated a modest increase in predictive performance. The highest area under the curve (AUC) was achieved by CT and laboratory data integration (AUC of 0.83, 95% confidence interval 0.81-0.85, P < 0.001), whereas the performance of individual models trained on laboratory and CT data independently yielded AUCs of 0.81 and 0.73, respectively. Conclusions In our retrospective cohort, integrating different noninvasive data modalities improved performance.
Collapse
Affiliation(s)
- M. Yeghaian
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Z. Bodalal
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - T.M. Tareco Bucho
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - I. Kurilova
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - C.U. Blank
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - E.F. Smit
- Pulmonology Department, Leiden University Medical Center, Leiden, The Netherlands
| | - M.S. van der Heijden
- Department of Medical Oncology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- Department of Molecular Carcinogenesis, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - T.D.L. Nguyen-Kim
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
- Institute of Diagnostic and Interventional Radiology, University Hospital of Zurich, Zurich, Switzerland
| | - D. van den Broek
- Department of Laboratory Medicine, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - R.G.H. Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - S. Trebeschi
- Department of Radiology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW Research Institute for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| |
Collapse
|
2
|
Jin H, Wang Y, Li X, Yang Y, Qi R. Radiomics nomogram for predicting chemo-immunotherapy efficiency in advanced non-small cell lung cancer. Sci Rep 2024; 14:20788. [PMID: 39242619 PMCID: PMC11379930 DOI: 10.1038/s41598-024-63415-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Accepted: 05/28/2024] [Indexed: 09/09/2024] Open
Abstract
This study aimed to explore potential radiomics biomarkers in predicting the efficiency of chemo-immunotherapy in patients with advanced non-small cell lung cancer (NSCLC). Eligible patients were prospectively assigned to receive chemo-immunotherapy, and were divided into a primary cohort (n = 138) and an internal validation cohort (n = 58). Additionally, a separative dataset was used as an external validation cohort (n = 60). Radiomics signatures were extracted and selected from the primary tumor sites from chest CT images. A multivariate logistic regression analysis was conducted to identify the independent clinical predictors. Subsequently, a radiomics nomogram model for predicting the efficiency of chemo-immunotherapy was conducted by integrating the selected radiomics signatures and the independent clinical predictors. The receiver operating characteristic (ROC) curves demonstrated that the radiomics model, the clinical model, and the radiomics nomogram model achieved areas under the curve (AUCs) of 0.85 (95% confidence interval [CI] 0.78-0.92), 0.76 (95% CI 0.68-0.84), and 0.89 (95% CI 0.84-0.94), respectively, in the primary cohort. In the internal validation cohort, the corresponding AUCs were 0.93 (95% CI 0.86-1.00), 0.79 (95% CI 0.68-0.91), and 0.96 (95% CI 0.90-1.00) respectively. Moreover, in the external validation cohort, the AUCs were 0.84 (95% CI 0.72-0.96), 0.75 (95% CI 0.62-0.87), and 0.86 (95% CI 0.75-0.96), respectively. In conclusion, the radiomics nomogram provides a convenient model for predicting the effect of chemo-immunotherapy in advanced NSCLC patients.
Collapse
Affiliation(s)
- Hua Jin
- Department of Respiratory Medicine, Jinshan Hospital, Fudan University, Shanghai, 201508, China
| | - Yuchao Wang
- Department of Medical Imaging, Third Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, 150030, China.
| | - Xushuo Li
- Department of Clinical Laboratory, Jinshan Hospital, Fudan University, Shanghai, 201508, China
| | - Ying Yang
- Department of Clinical Laboratory, Jinshan Hospital, Fudan University, Shanghai, 201508, China
| | - Ruixue Qi
- Department of Center for Tumor Diagnosis and Therapy, Jinshan Hospital, Fudan University, Shanghai, 201508, China.
| |
Collapse
|
3
|
Lo Iacono F, Maragna R, Pontone G, Corino VDA. A Novel Data Augmentation Method for Radiomics Analysis Using Image Perturbations. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01013-0. [PMID: 38710969 DOI: 10.1007/s10278-024-01013-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/20/2023] [Accepted: 12/22/2023] [Indexed: 05/08/2024]
Abstract
Radiomics extracts hundreds of features from medical images to quantitively characterize a region of interest (ROI). When applying radiomics, imbalanced or small dataset issues are commonly addressed using under or over-sampling, the latter being applied directly to the extracted features. Aim of this study is to propose a novel balancing and data augmentation technique by applying perturbations (erosion, dilation, contour randomization) to the ROI in cardiac computed tomography images. From the perturbed ROIs, radiomic features are extracted, thus creating additional samples. This approach was tested addressing the clinical problem of distinguishing cardiac amyloidosis (CA) from aortic stenosis (AS) and hypertrophic cardiomyopathy (HCM). Twenty-one CA, thirty-two AS and twenty-one HCM patients were included in the study. From each original and perturbed ROI, 107 radiomic features were extracted. The CA-AS dataset was balanced using the perturbation-based method along with random over-sampling, adaptive synthetic (ADASYN) and the synthetic minority oversampling technique (SMOTE). The same methods were tested to perform data augmentation dealing with CA and HCM. Features were submitted to robustness, redundancy, and relevance analysis testing five feature selection methods (p-value, least absolute shrinkage and selection operator (LASSO), semi-supervised LASSO, principal component analysis (PCA), semi-supervised PCA). Support vector machine performed the classification tasks, and its performance were evaluated by means of a 10-fold cross-validation. The perturbation-based approach provided the best performances in terms of f1 score and balanced accuracy in both CA-AS (f1 score: 80%, AUC: 0.91) and CA-HCM (f1 score: 86%, AUC: 0.92) classifications. These results suggest that ROI perturbations represent a powerful approach to address both data balancing and augmentation issues.
Collapse
Affiliation(s)
- F Lo Iacono
- Department of Electronics, Information and Bioengineering, Politecnico Di Milano, Milan, Italy.
| | - R Maragna
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - G Pontone
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - V D A Corino
- Department of Electronics, Information and Bioengineering, Politecnico Di Milano, Milan, Italy
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
| |
Collapse
|
4
|
Zhou J, Wen Y, Ding R, Liu J, Fang H, Li X, Zhao K, Wan Q. Radiomics signature based on robust features derived from diffusion data for differentiation between benign and malignant solitary pulmonary lesions. Cancer Imaging 2024; 24:14. [PMID: 38246984 PMCID: PMC10802010 DOI: 10.1186/s40644-024-00660-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 01/10/2024] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Classifying and characterizing pulmonary lesions are critical for clinical decision-making process to identify optimal therapeutic strategies. The purpose of this study was to develop and validate a radiomics nomogram for distinguishing between benign and malignant pulmonary lesions based on robust features derived from diffusion images. MATERIAL AND METHODS The study was conducted in two phases. In the first phase, we prospectively collected 30 patients with pulmonary nodule/mass who underwent twice EPI-DWI scans. The robustness of features between the two scans was evaluated using the concordance correlation coefficient (CCC) and dynamic range (DR). In the second phase, 139 patients who underwent pulmonary DWI were randomly divided into training and test sets in a 7:3 ratio. Maximum relevance minimum redundancy, least absolute shrinkage and selection operator, and logistic regression were used for feature selection and construction of radiomics signatures. Nomograms were established incorporating clinical features, radiomics signatures, and ADC(0, 800). The diagnostic efficiency of different models was evaluated using the area under the curve (AUC) and decision curve analysis. RESULTS Among the features extracted from DWI and ADC images, 42.7% and 37.4% were stable (both CCC and DR ≥ 0.85). The AUCs for distinguishing pulmonary lesions in the test set for clinical model, ADC, ADC radiomics signatures, and DWI radiomics signatures were 0.694, 0.802, 0.885, and 0.767, respectively. The nomogram exhibited the best differentiation performance (AUC = 0.923). The decision curve showed that the nomogram consistently outperformed ADC value and clinical model in lesion differentiation. CONCLUSION Our study demonstrates the robustness of radiomics features derived from lung DWI. The ADC radiomics nomogram shows superior clinical net benefits compared to conventional clinical models or ADC values alone in distinguishing solitary pulmonary lesions, offering a promising tool for noninvasive, precision diagnosis in lung cancer.
Collapse
Affiliation(s)
- Jiaxuan Zhou
- Department of Radiology, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Yu Wen
- Department of Radiology, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Ruolin Ding
- The Second Clinical Medicine School, Guangzhou Medical University, Guangzhou, China
| | - Jieqiong Liu
- Department of Radiology, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Hanzhen Fang
- Department of Radiology, Huilai County People's Hospital, Jieyang, China
| | - Xinchun Li
- Department of Radiology, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China
| | - Kangyan Zhao
- Department of Radiology, The Affiliated Hospital of Hubei University of Arts and Science, Xiangyang Central Hospital, Xiangyang, 441021, Hubei, China.
| | - Qi Wan
- Department of Radiology, The Key Laboratory of Advanced Interdisciplinary Studies Center, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, Guangdong, China.
| |
Collapse
|
5
|
Xiao B, Yu J, Ding PR. Nonoperative Management of dMMR/MSI-H Colorectal Cancer following Neoadjuvant Immunotherapy: A Narrative Review. Clin Colon Rectal Surg 2023; 36:378-384. [PMID: 37795463 PMCID: PMC10547541 DOI: 10.1055/s-0043-1767703] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023]
Abstract
Immunotherapy with PD-1 blockade has achieved a great success in colorectal cancers (CRCs) with high microsatellite instability (MSI-H) and deficient mismatch repair (dMMR), and has become the first-line therapy in metastatic setting. Studies of neoadjuvant immunotherapy also report exciting results, showing high rates of clinical complete response (cCR) and pathological complete response. The high efficacy and long duration of response of immunotherapy has prompt attempts to adopt watch-and-wait strategy for patients achieving cCR following the treatment. Thankfully, the watch-and-wait approach has been proposed for nearly 20 years for patients undergoing chemoradiotherapy and has gained ground among patients as well as clinicians. In this narrative review, we combed through the available information on immunotherapy for CRC and on the watch-and-wait strategy in chemoradiotherapy, and looked forward to a future where neoadjuvant immunotherapy as a curative therapy would play a big part in the treatment of MSI-H/dMMR CRC.
Collapse
Affiliation(s)
- Binyi Xiao
- Department of Colorectal Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Jiehai Yu
- Department of Colorectal Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| | - Pei-Rong Ding
- Department of Colorectal Surgery, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
| |
Collapse
|
6
|
Shieh A, Cen SY, Varghese BA, Hwang D, Lei X, Setayesh A, Siddiqi I, Aron M, Dsouza A, Gill IS, Wallace W, Duddalwar V. Radiomics Correlation to CD68+ Tumor-Associated Macrophages in Clear Cell Renal Cell Carcinoma. Oncology 2023; 102:260-270. [PMID: 37699367 DOI: 10.1159/000534078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/04/2023] [Indexed: 09/14/2023]
Abstract
INTRODUCTION Renal cell carcinoma (RCC) is the ninth most common cancer worldwide, with clear cell RCC (ccRCC) being the most frequent histological subtype. The tumor immune microenvironment (TIME) of ccRCC is an important factor to guide treatment, but current assessments are tissue-based, which can be time-consuming and resource-intensive. In this study, we used radiomics extracted from clinically performed computed tomography (CT) as a noninvasive surrogate for CD68 tumor-associated macrophages (TAMs), a significant component of ccRCC TIME. METHODS TAM population was measured by CD68+/PanCK+ ratio and tumor-TAM clustering was measured by normalized K function calculated from multiplex immunofluorescence (mIF). A total of 1,076 regions on mIF slides from 78 patients were included. Radiomic features were extracted from multiphase CT of the ccRCC tumor. Statistical machine learning models, including random forest, Adaptive Boosting, and ElasticNet, were used to predict TAM population and tumor-TAM clustering. RESULTS The best models achieved an area under the ROC curve of 0.81 (95% CI: [0.69, 0.92]) for TAM population and 0.77 (95% CI: [0.66, 0.88]) for tumor-TAM clustering, respectively. CONCLUSION Our study demonstrates the potential of using CT radiomics-derived imaging markers as a surrogate for assessment of TAM in ccRCC for real-time treatment response monitoring and patient selection for targeted therapies and immunotherapies.
Collapse
Affiliation(s)
- Alexander Shieh
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA,
| | - Steven Y Cen
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Department of Neurology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Bino A Varghese
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Darryl Hwang
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Xiaomeng Lei
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Ali Setayesh
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Imran Siddiqi
- Department of Pathology, University of Southern California, Los Angeles, California, USA
| | - Manju Aron
- Department of Pathology, University of Southern California, Los Angeles, California, USA
| | - Anishka Dsouza
- Division of Medical Oncology, Department of Medicine, University of Southern California, Los Angeles, California, USA
| | - Inderbir S Gill
- Institute of Urology, University of Southern California, Los Angeles, California, USA
| | - William Wallace
- Department of Pathology, University of Southern California, Los Angeles, California, USA
| | - Vinay Duddalwar
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
- Institute of Urology, University of Southern California, Los Angeles, California, USA
| |
Collapse
|
7
|
Limeta A, Gatto F, Herrgård MJ, Ji B, Nielsen J. Leveraging high-resolution omics data for predicting responses and adverse events to immune checkpoint inhibitors. Comput Struct Biotechnol J 2023; 21:3912-3919. [PMID: 37602228 PMCID: PMC10432706 DOI: 10.1016/j.csbj.2023.07.032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 07/17/2023] [Accepted: 07/22/2023] [Indexed: 08/22/2023] Open
Abstract
A long-standing goal of personalized and precision medicine is to enable accurate prediction of the outcomes of a given treatment regimen for patients harboring a disease. Currently, many clinical trials fail to meet their endpoints due to underlying factors in the patient population that contribute to either poor responses to the drug of interest or to treatment-related adverse events. Identifying these factors beforehand and correcting for them can lead to an increased success of clinical trials. Comprehensive and large-scale data gathering efforts in biomedicine by omics profiling of the healthy and diseased individuals has led to a treasure-trove of host, disease and environmental factors that contribute to the effectiveness of drugs aiming to treat disease. With increasing omics data, artificial intelligence allows an in-depth analysis of big data and offers a wide range of applications for real-world clinical use, including improved patient selection and identification of actionable targets for companion therapeutics for improved translatability across more patients. As a blueprint for complex drug-disease-host interactions, we here discuss the challenges of utilizing omics data for predicting responses and adverse events in cancer immunotherapy with immune checkpoint inhibitors (ICIs). The omics-based methodologies for improving patient outcomes as in the ICI case have also been applied across a wide-range of complex disease settings, exemplifying the use of omics for in-depth disease profiling and clinical use.
Collapse
Affiliation(s)
- Angelo Limeta
- Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden
| | - Francesco Gatto
- Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden
- Department of Oncology-Pathology, Karolinska Institute, 171 64 Stockholm, Sweden
| | | | - Boyang Ji
- BioInnovation Institute, 2200 Copenhagen N, Denmark
| | - Jens Nielsen
- Department of Biology and Biological Engineering, Chalmers University of Technology, 412 96 Göteborg, Sweden
- BioInnovation Institute, 2200 Copenhagen N, Denmark
| |
Collapse
|
8
|
Lo Iacono F, Maragna R, Pontone G, Corino VDA. A robust radiomic-based machine learning approach to detect cardiac amyloidosis using cardiac computed tomography. FRONTIERS IN RADIOLOGY 2023; 3:1193046. [PMID: 37588665 PMCID: PMC10426499 DOI: 10.3389/fradi.2023.1193046] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Accepted: 05/26/2023] [Indexed: 08/18/2023]
Abstract
Introduction Cardiac amyloidosis (CA) shares similar clinical and imaging characteristics (e.g., hypertrophic phenotype) with aortic stenosis (AS), but its prognosis is generally worse than severe AS alone. Recent studies suggest that the presence of CA is frequent (1 out of 8 patients) in patients with severe AS. The coexistence of the two diseases complicates the prognosis and therapeutic management of both conditions. Thus, there is an urgent need to standardize and optimize the diagnostic process of CA and AS. The aim of this study is to develop a robust and reliable radiomics-based pipeline to differentiate the two pathologies. Methods Thirty patients were included in the study, equally divided between CA and AS. For each patient, a cardiac computed tomography (CCT) was analyzed by extracting 107 radiomics features from the LV wall. Feature robustness was evaluated by means of geometrical transformations to the ROIs and intra-class correlation coefficient (ICC) computation. Various correlation thresholds (0.80, 0.85, 0.90, 0.95, 1), feature selection methods [p-value, least absolute shrinkage and selection operator (LASSO), semi-supervised LASSO, principal component analysis (PCA), semi-supervised PCA, sequential forwards selection] and machine learning classifiers (k-nearest neighbors, support vector machine, decision tree, logistic regression and gradient boosting) were assessed using a leave-one-out cross-validation. Data augmentation was performed using the synthetic minority oversampling technique. Finally, explainability analysis was performed by using the SHapley Additive exPlanations (SHAP) method. Results Ninety-two radiomic features were selected as robust and used in the further steps. Best performances of classification were obtained using a correlation threshold of 0.95, PCA (keeping 95% of the variance, corresponding to 9 PCs) and support vector machine classifier reaching an accuracy, sensitivity and specificity of 0.93. Four PCs were found to be mainly dependent on textural features, two on first-order statistics and three on shape and size features. Conclusion These preliminary results show that radiomics might be used as non-invasive tool able to differentiate CA from AS using clinical routine available images.
Collapse
Affiliation(s)
- Francesca Lo Iacono
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
| | - Riccardo Maragna
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
| | - Gianluca Pontone
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
| | - Valentina D. A. Corino
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy
- Department of Perioperative Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, Milan, Italy
| |
Collapse
|
9
|
Lévi-Strauss T, Tortorici B, Lopez O, Viau P, Ouizeman DJ, Schall B, Adhoute X, Humbert O, Chevallier P, Gual P, Fillatre L, Anty R. Radiomics, a Promising New Discipline: Example of Hepatocellular Carcinoma. Diagnostics (Basel) 2023; 13:diagnostics13071303. [PMID: 37046521 PMCID: PMC10093101 DOI: 10.3390/diagnostics13071303] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 03/23/2023] [Accepted: 03/27/2023] [Indexed: 04/03/2023] Open
Abstract
Radiomics is a discipline that involves studying medical images through their digital data. Using “artificial intelligence” algorithms, radiomics utilizes quantitative and high-throughput analysis of an image’s textural richness to obtain relevant information for clinicians, from diagnosis assistance to therapeutic guidance. Exploitation of these data could allow for a more detailed characterization of each phenotype, for each patient, making radiomics a new biomarker of interest, highly promising in the era of precision medicine. Moreover, radiomics is non-invasive, cost-effective, and easily reproducible in time. In the field of oncology, it performs an analysis of the entire tumor, which is impossible with a single biopsy but is essential for understanding the tumor’s heterogeneity and is known to be closely related to prognosis. However, current results are sometimes less accurate than expected and often require the addition of non-radiomics data to create a performing model. To highlight the strengths and weaknesses of this new technology, we take the example of hepatocellular carcinoma and show how radiomics could facilitate its diagnosis in difficult cases, predict certain histological features, and estimate treatment response, whether medical or surgical.
Collapse
Affiliation(s)
- Thomas Lévi-Strauss
- Hepatology Unit, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France; (T.L.-S.)
| | - Bettina Tortorici
- Department of Diagnosis and Interventional Imaging, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France
| | - Olivier Lopez
- Department of Diagnosis and Interventional Imaging, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France
| | - Philippe Viau
- Department of Nuclear Medicine, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France
| | - Dann J. Ouizeman
- Hepatology Unit, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France; (T.L.-S.)
| | | | - Xavier Adhoute
- Saint Joseph Hospital, 26 Bd de Louvain, 13008 Marseille, France
| | - Olivier Humbert
- Centre Antoine-Lacassagne, Department of Nuclear Medicine, 33 Av. de Valombrose, 06100 Nice, France
- TIRO-UMR E 4320, Université Côte d’Azur, 06000 Nice, France
| | - Patrick Chevallier
- Department of Diagnosis and Interventional Imaging, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France
| | - Philippe Gual
- INSERM, U1065, C3M, Université Côte d’Azur, 06000 Nice, France
- Correspondence: (P.G.); (R.A.)
| | | | - Rodolphe Anty
- Hepatology Unit, University Hospital of Nice, 151 Route de Saint Antoine de Ginestière, 06200 Nice, France; (T.L.-S.)
- INSERM, U1065, C3M, Université Côte d’Azur, 06000 Nice, France
- Correspondence: (P.G.); (R.A.)
| |
Collapse
|
10
|
Isaksson LJ, Repetto M, Summers PE, Pepa M, Zaffaroni M, Vincini MG, Corrao G, Mazzola G, Rotondi M, Bellerba F, Raimondi S, Haron Z, Alessi S, Pricolo P, Mistretta F, Luzzago S, Cattani F, Musi G, De Cobelli O, Cremonesi M, Orecchia R, Torre DL, Marvaso G, Petralia G, Jereczek-Fossa BA. High-performance prediction models for prostate cancer radiomics. INFORMATICS IN MEDICINE UNLOCKED 2023. [DOI: 10.1016/j.imu.2023.101161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023] Open
|
11
|
Lin ZF, Qin LX, Chen JH. Biomarkers for response to immunotherapy in hepatobiliary malignancies. Hepatobiliary Pancreat Dis Int 2022; 21:413-419. [PMID: 35973935 DOI: 10.1016/j.hbpd.2022.08.002] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/29/2022] [Indexed: 02/05/2023]
Abstract
BACKGROUND The advent of immune checkpoint inhibitors (ICIs) has revolutionized the therapeutic options of hepatobiliary malignancies. However, the clinical benefit provided by immunotherapy seems limited to a small subgroup of patients with hepatobiliary malignancies. The identification of reliable predictors of the response to immunotherapy is urgently needed. DATA SOURCES Literature search was conducted in PubMed for relevant articles published up to May 2022. Information of clinical trials was obtained from https://clinicaltrials.gov/. RESULTS Biomarkers for ICI response of hepatobiliary malignancies remain in the exploration stage and lack compelling evidence. Tumor programmed death-ligand 1 (PD-L1) expression is the most widely studied biomarker in hepatocellular carcinoma (HCC) and biliary tract cancers (BTCs), but there are conflicting results on its predictive potential. Tumor mutational burden (TMB) is generally low both in HCC and BTCs, and the clinical trials of TMB are rare in hepatobiliary malignancies. Promisingly, mismatch repair deficiency (dMMR)/high microsatellite instability (MSI-H) may be a predictive biomarker of response to anti-PD-1 therapy in BTCs. Furthermore, some emerging biomarkers, such as gut microbiota, show predictive potential in the preliminary studies. Radiomics and liquid-biopsy biomarkers, including circulating tumor cells, circulating tumor DNA (ctDNA) and exosomal PD-L1 provide a quick and non-invasive approach for monitoring the ICI response, showing a new promising direction. CONCLUSIONS Multiple potential biomarkers for predicting ICI response of hepatobiliary malignancies have been explored and tried to apply in clinic. Yet there is no robust evidence to prove their clinical value in predicting immunotherapeutic response for patients with hepatobiliary malignancies. The identification of predictors for response to ICIs is an urgent need and major challenge. Further studies are warranted to validate the role of emerging biomarkers in predicting immunotherapeutic responses.
Collapse
Affiliation(s)
- Zhi-Fei Lin
- Department of General Surgery, Huashan Hospital, Cancer Metastasis Institute, Fudan University, Shanghai 200040, China
| | - Lun-Xiu Qin
- Department of General Surgery, Huashan Hospital, Cancer Metastasis Institute, Fudan University, Shanghai 200040, China
| | - Jin-Hong Chen
- Department of General Surgery, Huashan Hospital, Cancer Metastasis Institute, Fudan University, Shanghai 200040, China.
| |
Collapse
|
12
|
Kothari G, Woon B, Patrick CJ, Korte J, Wee L, Hanna GG, Kron T, Hardcastle N, Siva S. The impact of inter-observer variation in delineation on robustness of radiomics features in non-small cell lung cancer. Sci Rep 2022; 12:12822. [PMID: 35896707 PMCID: PMC9329346 DOI: 10.1038/s41598-022-16520-9] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 07/11/2022] [Indexed: 11/25/2022] Open
Abstract
Artificial intelligence and radiomics have the potential to revolutionise cancer prognostication and personalised treatment. Manual outlining of the tumour volume for extraction of radiomics features (RF) is a subjective process. This study investigates robustness of RF to inter-observer variation (IOV) in contouring in lung cancer. We utilised two public imaging datasets: ‘NSCLC-Radiomics’ and ‘NSCLC-Radiomics-Interobserver1’ (‘Interobserver’). For ‘NSCLC-Radiomics’, we created an additional set of manual contours for 92 patients, and for ‘Interobserver’, there were five manual and five semi-automated contours available for 20 patients. Dice coefficients (DC) were calculated for contours. 1113 RF were extracted including shape, first order and texture features. Intraclass correlation coefficient (ICC) was computed to assess robustness of RF to IOV. Cox regression analysis for overall survival (OS) was performed with a previously published radiomics signature. The median DC ranged from 0.81 (‘NSCLC-Radiomics’) to 0.85 (‘Interobserver’—semi-automated). The median ICC for the ‘NSCLC-Radiomics’, ‘Interobserver’ (manual) and ‘Interobserver’ (semi-automated) were 0.90, 0.88 and 0.93 respectively. The ICC varied by feature type and was lower for first order and gray level co-occurrence matrix (GLCM) features. Shape features had a lower median ICC in the ‘NSCLC-Radiomics’ dataset compared to the ‘Interobserver’ dataset. Survival analysis showed similar separation of curves for three of four RF apart from ‘original_shape_Compactness2’, a feature with low ICC (0.61). The majority of RF are robust to IOV, with first order, GLCM and shape features being the least robust. Semi-automated contouring improves feature stability. Decreased robustness of a feature is significant as it may impact upon the features’ prognostic capability.
Collapse
Affiliation(s)
- Gargi Kothari
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Victorian Comprehensive Cancer Centre Building, 305 Grattan Street, Melbourne, VIC, 3000, Australia. .,Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia.
| | - Beverley Woon
- Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia.,Department of Radiology, Cancer Imaging, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Cameron J Patrick
- Statistical Consulting Centre, University of Melbourne, Parkville, Australia
| | - James Korte
- Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Department of Biomedical Engineering, School of Chemical and Biomedical Engineering, University of Melbourne, Melbourne, VIC, Australia
| | - Leonard Wee
- Department of Radiotherapy (MAASTRO), GROW School of Oncology, Maastricht University Medical Centre+, Maastricht, The Netherlands.,Clinical Data Science, Maastricht University, Maastricht, The Netherlands
| | - Gerard G Hanna
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Victorian Comprehensive Cancer Centre Building, 305 Grattan Street, Melbourne, VIC, 3000, Australia.,Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia
| | - Tomas Kron
- Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia.,Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
| | - Nicholas Hardcastle
- Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia.,Department of Physical Sciences, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.,Centre for Medical Radiation Physics, University of Wollongong, Wollongong, NSW, Australia
| | - Shankar Siva
- Department of Radiation Oncology, Peter MacCallum Cancer Centre, Victorian Comprehensive Cancer Centre Building, 305 Grattan Street, Melbourne, VIC, 3000, Australia.,Sir Peter MacCallum Department of Oncology, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia
| |
Collapse
|