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Nardone V, Reginelli A, Rubini D, Gagliardi F, Del Tufo S, Belfiore MP, Boldrini L, Desideri I, Cappabianca S. Delta radiomics: an updated systematic review. LA RADIOLOGIA MEDICA 2024:10.1007/s11547-024-01853-4. [PMID: 39017760 DOI: 10.1007/s11547-024-01853-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 07/04/2024] [Indexed: 07/18/2024]
Abstract
BACKGROUND Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and diverse clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches. METHODS Eligible articles were searched in Embase, Pubmed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with 3 key search terms: 'radiomics,' 'texture,' and 'delta.' Studies were analyzed using QUADAS-2 and the RQS tool. RESULTS Forty-eight studies were finally included. The studies were divided into preclinical/methodological (5 studies, 10.4%); rectal cancer (6 studies, 12.5%); lung cancer (12 studies, 25%); sarcoma (5 studies, 10.4%); prostate cancer (3 studies, 6.3%), head and neck cancer (6 studies, 12.5%); gastrointestinal malignancies excluding rectum (7 studies, 14.6%) and other disease sites (4 studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%. CONCLUSIONS Delta radiomics shows potential benefit for several clinical endpoints in oncology, such asdifferential diagnosis, prognosis and prediction of treatment response, evaluation of side effects. Nevertheless, the studies included in this systematic review suffer from the bias of overall low methodological rigor, so that the conclusions are currently heterogeneous, not robust and hardly replicable. Further research with prospective and multicenter studies is needed for the clinical validation of delta radiomics approaches.
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Affiliation(s)
- Valerio Nardone
- 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
| | - Dino Rubini
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Federico Gagliardi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Sara Del Tufo
- 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
| | - Luca Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Isacco Desideri
- Department of Biomedical, Experimental and Clinical Sciences "M. Serio", University of Florence, Florence, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
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Huynh LM, Swanson S, Cima S, Haddadin E, Baine M. Prostate-Specific Membrane Antigen Positron Emission Tomography/Computed Tomography-Derived Radiomic Models in Prostate Cancer Prognostication. Cancers (Basel) 2024; 16:1897. [PMID: 38791977 PMCID: PMC11120365 DOI: 10.3390/cancers16101897] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2024] [Revised: 04/24/2024] [Accepted: 05/14/2024] [Indexed: 05/26/2024] Open
Abstract
The clinical integration of prostate membrane specific antigen (PSMA) positron emission tomography and computed tomography (PET/CT) scans represents potential for advanced data analysis techniques in prostate cancer (PC) prognostication. Among these tools is the use of radiomics, a computer-based method of extracting and quantitatively analyzing subvisual features in medical imaging. Within this context, the present review seeks to summarize the current literature on the use of PSMA PET/CT-derived radiomics in PC risk stratification. A stepwise literature search of publications from 2017 to 2023 was performed. Of 23 articles on PSMA PET/CT-derived prostate radiomics, PC diagnosis, prediction of biopsy Gleason score (GS), prediction of adverse pathology, and treatment outcomes were the primary endpoints of 4 (17.4%), 5 (21.7%), 7 (30.4%), and 7 (30.4%) studies, respectively. In predicting PC diagnosis, PSMA PET/CT-derived models performed well, with receiver operator characteristic curve area under the curve (ROC-AUC) values of 0.85-0.925. Similarly, in the prediction of biopsy and surgical pathology results, ROC-AUC values had ranges of 0.719-0.84 and 0.84-0.95, respectively. Finally, prediction of recurrence, progression, or survival following treatment was explored in nine studies, with ROC-AUC ranging 0.698-0.90. Of the 23 studies included in this review, 2 (8.7%) included external validation. While explorations of PSMA PET/CT-derived radiomic models are immature in follow-up and experience, these results represent great potential for future investigation and exploration. Prior to consideration for clinical use, however, rigorous validation in feature reproducibility and biologic validation of radiomic signatures must be prioritized.
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Affiliation(s)
- Linda My Huynh
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68105, USA; (L.M.H.); (S.C.)
- Department of Urology, University of California, Irvine, CA 92868, USA;
| | - Shea Swanson
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68105, USA; (L.M.H.); (S.C.)
| | - Sophia Cima
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68105, USA; (L.M.H.); (S.C.)
| | - Eliana Haddadin
- Department of Urology, University of California, Irvine, CA 92868, USA;
| | - Michael Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68105, USA; (L.M.H.); (S.C.)
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Rallis KS, Makker S, Ghose A, Sideris M. Assessing Radiological Response to Immunotherapy in Lung Cancer: An Evolving Arena. CANCER DIAGNOSIS & PROGNOSIS 2024; 4:1-8. [PMID: 38173660 PMCID: PMC10758839 DOI: 10.21873/cdp.10278] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Accepted: 12/01/2023] [Indexed: 01/05/2024]
Abstract
In the past decade, immune checkpoint inhibitors (ICIs) have entered the treatment landscape of non-small-cell lung cancer, signalling a paradigm shift within the field characterized by significant survival benefits for patients with advanced and metastatic disease, and especially those with non-targetable genetic oncogenic driver mutations. However, the shift towards immune-based treatments has created new challenges in oncology. Atypical immunotherapy response patterns, including pseudo-progression and hyperprogressive disease, as well as immune-related adverse events have generated the need for new methods to predict patient response to treatment. Hence, new versions of the traditional Response Evaluation Criteria for Solid Tumors (RECIST) have emerged to help characterise with better accuracy radiological findings concerning patient response classification to immunotherapy. This review discusses response evaluation criteria relevant to unique radiological findings observed in patients treated with immunotherapy for non-small-cell lung cancer.
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Affiliation(s)
- Kathrine S Rallis
- Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, U.K
- Barts Cancer Institute, Queen Mary University of London, London, U.K
| | - Shania Makker
- Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, U.K
- University College London Cancer Institute, London, U.K
| | - Aruni Ghose
- Department of Medical Oncology, Barts Cancer Centre, St Bartholomew's Hospital, Barts Health NHS Trust, London, U.K
- Department of Medical Oncology, Mount Vernon Cancer Centre, East and North Hertfordshire NHS Trust, Northwood, U.K
- Department of Medical Oncology, Medway NHS Foundation Trust, Immuno-Oncology Clinical Network, Kent, U.K
| | - Michail Sideris
- Wolfson Institute of Population Health, Cancer Research UK Barts Centre, Queen Mary University of London, London, U.K
- Department of Gynaecological Oncology, Barts Health NHS Trust, Royal London Hospital, London, U.K
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Luo W, Xiu Z, Wang X, McGarry R, Allen J. A Novel Method for Evaluating Early Tumor Response Based on Daily CBCT Images for Lung SBRT. Cancers (Basel) 2023; 16:20. [PMID: 38201447 PMCID: PMC10778260 DOI: 10.3390/cancers16010020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 12/07/2023] [Accepted: 12/11/2023] [Indexed: 01/12/2024] Open
Abstract
BACKGROUND We aimed to develop a new tumor response assessment method for lung SBRT. METHODS In total, 132 lung cancer patients with 134 tumors who received SBRT treatment with daily CBCT were included in this study. The information about tumor size (area), contrast (contrast-to-noise ratio (CNR)), and density/attenuation (μ) was derived from the CBCT images for the first and the last fractions. The ratios of tumor area, CNR, and μ (RA, RCNR, Rμ) between the last and first fractions were calculated for comparison. The product of the three rations was defined as a new parameter (R) for assessment. Tumor response was independently assessed by a radiologist based on a comprehensive analysis of the CBCT images. RESULTS R ranged from 0.27 to 1.67 with a mean value of 0.95. Based on the radiologic assessment results, a receiver operation characteristic (ROC) curve with the area under the curve (AUC) of 95% was obtained and the optimal cutoff value (RC) was determined as 1.1. The results based on RC achieved a 94% accuracy, 94% specificity, and 90% sensitivity. CONCLUSION The results show that R was correlated with early tumor response to lung SBRT and that using R for evaluating tumor response to SBRT would be viable and efficient.
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Affiliation(s)
- Wei Luo
- Department of Radiation Medicine, University of Kentucky, 800 Rose Street, Lexington, KY 40536, USA; (Z.X.); (R.M.)
| | - Zijian Xiu
- Department of Radiation Medicine, University of Kentucky, 800 Rose Street, Lexington, KY 40536, USA; (Z.X.); (R.M.)
| | - Xiaoqin Wang
- Department of Radiology, University of Kentucky, 800 Rose Street, Lexington, KY 40536, USA;
| | - Ronald McGarry
- Department of Radiation Medicine, University of Kentucky, 800 Rose Street, Lexington, KY 40536, USA; (Z.X.); (R.M.)
| | - Joshua Allen
- AdventHealth, 2501 N Orange Ave, Orlando, FL 32804, USA;
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Hanna MG, Ardon O. Digital pathology systems enabling quality patient care. Genes Chromosomes Cancer 2023; 62:685-697. [PMID: 37458325 DOI: 10.1002/gcc.23192] [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: 04/13/2023] [Revised: 06/27/2023] [Accepted: 07/06/2023] [Indexed: 09/20/2023] Open
Abstract
Pathology laboratories are undergoing digital transformations, adopting innovative technologies to enhance patient care. Digital pathology systems impact clinical, education, and research use cases where pathologists use digital technologies to perform tasks in lieu of using glass slides and a microscope. Pathology professional societies have established clinical validation guidelines, and the US Food and Drug Administration have also authorized digital pathology systems for primary diagnosis, including image analysis and machine learning systems. Whole slide images, or digital slides, can be viewed and navigated similar to glass slides on a microscope. These modern tools not only enable pathologists to practice their routine clinical activities, but can potentially enable digital computational discovery. Assimilation of whole slide images in pathology clinical workflow can further empower machine learning systems to support computer assisted diagnostics. The potential enrichment these systems can provide is unprecedented in the field of pathology. With appropriate integration, these clinical decision support systems will allow pathologists to increase the delivery of quality patient care. This review describes the digital pathology transformation process, applicable clinical use cases, incorporation of image analysis and machine learning systems in the clinical workflow, as well as future technologies that may further disrupt pathology modalities to deliver quality patient care.
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Affiliation(s)
- Matthew G Hanna
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Orly Ardon
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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Canahuate G, Wentzel A, Mohamed ASR, van Dijk LV, Vock DM, Elgohari B, Elhalawani H, Fuller CD, Marai GE. Spatially-aware clustering improves AJCC-8 risk stratification performance in oropharyngeal carcinomas. Oral Oncol 2023; 144:106460. [PMID: 37390759 PMCID: PMC10561377 DOI: 10.1016/j.oraloncology.2023.106460] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 05/26/2023] [Accepted: 06/05/2023] [Indexed: 07/02/2023]
Abstract
OBJECTIVE Evaluate the effectiveness of machine learning tools that incorporate spatial information such as disease location and lymph node metastatic patterns-of-spread, for prediction of survival and toxicity in HPV+ oropharyngeal cancer (OPC). MATERIALS & METHODS 675 HPV+ OPC patients that were treated at MD Anderson Cancer Center between 2005 and 2013 with curative intent IMRT were retrospectively collected under IRB approval. Risk stratifications incorporating patient radiometric data and lymph node metastasis patterns via an anatomically-adjacent representation with hierarchical clustering were identified. These clusterings were combined into a 3-level patient stratification and included along with other known clinical features in a Cox model for predicting survival outcomes, and logistic regression for toxicity, using independent subsets for training and validation. RESULTS Four groups were identified and combined into a 3-level stratification. The inclusion of patient stratifications in predictive models for 5-yr Overall survival (OS), 5-year recurrence free survival, (RFS) and Radiation-associated dysphagia (RAD) consistently improved model performance measured using the area under the curve (AUC). Test set AUC improvements over models with clinical covariates, was 9 % for predicting OS, and 18 % for predicting RFS, and 7 % for predicting RAD. For models with both clinical and AJCC covariates, AUC improvement was 7 %, 9 %, and 2 % for OS, RFS, and RAD, respectively. CONCLUSION Including data-driven patient stratifications considerably improve prognosis for survival and toxicity outcomes over the performance achieved by clinical staging and clinical covariates alone. These stratifications generalize well to across cohorts, and sufficient information for reproducing these clusters is included.
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Affiliation(s)
- Guadalupe Canahuate
- Department of Electrical and Computer Engineering, University of Iowa, Iowa City, IA 52242, USA.
| | - Andrew Wentzel
- Department of Computer Science, The University of Illinois at Chicago, Chicago, IL 60612, USA
| | - Abdallah S R Mohamed
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Lisanne V van Dijk
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - David M Vock
- Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA
| | - Baher Elgohari
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Hesham Elhalawani
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Clifton D Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - G Elisabeta Marai
- Department of Computer Science, The University of Illinois at Chicago, Chicago, IL 60612, USA
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7
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Krishna S, Sertic A, Liu Z(A, Liu Z, Darling GE, Yeung J, Wong R, Chen EX, Kalimuthu S, Allen MJ, Suzuki C, Panov E, Ma LX, Bach Y, Jang RW, Swallow CJ, Brar S, Elimova E, Veit-Haibach P. Combination of clinical, radiomic, and "delta" radiomic features in survival prediction of metastatic gastroesophageal adenocarcinoma. Front Oncol 2023; 13:892393. [PMID: 37645426 PMCID: PMC10461093 DOI: 10.3389/fonc.2023.892393] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Accepted: 07/17/2023] [Indexed: 08/31/2023] Open
Abstract
Objectives To identify combined clinical, radiomic, and delta-radiomic features in metastatic gastroesophageal adenocarcinomas (GEAs) that may predict survival outcomes. Methods A total of 166 patients with metastatic GEAs on palliative chemotherapy with baseline and treatment/follow-up (8-12 weeks) contrast-enhanced CT were retrospectively identified. Demographic and clinical data were collected. Three-dimensional whole-lesional radiomic analysis was performed on the treatment/follow-up scans. "Delta" radiomic features were calculated based on the change in radiomic parameters compared to the baseline. The univariable analysis (UVA) Cox proportional hazards model was used to select clinical variables predictive of overall survival (OS) and progression-free survival (PFS) (p-value <0.05). The radiomic and "delta" features were then assessed in a multivariable analysis (MVA) Cox model in combination with clinical features identified on UVA. Features with a p-value <0.01 in the MVA models were selected to assess their pairwise correlation. Only non-highly correlated features (Pearson's correlation coefficient <0.7) were included in the final model. Leave-one-out cross-validation method was used, and the 1-year area under the receiver operating characteristic curve (AUC) was calculated for PFS and OS. Results Of the 166 patients (median age of 59.8 years), 114 (69%) were male, 139 (84%) were non-Asian, and 147 (89%) had an Eastern Cooperative Oncology Group (ECOG) performance status of 0-1. The median PFS and OS on treatment were 3.6 months (95% CI 2.86, 4.63) and 9 months (95% CI 7.49, 11.04), respectively. On UVA, the number of chemotherapy cycles and number of lesions at the end of treatment were associated with both PFS and OS (p < 0.001). ECOG status was associated with OS (p = 0.0063), but not PFS (p = 0.054). Of the delta-radiomic features, delta conventional HUmin, delta gray-level zone length matrix (GLZLM) GLNU, and delta GLZLM LGZE were incorporated into the model for PFS, and delta shape compacity was incorporated in the model for OS. Of the treatment/follow-up radiomic features, shape compacity and neighborhood gray-level dependence matrix (NGLDM) contrast were used in both models. The combined 1-year AUC (Kaplan-Meier estimator) was 0.82 and 0.81 for PFS and OS, respectively. Conclusions A combination of clinical, radiomics, and delta-radiomic features may predict PFS and OS in GEAs with reasonable accuracy.
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Affiliation(s)
- Satheesh Krishna
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Andrew Sertic
- Department of Medical Imaging, University of Toronto, Toronto, ON, Canada
| | - Zhihui (Amy) Liu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Zijin Liu
- Department of Biostatistics, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Gail E. Darling
- Division of Thoracic Oncology, Toronto General Hospital, University Health Network, Toronto, ON, Canada
| | - Jonathon Yeung
- Division of Thoracic Oncology, Toronto General Hospital, University Health Network, Toronto, ON, Canada
| | - Rebecca Wong
- Division of Radiation Oncology, Princess Margaret Hospital, University Health Network, Toronto, ON, Canada
| | - Eric X. Chen
- Division of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Sangeetha Kalimuthu
- Division of Pathology, Toronto General Hospital, University Health Network, Toronto, ON, Canada
| | - Michael J. Allen
- Division of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Chihiro Suzuki
- Division of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Elan Panov
- Division of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Lucy X. Ma
- Division of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Yvonne Bach
- Division of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Raymond W. Jang
- Division of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Carol J. Swallow
- Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Savtaj Brar
- Department of Surgery, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Elena Elimova
- Division of Medical Oncology, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
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Dong W, Ji Y, Pi S, Chen QF. Noninvasive imaging-based machine learning algorithm to identify progressive disease in advanced hepatocellular carcinoma receiving second-line systemic therapy. Sci Rep 2023; 13:10690. [PMID: 37393336 PMCID: PMC10314898 DOI: 10.1038/s41598-023-37862-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Accepted: 06/28/2023] [Indexed: 07/03/2023] Open
Abstract
The aim of this study was to predict tyrosine kinase inhibitors (TKI) plus anti-PD-1 antibodies (TKI-PD-1) efficacy as second-line treatment in advanced hepatocellular carcinoma (HCC) using radiomics analysis. From November 2018 to November 2019, a total of 55 patients were included. Radiomic features were obtained from the CT images before treatment and filtered using intraclass correlation coefficients (ICCs) and least absolute shrinkage and selection operator (LASSO) methods. Subsequently, ten prediction algorithms were developed and validated based on radiomic characteristics. The accuracy of the constructed model was measured through area under the receiver operating characteristic curve (AUC) analysis; survival analysis was performed via Kaplan-Meier and Cox regression analyses. Overall, 18 (32.7%) out of 55 patients had progressive disease. Through ICCs and LASSO, ten radiomic features were entered into the algorithm construction and validation. Ten machine learning algorithms showed different accuracies, with the support vector machine (SVM) model having the highest AUC value of 0.933 in the training cohort and 0.792 in the testing cohort. The radiomic features were associated with overall survival. In conclsion, the SVM algorithm is a useful method to predict TKI-PD-1 efficacy in patients with advanced HCC using images taken prior to treatment.
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Affiliation(s)
- Wei Dong
- Department of Medical Oncology, Nanyang Second People's Hospital, Nanyang, China
| | - Ye Ji
- Department of Medical Oncology, Nanyang Central Hospital, Nanyang, China
| | - Shan Pi
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, No. 600 Tianhe Road, Guangzhou, 510630, Guangdong, China.
| | - Qi-Feng Chen
- Department of Medical Imaging and Interventional Radiology, Sun Yat-Sen University Cancer Center, 651 Dongfeng Road East, Guangzhou, 510060, Guangdong, China.
- State Key Laboratory of Oncology in South China, Guangzhou, Guangdong, China.
- Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China.
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Delasos L, Madabhushi A, Patil PD. Can Radiomics Bridge the Gap Between Immunotherapy and Precision Medicine in Lung Cancer? J Thorac Oncol 2023; 18:686-688. [PMID: 37210178 DOI: 10.1016/j.jtho.2023.03.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Accepted: 03/10/2023] [Indexed: 05/22/2023]
Affiliation(s)
- Lukas Delasos
- Department of Hematology and Medical Oncology, Cleveland Clinic Taussig Cancer Center, Cleveland, Ohio
| | - Anant Madabhushi
- Department of Biomedical Engineering, Emory University, Atlanta, Georgia; Georgia Institute of Technology, Atlanta, Georgia; Atlanta Veterans Affairs Medical Center, Atlanta, Georgia
| | - Pradnya D Patil
- Department of Hematology and Medical Oncology, Cleveland Clinic Taussig Cancer Center, Cleveland, Ohio.
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Kim SJ, Choi JY, Ahn YC, Ahn MJ, Moon SH. The prognostic value of radiomic features from pre- and post-treatment 18F-FDG PET imaging in patients with nasopharyngeal carcinoma. Sci Rep 2023; 13:8462. [PMID: 37231092 DOI: 10.1038/s41598-023-35582-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2022] [Accepted: 05/20/2023] [Indexed: 05/27/2023] Open
Abstract
Positron emission tomography/computed tomography (PET/CT) with 18F-fluorodeoxyglucose (FDG) is widely used for management of nasopharyngeal carcinoma (NPC). Combining the radiomic features of pre- and post-treatment FDG PET images may improve tumor characterization and prognostic predication. We investigated prognostic value of radiomic features from pre- and post-radiotherapy FDG PET images in patients with NPC. Quantitative radiomic features of primary tumors were extracted from the FDG PET images of 145 NPC patients and the delta values were also calculated. The study population was divided randomly into two groups, the training and test sets (7:3). A random survival forest (RSF) model was adopted to perform analyses of progression-free survival (PFS) and overall survival (OS). There were 37 (25.5%) cases of recurrence and 16 (11.0%) cases of death during a median follow-up period of 54.5 months. Both RSF models with clinical variables and radiomic PET features for PFS and OS showed comparable predictive performance to RSF models with clinical variables and conventional PET parameters. Tumoral radiomic features of pre- and post-treatment FDG PET and the corresponding delta values may predict PFS and OS in patients with NPC.
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Affiliation(s)
- Soo Jeong Kim
- Department of Nuclear Medicine, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, 29, Saemunan-ro, Jongno-gu, Seoul, 03181, Republic of Korea
| | - Joon Young Choi
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Yong Chan Ahn
- Department of Radiation Oncology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Myung-Ju Ahn
- Division of Hematology-Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Seung Hwan Moon
- Department of Nuclear Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
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A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques. Healthcare (Basel) 2022; 10:healthcare10102075. [DOI: 10.3390/healthcare10102075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 11/17/2022] Open
Abstract
An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite this, radiomics faces many challenges and limitations. This study sheds light on these limitations by reviewing the studies that used radiomics to predict therapeutic response in CRCLM. Despite radiomics’ potential to enhance clinical decision-making, it lacks standardization. According to the results of this study, the instability of radiomics quantification is caused by changes in CT scan parameters used to obtain CT scans, lesion segmentation methods used for contouring liver metastases, feature extraction methods, and dataset size used for experimentation and validation. Accordingly, the study recommends combining radiomics with deep learning to improve prediction accuracy.
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Braman N, Prasanna P, Bera K, Alilou M, Khorrami M, Leo P, Etesami M, Vulchi M, Turk P, Gupta A, Jain P, Fu P, Pennell N, Velcheti V, Abraham J, Plecha D, Madabhushi A. Novel Radiomic Measurements of Tumor-Associated Vasculature Morphology on Clinical Imaging as a Biomarker of Treatment Response in Multiple Cancers. Clin Cancer Res 2022; 28:4410-4424. [PMID: 35727603 PMCID: PMC9588630 DOI: 10.1158/1078-0432.ccr-21-4148] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 03/14/2022] [Accepted: 06/17/2022] [Indexed: 12/14/2022]
Abstract
PURPOSE The tumor-associated vasculature (TAV) differs from healthy blood vessels by its convolutedness, leakiness, and chaotic architecture, and these attributes facilitate the creation of a treatment-resistant tumor microenvironment. Measurable differences in these attributes might also help stratify patients by likely benefit of systemic therapy (e.g., chemotherapy). In this work, we present a new category of computational image-based biomarkers called quantitative tumor-associated vasculature (QuanTAV) features, and demonstrate their ability to predict response and survival across multiple cancer types, imaging modalities, and treatment regimens involving chemotherapy. EXPERIMENTAL DESIGN We isolated tumor vasculature and extracted mathematical measurements of twistedness and organization from routine pretreatment radiology (CT or contrast-enhanced MRI) of a total of 558 patients, who received one of four first-line chemotherapy-based therapeutic intervention strategies for breast (n = 371) or non-small cell lung cancer (NSCLC, n = 187). RESULTS Across four chemotherapy-based treatment strategies, classifiers of QuanTAV measurements significantly (P < 0.05) predicted response in held out testing cohorts alone (AUC = 0.63-0.71) and increased AUC by 0.06-0.12 when added to models of significant clinical variables alone. Similarly, we derived QuanTAV risk scores that were prognostic of recurrence-free survival in treatment cohorts who received surgery following chemotherapy for breast cancer [P = 0.0022; HR = 1.25; 95% confidence interval (CI), 1.08-1.44; concordance index (C-index) = 0.66] and chemoradiation for NSCLC (P = 0.039; HR = 1.28; 95% CI, 1.01-1.62; C-index = 0.66). From vessel-based risk scores, we further derived categorical QuanTAV high/low risk groups that were independently prognostic among all treatment groups, including patients with NSCLC who received chemotherapy only (P = 0.034; HR = 2.29; 95% CI, 1.07-4.94; C-index = 0.62). QuanTAV response and risk scores were independent of clinicopathologic risk factors and matched or exceeded models of clinical variables including posttreatment response. CONCLUSIONS Across these domains, we observed an association of vascular morphology on CT and MRI-as captured by metrics of vessel curvature, torsion, and organizational heterogeneity-and treatment outcome. Our findings suggest the potential of shape and structure of the TAV in developing prognostic and predictive biomarkers for multiple cancers and different treatment strategies.
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Affiliation(s)
- Nathaniel Braman
- Case Western Reserve University, Cleveland, OH
- Picture Health, Cleveland, OH
| | - Prateek Prasanna
- Case Western Reserve University, Cleveland, OH
- Stony Brook University, New York, NY
| | - Kaustav Bera
- Case Western Reserve University, Cleveland, OH
- University Hospitals Cleveland Medical Center, Cleveland, OH
| | | | | | - Patrick Leo
- Case Western Reserve University, Cleveland, OH
| | - Maryam Etesami
- Yale School of Medicine, Department of Radiology & Biomedical Imaging, New Haven, CT
| | - Manasa Vulchi
- The Cleveland Clinic Foundation (CCF), Cleveland, OH
| | - Paulette Turk
- The Cleveland Clinic Foundation (CCF), Cleveland, OH
| | - Amit Gupta
- University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Prantesh Jain
- University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Pingfu Fu
- Case Western Reserve University, Cleveland, OH
| | | | | | - Jame Abraham
- The Cleveland Clinic Foundation (CCF), Cleveland, OH
| | - Donna Plecha
- University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Anant Madabhushi
- Case Western Reserve University, Cleveland, OH
- Louis Stokes Cleveland Veterans Medical Center, Cleveland, OH
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13
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Xie D, Xu F, Zhu W, Pu C, Huang S, Lou K, Wu Y, Huang D, He C, Hu H. Delta radiomics model for the prediction of progression-free survival time in advanced non-small-cell lung cancer patients after immunotherapy. Front Oncol 2022; 12:990608. [PMID: 36276082 PMCID: PMC9583844 DOI: 10.3389/fonc.2022.990608] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2022] [Accepted: 09/22/2022] [Indexed: 11/22/2022] Open
Abstract
Objective To assess the validity of pre- and posttreatment computed tomography (CT)-based radiomics signatures and delta radiomics signatures for predicting progression-free survival (PFS) in stage III-IV non-small-cell lung cancer (NSCLC) patients after immune checkpoint inhibitor (ICI) therapy. Methods Quantitative image features of the largest primary lung tumours were extracted on CT-enhanced imaging at baseline (time point 0, TP0) and after the 2nd-3rd immunotherapy cycles (time point 1, TP1). The critical features were selected to construct TP0, TP1 and delta radiomics signatures for the risk stratification of patient survival after ICI treatment. In addition, a prediction model integrating the clinicopathologic risk characteristics and phenotypic signature was developed for the prediction of PFS. Results The C-index of TP0, TP1 and delta radiomics models in the training and validation cohort were 0.64, 0.75, 0.80, and 0.61, 0.68, 0.78, respectively. The delta radiomics score exhibited good accuracy for distinguishing patients with slow and rapid progression to ICI treatment. The predictive accuracy of the combined prediction model was higher than that of the clinical prediction model in both training and validation sets (P<0.05), with a C-index of 0.83 and 0.70, respectively. Additionally, the delta radiomics model (C-index of 0.86) had a higher predictive accuracy compared to PD-L1 expression (C-index of 0.50) (P<0.0001). Conclusions The combined prediction model including clinicopathologic characteristics (tumour anatomical classification and brain metastasis) and the delta radiomics signature could achieve the individualized prediction of PFS in ICIs-treated NSCLC patients.
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Affiliation(s)
- Dong Xie
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiology, Shaoxing Second Hospital, Shaoxing, China
| | - Fangyi Xu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Wenchao Zhu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cailing Pu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Shaoyu Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- Department of Radiology, Ningbo Medical Center LiHuili Hospital, Ningbo, China
| | - Kaihua Lou
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yan Wu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Dingpin Huang
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Cong He
- Department of Radiology, Shaoxing Second Hospital, Shaoxing, China
| | - Hongjie Hu
- Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, China
- *Correspondence: Hongjie Hu,
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14
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Gupta A, Bera K, Kikano E, Pierce JD, Gan J, Rajdev M, Ciancibello LM, Gupta A, Rajagopalan S, Gilkeson RC. Coronary Artery Calcium Scoring: Current Status and Future Directions. Radiographics 2022; 42:947-967. [PMID: 35657766 DOI: 10.1148/rg.210122] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Coronary artery calcium (CAC) scores obtained from CT scans have been shown to be prognostic in assessment of the risk for development of cardiovascular diseases, facilitating the prediction of outcome in asymptomatic individuals. Currently, several methods to calculate the CAC score exist, and each has its own set of advantages and disadvantages. Agatston CAC scoring is the most extensively used method. CAC scoring is currently recommended for use in asymptomatic individuals to predict the risk of developing cardiovascular diseases and the disease-specific mortality. In specific subsets of patients, the CAC score has also been recommended for reclassifying cardiovascular risk and aiding in decision making when planning primary prevention interventions such as statin therapy. The progression of CAC scores on follow-up images has been shown to be linked to risk of myocardial infarction and cardiovascular mortality. While the CAC score is a validated tool used clinically, several challenges, including various pitfalls associated with the acquisition, calculation, and interpretation of the score, prevent more widespread adoption of this metric. Recent research has been focused extensively on strategies to improve existing scoring methods, including measuring calcium attenuation, detecting microcalcifications, and focusing on extracoronary calcifications, and on strategies to improve image acquisition. A better understanding of CAC scoring approaches will help radiologists and other physicians better use and interpret these scores in their workflows. An invited commentary by S. Gupta is available online. Online supplemental material is available for this article. ©RSNA, 2022.
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Affiliation(s)
- Amit Gupta
- From the Department of Radiology (Amit Gupta, K.B., E.K., J.D.P., J.G., M.R., L.M.C., R.C.G.) and Harrington Heart & Vascular Institute (S.R.), University Hospitals Cleveland Medical Center/Case Western Reserve University School of Medicine, 11100 Euclid Ave, Cleveland, OH 44106; and Department of Medicine, Mercy Health-St. Elizabeth Youngstown Hospital, Youngstown, OH (Aekta Gupta)
| | - Kaustav Bera
- From the Department of Radiology (Amit Gupta, K.B., E.K., J.D.P., J.G., M.R., L.M.C., R.C.G.) and Harrington Heart & Vascular Institute (S.R.), University Hospitals Cleveland Medical Center/Case Western Reserve University School of Medicine, 11100 Euclid Ave, Cleveland, OH 44106; and Department of Medicine, Mercy Health-St. Elizabeth Youngstown Hospital, Youngstown, OH (Aekta Gupta)
| | - Elias Kikano
- From the Department of Radiology (Amit Gupta, K.B., E.K., J.D.P., J.G., M.R., L.M.C., R.C.G.) and Harrington Heart & Vascular Institute (S.R.), University Hospitals Cleveland Medical Center/Case Western Reserve University School of Medicine, 11100 Euclid Ave, Cleveland, OH 44106; and Department of Medicine, Mercy Health-St. Elizabeth Youngstown Hospital, Youngstown, OH (Aekta Gupta)
| | - Jonathan D Pierce
- From the Department of Radiology (Amit Gupta, K.B., E.K., J.D.P., J.G., M.R., L.M.C., R.C.G.) and Harrington Heart & Vascular Institute (S.R.), University Hospitals Cleveland Medical Center/Case Western Reserve University School of Medicine, 11100 Euclid Ave, Cleveland, OH 44106; and Department of Medicine, Mercy Health-St. Elizabeth Youngstown Hospital, Youngstown, OH (Aekta Gupta)
| | - Jonathan Gan
- From the Department of Radiology (Amit Gupta, K.B., E.K., J.D.P., J.G., M.R., L.M.C., R.C.G.) and Harrington Heart & Vascular Institute (S.R.), University Hospitals Cleveland Medical Center/Case Western Reserve University School of Medicine, 11100 Euclid Ave, Cleveland, OH 44106; and Department of Medicine, Mercy Health-St. Elizabeth Youngstown Hospital, Youngstown, OH (Aekta Gupta)
| | - Maharshi Rajdev
- From the Department of Radiology (Amit Gupta, K.B., E.K., J.D.P., J.G., M.R., L.M.C., R.C.G.) and Harrington Heart & Vascular Institute (S.R.), University Hospitals Cleveland Medical Center/Case Western Reserve University School of Medicine, 11100 Euclid Ave, Cleveland, OH 44106; and Department of Medicine, Mercy Health-St. Elizabeth Youngstown Hospital, Youngstown, OH (Aekta Gupta)
| | - Leslie M Ciancibello
- From the Department of Radiology (Amit Gupta, K.B., E.K., J.D.P., J.G., M.R., L.M.C., R.C.G.) and Harrington Heart & Vascular Institute (S.R.), University Hospitals Cleveland Medical Center/Case Western Reserve University School of Medicine, 11100 Euclid Ave, Cleveland, OH 44106; and Department of Medicine, Mercy Health-St. Elizabeth Youngstown Hospital, Youngstown, OH (Aekta Gupta)
| | - Aekta Gupta
- From the Department of Radiology (Amit Gupta, K.B., E.K., J.D.P., J.G., M.R., L.M.C., R.C.G.) and Harrington Heart & Vascular Institute (S.R.), University Hospitals Cleveland Medical Center/Case Western Reserve University School of Medicine, 11100 Euclid Ave, Cleveland, OH 44106; and Department of Medicine, Mercy Health-St. Elizabeth Youngstown Hospital, Youngstown, OH (Aekta Gupta)
| | - Sanjay Rajagopalan
- From the Department of Radiology (Amit Gupta, K.B., E.K., J.D.P., J.G., M.R., L.M.C., R.C.G.) and Harrington Heart & Vascular Institute (S.R.), University Hospitals Cleveland Medical Center/Case Western Reserve University School of Medicine, 11100 Euclid Ave, Cleveland, OH 44106; and Department of Medicine, Mercy Health-St. Elizabeth Youngstown Hospital, Youngstown, OH (Aekta Gupta)
| | - Robert C Gilkeson
- From the Department of Radiology (Amit Gupta, K.B., E.K., J.D.P., J.G., M.R., L.M.C., R.C.G.) and Harrington Heart & Vascular Institute (S.R.), University Hospitals Cleveland Medical Center/Case Western Reserve University School of Medicine, 11100 Euclid Ave, Cleveland, OH 44106; and Department of Medicine, Mercy Health-St. Elizabeth Youngstown Hospital, Youngstown, OH (Aekta Gupta)
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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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Vaidya P, Bera K, Linden PA, Gupta A, Rajiah PS, Jones DR, Bott M, Pass H, Gilkeson R, Jacono F, Hsieh KLC, Lan GY, Velcheti V, Madabhushi A. Combined Radiomic and Visual Assessment for Improved Detection of Lung Adenocarcinoma Invasiveness on Computed Tomography Scans: A Multi-Institutional Study. Front Oncol 2022; 12:902056. [PMID: 35707362 PMCID: PMC9190758 DOI: 10.3389/fonc.2022.902056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Accepted: 04/19/2022] [Indexed: 12/20/2022] Open
Abstract
Objective The timing and nature of surgical intervention for semisolid abnormalities are dependent upon distinguishing between adenocarcinoma-in-situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (INV). We sought to develop and evaluate a quantitative imaging method to determine invasiveness of small, ground-glass lesions on computed tomography (CT) chest scans. Methods The study comprised 268 patients from 4 institutions with resected (<=3 cm) semisolid lesions with confirmed histopathological diagnosis of MIA/AIS or INV. A total of 248 radiomic texture features from within the tumor nodule (intratumoral) and adjacent to the nodule (peritumoral) were extracted from manually annotated lung nodules of chest CT scans. The datasets were randomly divided, with 40% of patients used for training and 60% used for testing the machine classifier (Training DTrain, N=106; Testing, DTest, N=162). Results The top five radiomic stable features included four intratumoral (Laws and Haralick feature families) and one peritumoral feature within 3 to 6 mm of the nodule (CoLlAGe feature family), which successfully differentiated INV from MIA/AIS nodules with an AUC of 0.917 [0.867-0.967] on DTrain and 0.863 [0.79-0.931] on DTest. The radiomics model successfully differentiated INV from MIA cases (<1 cm AUC: 0.76 [0.53-0.98], 1-2 cm AUC: 0.92 [0.85-0.98], 2-3 cm AUC: 0.95 [0.88-1]). The final integrated model combining the classifier with the radiologists’ score gave the best AUC on DTest (AUC=0.909, p<0.001). Conclusions Addition of advanced image analysis via radiomics to the routine visual assessment of CT scans help better differentiate adenocarcinoma subtypes and can aid in clinical decision making. Further prospective validation in this direction is warranted.
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Affiliation(s)
- Pranjal Vaidya
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Philip A. Linden
- Department of Surgery, Division of Thoracic and Esophageal Surgery, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Amit Gupta
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | | | - David R. Jones
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Matthew Bott
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Harvey Pass
- Department of Cardiothoracic Surgery, New York University (NYU) Langone Health, New York, NY, United States
| | - Robert Gilkeson
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Frank Jacono
- Division of Pulmonary Medicine, Louis Stokes VA Medical Center, Cleveland, OH, United States
| | - Kevin Li-Chun Hsieh
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University and Taipei Medical University Hospital, Taipei, Taiwan
| | - Gong-Yau Lan
- Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University and Taipei Medical University Hospital, Taipei, Taiwan
| | - Vamsidhar Velcheti
- New York University (NYU) Langone Perlmutter Cancer Center, New York, NY, United States
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, United States
- *Correspondence: Anant Madabhushi,
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Vaidya P, Alilou M, Hiremath A, Gupta A, Bera K, Furin J, Armitage K, Gilkeson R, Yuan L, Fu P, Lu C, Ji M, Madabhushi A. An End-to-End Integrated Clinical and CT-Based Radiomics Nomogram for Predicting Disease Severity and Need for Ventilator Support in COVID-19 Patients: A Large Multisite Retrospective Study. FRONTIERS IN RADIOLOGY 2022; 2:781536. [PMID: 36437821 PMCID: PMC9696643 DOI: 10.3389/fradi.2022.781536] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
OBJECTIVE The disease COVID-19 has caused a widespread global pandemic with ~3. 93 million deaths worldwide. In this work, we present three models-radiomics (MRM), clinical (MCM), and combined clinical-radiomics (MRCM) nomogram to predict COVID-19-positive patients who will end up needing invasive mechanical ventilation from the baseline CT scans. METHODS We performed a retrospective multicohort study of individuals with COVID-19-positive findings for a total of 897 patients from two different institutions (Renmin Hospital of Wuhan University, D1 = 787, and University Hospitals, US D2 = 110). The patients from institution-1 were divided into 60% training, D 1 T ( N = 473 ) , and 40% test set D 1 V ( N = 314 ) . The patients from institution-2 were used for an independent validation test set D 2 V ( N = 110 ) . A U-Net-based neural network (CNN) was trained to automatically segment out the COVID consolidation regions on the CT scans. The segmented regions from the CT scans were used for extracting first- and higher-order radiomic textural features. The top radiomic and clinical features were selected using the least absolute shrinkage and selection operator (LASSO) with an optimal binomial regression model within D 1 T . RESULTS The three out of the top five features identified using D 1 T were higher-order textural features (GLCM, GLRLM, GLSZM), whereas the last two features included the total absolute infection size on the CT scan and the total intensity of the COVID consolidations. The radiomics model (MRM) was constructed using the radiomic score built using the coefficients obtained from the LASSO logistic model used within the linear regression (LR) classifier. The MRM yielded an area under the receiver operating characteristic curve (AUC) of 0.754 (0.709-0.799) on D 1 T , 0.836 on D 1 V , and 0.748 D 2 V . The top prognostic clinical factors identified in the analysis were dehydrogenase (LDH), age, and albumin (ALB). The clinical model had an AUC of 0.784 (0.743-0.825) on D 1 T , 0.813 on D 1 V , and 0.688 on D 2 V . Finally, the combined model, MRCM integrating radiomic score, age, LDH and ALB, yielded an AUC of 0.814 (0.774-0.853) on D 1 T , 0.847 on D 1 V , and 0.771 on D 2 V . The MRCM had an overall improvement in the performance of ~5.85% ( D 1 T : p = 0.0031; D 1 V p = 0.0165; D 2 V : p = 0.0369) over MCM. CONCLUSION The novel integrated imaging and clinical model (MRCM) outperformed both models (MRM) and (MCM). Our results across multiple sites suggest that the integrated nomogram could help identify COVID-19 patients with more severe disease phenotype and potentially require mechanical ventilation.
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Affiliation(s)
- Pranjal Vaidya
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Mehdi Alilou
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Amogh Hiremath
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY, United States
| | - Jennifer Furin
- Department of Infectious Diseases, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Keith Armitage
- Department of Infectious Diseases, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Robert Gilkeson
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH, United States
| | - Lei Yuan
- Department of Information Center, Renmin Hospital of Wuhan University, Wuhan, China
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States
| | - Cheng Lu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
| | - Mengyao Ji
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, United States
- Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, OH, United States
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Viswanathan VS, Gupta A, Madabhushi A. Novel Imaging Biomarkers to Assess Oncologic Treatment-Related Changes. Am Soc Clin Oncol Educ Book 2022; 42:1-13. [PMID: 35671432 DOI: 10.1200/edbk_350931] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
Cancer therapeutics cause various treatment-related changes that may impact patient follow-up and disease monitoring. Although atypical responses such as pseudoprogression may be misinterpreted as treatment nonresponse, other changes, such as hyperprogressive disease seen with immunotherapy, must be recognized early for timely management. Radiation necrosis in the brain is a known response to radiotherapy and must be distinguished from local tumor recurrence. Radiotherapy can also cause adverse effects such as pneumonitis and local tissue toxicity. Systemic therapies, like chemotherapy and targeted therapies, are known to cause long-term cardiovascular effects. Thus, there is a need for robust biomarkers to identify, distinguish, and predict cancer treatment-related changes. Radiomics, which refers to the high-throughput extraction of subvisual features from radiologic images, has been widely explored for disease classification, risk stratification, and treatment-response prediction. Lately, there has been much interest in investigating the role of radiomics to assess oncologic treatment-related changes. We review the utility and various applications of radiomics in identifying and distinguishing atypical responses to treatments, as well as in predicting adverse effects. Although artificial intelligence tools show promise, several challenges-including multi-institutional clinical validation, deployment in health care settings, and artificial-intelligence bias-must be addressed for seamless clinical translation of these tools.
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Affiliation(s)
| | - Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center, Cleveland, OH
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH.,Louis Stokes Cleveland VA Medical Center, Cleveland, OH
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Cheng J, Pan Y, Huang W, Huang K, Cui Y, Hong W, Wang L, Ni D, Tan P. Differentiation between immune checkpoint inhibitor-related and radiation pneumonitis in lung cancer by CT radiomics and machine learning. Med Phys 2022; 49:1547-1558. [PMID: 35026041 PMCID: PMC9306809 DOI: 10.1002/mp.15451] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 11/05/2021] [Accepted: 12/28/2021] [Indexed: 11/22/2022] Open
Abstract
Purpose Consolidation immunotherapy after completion of chemoradiotherapy has become the standard of care for unresectable locally advanced non‐small cell lung cancer and can induce potentially severe and life‐threatening adverse events, including both immune checkpoint inhibitor‐related pneumonitis (CIP) and radiation pneumonitis (RP), which are very challenging for radiologists to diagnose. Differentiating between CIP and RP has significant implications for clinical management such as the treatments for pneumonitis and the decision to continue or restart immunotherapy. The purpose of this study is to differentiate between CIP and RP by a CT radiomics approach. Methods We retrospectively collected the CT images and clinical information of patients with pneumonitis who received immune checkpoint inhibitor (ICI) only (n = 28), radiotherapy (RT) only (n = 31), and ICI+RT (n = 14). Three kinds of radiomic features (intensity histogram, gray‐level co‐occurrence matrix [GLCM] based, and bag‐of‐words [BoW] features) were extracted from CT images, which characterize tissue texture at different scales. Classification models, including logistic regression, random forest, and linear SVM, were first developed and tested in patients who received ICI or RT only with 10‐fold cross‐validation and further tested in patients who received ICI+RT using clinicians’ diagnosis as a reference. Results Using 10‐fold cross‐validation, the classification models built on the intensity histogram features, GLCM‐based features, and BoW features achieved an area under curve (AUC) of 0.765, 0.848, and 0.937, respectively. The best model was then applied to the patients receiving combination treatment, achieving an AUC of 0.896. Conclusions This study demonstrates the promising potential of radiomic analysis of CT images for differentiating between CIP and RP in lung cancer, which could be a useful tool to attribute the cause of pneumonitis in patients who receive both ICI and RT.
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Affiliation(s)
- Jun Cheng
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518055, China.,Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, 518055, China.,Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, 518055, China
| | - Yi Pan
- Department of Radiation Oncology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Wei Huang
- Department of Radiation Oncology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Kun Huang
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, 46202, USA.,Regenstrief Institute, Indianapolis, IN, 46202, USA
| | - Yanhai Cui
- Department of Radiology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
| | - Wenhui Hong
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518055, China
| | - Lingling Wang
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518055, China
| | - Dong Ni
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, 518055, China.,Medical Ultrasound Image Computing (MUSIC) Laboratory, Shenzhen University, Shenzhen, 518055, China.,Marshall Laboratory of Biomedical Engineering, Shenzhen University, Shenzhen, 518055, China
| | - Peixin Tan
- Department of Radiation Oncology, Guangdong Provincial People's Hospital, Guangdong Academy of Medical Sciences, Guangzhou, 510080, China
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20
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Önner H, Coskun N, Erol M, Eren Karanis Mİ. Association of 18F-FDG PET/CT textural features with immunohistochemical characteristics in invasive ductal breast cancer. Rev Esp Med Nucl Imagen Mol 2022; 41:11-16. [PMID: 34991831 DOI: 10.1016/j.remnie.2020.12.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Accepted: 10/18/2020] [Indexed: 12/12/2022]
Abstract
OBJECTıVES: This study investigates whether textural features (TFs) extracted from 18F-FDG positron emission tomography/computed tomography (PET/CT) are associated with immunohistochemical characteristics (IHCs) of invasive ductal breast carcinoma (IDBC). MATERIALS AND METHODS The relationship of TFs with IHCs [estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2), Ki-67 proliferation index, and histological grades] from solely excised primary tumors were evaluated for a more accurate assessment. Therefore patients with early-stage IDBC who underwent pre-operative 18F-FDG PET/CT scan for staging were included in this retrospective study. The clinical staging was performed according to the 8th edition of the American Joint Committee on Cancer. Maximum standardized uptake value (SUVmax) and 37TFs of the primary tumor were extracted from 18F-FDG PET/CT. Spearman's rank correlation test was used to evaluate the correlation between TFs and SUVmax. Receiver operating characteristic curves were generated to define the diagnostic performance of each parameter. Among these parameters, those with the highest diagnostic performance were included in the multivariate logistic regression model to identify the independent predictors of histopathological characteristics. RESULTS A total of 124 patients were included. Histogram-uniformity, grey-level co-occurrence matrix (GLCM), GLCM-energy, and GLCM-homogeneity showed a strong negative correlation with SUVmax, while grey-level run-length matrix (GLRLM), GLRLM-SRHGE, grey-level zone length matrix (GLZLM), GLZLM-HGZE, GLRLM-HGRE, GLCM-entropy, GLCM-contrast, histogram-entropy, and GLCM-dissimilarity showed a strong positive correlation. Some of the TFs were independently associated with ER-negativity, PR-negativity, HER-2-positivity, and increased Ki-67 proliferation index (GLCM-contrast, GLZLM-GLNU, histogram-uniformity, and shape-sphericity respectively). While SUVmax had an independent association with high-grade and triple-negativity, GLZLM-SZLGE, a high-order TF that shows the distribution of the short homogeneous zones with low grey-levels, had an independent association with axillary lymph node metastasis. CONCLUSIONS ER-negative, PR-negative, HER-2-positive, triple-negative, high-grade, highly proliferative, and high-stage tumors were found to be more glycolytic and metabolically heterogeneous. These findings suggest that the use of TFs in addition to SUVmax may improve the prognostic value of 18F-FDG PET/CT in IDBC, as certain TFs were independently associated with many IHCs and predicted axillary lymph node involvement.
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21
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Hasani N, Paravastu SS, Farhadi F, Yousefirizi F, Morris MA, Rahmim A, Roschewski M, Summers RM, Saboury B. Artificial Intelligence in Lymphoma PET Imaging:: A Scoping Review (Current Trends and Future Directions). PET Clin 2022; 17:145-174. [PMID: 34809864 PMCID: PMC8735853 DOI: 10.1016/j.cpet.2021.09.006] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Malignant lymphomas are a family of heterogenous disorders caused by clonal proliferation of lymphocytes. 18F-FDG-PET has proven to provide essential information for accurate quantification of disease burden, treatment response evaluation, and prognostication. However, manual delineation of hypermetabolic lesions is often a time-consuming and impractical task. Applications of artificial intelligence (AI) may provide solutions to overcome this challenge. Beyond segmentation and detection of lesions, AI could enhance tumor characterization and heterogeneity quantification, as well as treatment response prediction and recurrence risk stratification. In this scoping review, we have systematically mapped and discussed the current applications of AI (such as detection, classification, segmentation as well as the prediction and prognostication) in lymphoma PET.
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Affiliation(s)
- Navid Hasani
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; University of Queensland Faculty of Medicine, Ochsner Clinical School, New Orleans, LA 70121, USA
| | - Sriram S Paravastu
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA
| | - Faraz Farhadi
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA
| | - Fereshteh Yousefirizi
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada
| | - Michael A Morris
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland-Baltimore Country, Baltimore, MD, USA
| | - Arman Rahmim
- Department of Integrative Oncology, BC Cancer Research Institute, Vancouver, BC, Canada; Department of Radiology, BC Cancer Research Institute, University of British Columbia, 675 West 10th Avenue, Vancouver, British Columbia, V5Z 1L3, Canada
| | - Mark Roschewski
- Lymphoid Malignancies Branch, Center for Cancer Research, National Institutes of Health, Bethesda, MD, USA
| | - Ronald M Summers
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA.
| | - Babak Saboury
- Department of Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 9000 Rockville Pike, Building 10, Room 1C455, Bethesda, MD 20892, USA; Department of Computer Science and Electrical Engineering, University of Maryland-Baltimore Country, Baltimore, MD, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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22
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Scarborough JA, Scott JG. Translation of Precision Medicine Research Into Biomarker-Informed Care in Radiation Oncology. Semin Radiat Oncol 2022; 32:42-53. [PMID: 34861995 PMCID: PMC8667861 DOI: 10.1016/j.semradonc.2021.09.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
The reach of personalized medicine in radiation oncology has expanded greatly over the past few decades as technical precision has improved the delivery of radiation to each patient's unique anatomy. Yet, the consideration of biological heterogeneity between patients has largely not been translated to clinical care. There are innumerable promising advancements in the discovery and validation of biomarkers, which could be used to alter radiation therapy directly or indirectly. Directly, biomarker-informed care may alter treatment dose or identify patients who would benefit most from radiation therapy and who could safely avoid more aggressive care. Indirectly, a variety of biomarkers could assist with choosing the best radiosensitizing chemotherapies. The translation of these advancements into clinical practice will bring radiation oncology even further into the era of precision medicine, treating patients according to their unique anatomical and biological differences.
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Affiliation(s)
- Jessica A Scarborough
- Translational Hematology and Oncology Research Department, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland,OH; Systems Biology and Bioinformatics Program, School of Medicine, Case Western Reserve University, Cleveland, OH
| | - Jacob G Scott
- Translational Hematology and Oncology Research Department, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland,OH; Radiation Oncology Department, Taussig Cancer Institute, Cleveland Clinic Foundation, 10201 Carnegie Ave, Cleveland, OH.
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23
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CT-based radiomics signatures can predict the tumor response of non-small cell lung cancer patients treated with first-line chemotherapy and targeted therapy. Eur Radiol 2021; 32:1538-1547. [PMID: 34564744 DOI: 10.1007/s00330-021-08277-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2020] [Revised: 07/20/2021] [Accepted: 08/08/2021] [Indexed: 12/29/2022]
Abstract
OBJECTIVES The goal of this study was to evaluate the effectiveness of radiomics signatures on pre-treatment computed tomography (CT) images of lungs to predict the tumor responses of non-small cell lung cancer (NSCLC) patients treated with first-line chemotherapy, targeted therapy, or a combination of both. MATERIALS AND METHODS This retrospective study included 322 NSCLC patients who were treated with first-line chemotherapy, targeted therapy, or a combination of both. Of these patients, 224 were randomly assigned to a cohort to help develop the radiomics signature. A total of 1946 radiomics features were obtained from each patient's CT scan. The top-ranked features were selected by the Minimum Redundancy Maximum Relevance (MRMR) feature-ranking method and used to build a lightweight radiomics signature with the Random Forest (RF) classifier. The independent predictive (IP) features (AUC > 0.6, p value < 0.05) were further identified from the top-ranked features and used to build a refined radiomics signature by the RF classifier. Its prediction performance was tested on the validation cohort, which consisted of the remaining 98 patients. RESULTS The initial lightweight radiomics signature constructed from 15 top-ranked features had an AUC of 0.721 (95% CI, 0.619-0.823). After six IP features were further identified and a refined radiomics signature was built, it had an AUC of 0.746 (95% CI, 0.646-0.846). CONCLUSIONS Radiomics signatures based on pre-treatment CT scans can accurately predict tumor response in NSCLC patients after first-line chemotherapy or targeted therapy treatments. Radiomics features could be used as promising prognostic imaging biomarkers in the future. KEY POINTS The radiomics signature extracted from baseline CT images in patients with NSCLC can predict response to first-line chemotherapy, targeted therapy, or both treatments with an AUC = 0.746 (95% CI, 0.646-0.846). The radiomics signature could be used as a new biomarker for quantitative analysis in radiology, which might provide value in decision-making and to define personalized treatments for cancer patients.
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24
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Önner H, Coskun N, Erol M, Karanis MIE. Association of 18F-FDG PET/CT textural features with immunohistochemical characteristics in invasive ductal breast cancer. Rev Esp Med Nucl Imagen Mol 2021; 41:S2253-654X(20)30201-8. [PMID: 34305044 DOI: 10.1016/j.remn.2020.10.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2020] [Revised: 10/15/2020] [Accepted: 10/18/2020] [Indexed: 11/29/2022]
Abstract
OBJECTıVES: This study investigates whether textural features (TFs) extracted from F-18 FDG positron emission tomography/computed tomography (PET/CT) are associated with IHCs of invasive ductal breast carcinoma (IDBC). MATERIALS AND METHODS The relationship of TFs with IHCs [estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (HER-2), Ki-67 proliferation index, and histological grades] from solely excised primary tumors were evaluated for a more accurate assessment. Therefore patients with early-stage IDBC who underwent pre-operative F-18 FDG PET/CT scan for staging were included in this retrospective study. The clinical staging was performed according to the 8th edition of the American Joint Committee on Cancer. Maximum standardized uptake value (SUVmax) and 37 TFs of the primary tumor were extracted from F-18 FDG PET/CT. Spearman's rank correlation test was used to evaluate the correlation between TFs and SUVmax. Receiver operating characteristic curves were generated to define the diagnostic performance of each parameter. Among these parameters, those with the highest diagnostic performance were included in the multivariate logistic regression model to identify the independent predictors of histopathological characteristics. RESULTS A total of 124 patients were included. Histogram-uniformity, GLCM-energy, and GLCM-homogeneity showed a strong negative correlation with SUVmax, while GLRLM-SRHGE, GLZLM-HGZE, GLRLM-HGRE, GLCM-entropy, GLCM-contrast, histogram-entropy, and GLCM-dissimilarity showed a strong positive correlation. Some of the TFs were independently associated with ER-negativity, PR-negativity, HER-2-positivity, and increased Ki-67 proliferation index (GLCM-contrast, GLZLM-GLNU, histogram-uniformity, and shape-sphericity respectively). While SUVmax had an independent association with high-grade and triple-negativity, GLZLM-SZLGE, a high-order TF that shows the distribution of the short homogeneous zones with low grey-levels, had an independent association with axillary lymph node metastasis. CONCLUSIONS ER-negative, PR-negative, HER-2-positive, triple-negative, high-grade, highly proliferative, and high-stage tumors were found to be more glycolytic and metabolically heterogeneous. These findings suggest that the use of TFs in addition to SUVmax may improve the prognostic value of F-18 FDG PET/CT in IDBC, as certain TFs were independently associated with many IHCs and predicted axillary lymph node involvement.
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Affiliation(s)
- H Önner
- Department of Nuclear Medicine, Konya City Hospital, Konya, Turkey.
| | - N Coskun
- Ankara City Hospital, Ankara, Turkey
| | - M Erol
- Department of Nuclear Medicine, Konya City Hospital, Konya, Turkey
| | - M I E Karanis
- Department of Nuclear Medicine, Konya City Hospital, Konya, Turkey
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25
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Ko CC, Yeh LR, Kuo YT, Chen JH. Imaging biomarkers for evaluating tumor response: RECIST and beyond. Biomark Res 2021; 9:52. [PMID: 34215324 PMCID: PMC8252278 DOI: 10.1186/s40364-021-00306-8] [Citation(s) in RCA: 56] [Impact Index Per Article: 18.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Accepted: 06/10/2021] [Indexed: 12/12/2022] Open
Abstract
Response Evaluation Criteria in Solid Tumors (RECIST) is the gold standard for assessment of treatment response in solid tumors. Morphologic change of tumor size evaluated by RECIST is often correlated with survival length and has been considered as a surrogate endpoint of therapeutic efficacy. However, the detection of morphologic change alone may not be sufficient for assessing response to new anti-cancer medication in all solid tumors. During the past fifteen years, several molecular-targeted therapies and immunotherapies have emerged in cancer treatment which work by disrupting signaling pathways and inhibited cell growth. Tumor necrosis or lack of tumor progression is associated with a good therapeutic response even in the absence of tumor shrinkage. Therefore, the use of unmodified RECIST criteria to estimate morphological changes of tumor alone may not be sufficient to estimate tumor response for these new anti-cancer drugs. Several studies have reported the low reliability of RECIST in evaluating treatment response in different tumors such as hepatocellular carcinoma, lung cancer, prostate cancer, brain glioma, bone metastasis, and lymphoma. There is an increased need for new medical imaging biomarkers, considering the changes in tumor viability, metabolic activity, and attenuation, which are related to early tumor response. Promising imaging techniques, beyond RECIST, include dynamic contrast-enhanced computed tomography (CT) or magnetic resonance imaging (MRI), diffusion-weight imaging (DWI), magnetic resonance spectroscopy (MRS), and 18 F-fluorodeoxyglucose (FDG) positron emission tomography (PET). This review outlines the current RECIST with their limitations and the new emerging concepts of imaging biomarkers in oncology.
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Affiliation(s)
- Ching-Chung Ko
- Department of Medical Imaging, Chi Mei Medical Center, Tainan, Taiwan.,Department of Health and Nutrition, Chia Nan University of Pharmacy and Science, Tainan, Taiwan
| | - Lee-Ren Yeh
- Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung, Taiwan
| | - Yu-Ting Kuo
- Department of Medical Imaging, Chi Mei Medical Center, Tainan, Taiwan.,Department of Medical Imaging, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
| | - Jeon-Hor Chen
- Department of Radiology, E-DA Hospital, I-Shou University, Kaohsiung, Taiwan. .,Tu & Yuan Center for Functional Onco-Imaging, Department of Radiological Sciences, University of California, 164 Irvine Hall, Irvine, CA, 92697 - 5020, USA.
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26
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Radiomics in Lung Diseases Imaging: State-of-the-Art for Clinicians. J Pers Med 2021; 11:jpm11070602. [PMID: 34202096 PMCID: PMC8306026 DOI: 10.3390/jpm11070602] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2021] [Revised: 06/16/2021] [Accepted: 06/21/2021] [Indexed: 12/11/2022] Open
Abstract
Artificial intelligence (AI) has increasingly been serving the field of radiology over the last 50 years. As modern medicine is evolving towards precision medicine, offering personalized patient care and treatment, the requirement for robust imaging biomarkers has gradually increased. Radiomics, a specific method generating high-throughput extraction of a tremendous amount of quantitative imaging data using data-characterization algorithms, has shown great potential in individuating imaging biomarkers. Radiomic analysis can be implemented through the following two methods: hand-crafted radiomic features extraction or deep learning algorithm. Its application in lung diseases can be used in clinical decision support systems, regarding its ability to develop descriptive and predictive models in many respiratory pathologies. The aim of this article is to review the recent literature on the topic, and briefly summarize the interest of radiomics in chest Computed Tomography (CT) and its pertinence in the field of pulmonary diseases, from a clinician's perspective.
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27
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Liu Y, Wu M, Zhang Y, Luo Y, He S, Wang Y, Chen F, Liu Y, Yang Q, Li Y, Wei H, Zhang H, Jin C, Lu N, Li W, Wang S, Guo Y, Ye Z. Imaging Biomarkers to Predict and Evaluate the Effectiveness of Immunotherapy in Advanced Non-Small-Cell Lung Cancer. Front Oncol 2021; 11:657615. [PMID: 33816314 PMCID: PMC8017283 DOI: 10.3389/fonc.2021.657615] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Accepted: 02/25/2021] [Indexed: 02/05/2023] Open
Abstract
Objective We aimed to identify imaging biomarkers to assess predictive capacity of radiomics nomogram regarding treatment response status (responder/non-responder) in patients with advanced NSCLC undergoing anti-PD1 immunotherapy. Methods 197 eligible patients with histologically confirmed NSCLC were retrospectively enrolled from nine hospitals. We carried out a radiomics characterization from target lesions (TL) approach and largest target lesion (LL) approach on baseline and first follow-up (TP1) CT imaging data. Delta-radiomics feature was calculated as the relative net change in radiomics feature between baseline and TP1. Minimum Redundancy Maximum Relevance (mRMR) and Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression were applied for feature selection and radiomics signature construction. Results Radiomics signature at baseline did not show significant predictive value regarding response status for LL approach (P = 0.10), nor in terms of TL approach (P = 0.27). A combined Delta-radiomics nomogram incorporating Delta-radiomics signature with clinical factor of distant metastasis for target lesions had satisfactory performance in distinguishing responders from non-responders with AUCs of 0.83 (95% CI: 0.75–0.91) and 0.81 (95% CI: 0.68–0.95) in the training and test sets respectively, which was comparable with that from LL approach (P = 0.92, P = 0.97). Among a subset of those patients with available pretreatment PD-L1 expression status (n = 66), models that incorporating Delta-radiomics features showed superior predictive accuracy than that of PD-L1 expression status alone (P <0.001). Conclusion Early response assessment using combined Delta-radiomics nomograms have potential advantages to identify patients that were more likely to benefit from immunotherapy, and help oncologists modify treatments tailored individually to each patient under therapy.
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Affiliation(s)
- Ying Liu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Minghao Wu
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Yuwei Zhang
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
| | - Yahong Luo
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Shuai He
- Department of Medical Imaging, Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang, China
| | - Yina Wang
- Department of Medical Oncology, 1st Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Feng Chen
- Department of Radiology, 1st Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yulin Liu
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qian Yang
- Department of Radiology, Hubei Cancer Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yanying Li
- Department of Thoracic Oncology, Cancer Center, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Zhang
- Department of Radiology, Tianjin Chest Hospital, Tianjin, China
| | - Chenwang Jin
- Department of Radiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China
| | - Nian Lu
- Department of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in Southern China, Guangzhou, China
| | - Wanhu Li
- Department of Medical Imaging, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, China
| | - Sicong Wang
- Prognostic Diagnosis, GE Healthcare China, Beijing, China
| | - Yan Guo
- Prognostic Diagnosis, GE Healthcare China, Beijing, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, Tianjin's Clinical Research Center for Cancer, Tianjin, China
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28
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Corradini S, Niyazi M, Verellen D, Valentini V, Walsh S, Grosu AL, Lauber K, Giaccia A, Unger K, Debus J, Pieters BR, Guckenberger M, Senan S, Budach W, Rad R, Mayerle J, Belka C. X-change symposium: status and future of modern radiation oncology-from technology to biology. Radiat Oncol 2021; 16:27. [PMID: 33541387 PMCID: PMC7863262 DOI: 10.1186/s13014-021-01758-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 01/28/2021] [Indexed: 02/06/2023] Open
Abstract
Future radiation oncology encompasses a broad spectrum of topics ranging from modern clinical trial design to treatment and imaging technology and biology. In more detail, the application of hybrid MRI devices in modern image-guided radiotherapy; the emerging field of radiomics; the role of molecular imaging using positron emission tomography and its integration into clinical routine; radiation biology with its future perspectives, the role of molecular signatures in prognostic modelling; as well as special treatment modalities such as brachytherapy or proton beam therapy are areas of rapid development. More clinically, radiation oncology will certainly find an important role in the management of oligometastasis. The treatment spectrum will also be widened by the rational integration of modern systemic targeted or immune therapies into multimodal treatment strategies. All these developments will require a concise rethinking of clinical trial design. This article reviews the current status and the potential developments in the field of radiation oncology as discussed by a panel of European and international experts sharing their vision during the "X-Change" symposium, held in July 2019 in Munich (Germany).
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Affiliation(s)
- Stefanie Corradini
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany.
| | - Maximilian Niyazi
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Dirk Verellen
- Department of Radiotherapy, Iridium Network, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Vincenzo Valentini
- Department of Radiation Oncology and Hematology, Fondazione Policlinico Universitario A.Gemelli IRCCS, Università Cattolica S. Cuore, Rome, Italy
| | | | - Anca-L Grosu
- Department of Radiation Oncology, Medical Center, Medical Faculty, University of Freiburg, Freiburg, Germany
- German Cancer Consortium (DKTK), Partner Site Freiburg, Freiburg, Germany
| | - Kirsten Lauber
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
| | - Amato Giaccia
- Division of Radiation and Cancer Biology, Department of Radiation Oncology, Stanford University, Stanford, USA
| | - Kristian Unger
- Integrative Biology Group, Helmholtz Zentrum Munich, Munich, Germany
| | - Jürgen Debus
- Department of Radiation Oncology, Heidelberg University Hospital, Heidelberg, Germany
| | - Bradley R Pieters
- Department of Radiation Oncology, Amsterdam University Medical Centers, Location Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital of Zurich, University of Zurich, Zurich, Switzerland
| | - Suresh Senan
- Department of Radiation Oncology, Amsterdam University Medical Centers, Location VUmc, Amsterdam, The Netherlands
| | - Wilfried Budach
- Department of Radiation Oncology, Medical Faculty, Heinrich Heine University, Düsseldorf, Germany
| | - Roland Rad
- Center for Translational Cancer Research (TranslaTUM), TU Munich, Munich, Germany
| | - Julia Mayerle
- Department of Internal Medicine II, University Hospital, LMU, Munich, Germany
| | - Claus Belka
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistr. 15, 81377, Munich, Germany
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Chetan MR, Gleeson FV. Radiomics in predicting treatment response in non-small-cell lung cancer: current status, challenges and future perspectives. Eur Radiol 2021; 31:1049-1058. [PMID: 32809167 PMCID: PMC7813733 DOI: 10.1007/s00330-020-07141-9] [Citation(s) in RCA: 126] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 06/03/2020] [Accepted: 08/03/2020] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Radiomics is the extraction of quantitative data from medical imaging, which has the potential to characterise tumour phenotype. The radiomics approach has the capacity to construct predictive models for treatment response, essential for the pursuit of personalised medicine. In this literature review, we summarise the current status and evaluate the scientific and reporting quality of radiomics research in the prediction of treatment response in non-small-cell lung cancer (NSCLC). METHODS A comprehensive literature search was conducted using the PubMed database. A total of 178 articles were screened for eligibility and 14 peer-reviewed articles were included. The radiomics quality score (RQS), a radiomics-specific quality metric emulating the TRIPOD guidelines, was used to assess scientific and reporting quality. RESULTS Included studies reported several predictive markers including first-, second- and high-order features, such as kurtosis, grey-level uniformity and wavelet HLL mean respectively, as well as PET-based metabolic parameters. Quality assessment demonstrated a low median score of + 2.5 (range - 5 to + 9), mainly reflecting a lack of reproducibility and clinical evaluation. There was extensive heterogeneity between studies due to differences in patient population, cancer stage, treatment modality, follow-up timescales and radiomics workflow methodology. CONCLUSIONS Radiomics research has not yet been translated into clinical use. Efforts towards standardisation and collaboration are needed to identify reproducible radiomic predictors of response. Promising radiomic models must be externally validated and their impact evaluated within the clinical pathway before they can be implemented as a clinical decision-making tool to facilitate personalised treatment for patients with NSCLC. KEY POINTS • The included studies reported several promising radiomic markers of treatment response in lung cancer; however, there was a lack of reproducibility between studies. • Quality assessment using the radiomics quality score (RQS) demonstrated a low median total score of + 2.5 (range - 5 to + 9). • Future radiomics research should focus on implementation of standardised radiomics features and software, together with external validation in a prospective setting.
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Affiliation(s)
- Madhurima R Chetan
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Old Road, Headington, Oxford, OX3 7LE, UK.
- Nuffield Department of Surgical Sciences, John Radcliffe Hospital, University of Oxford, Room 6607, Level 6, Oxford, OX3 9DU, UK.
| | - Fergus V Gleeson
- Department of Radiology, Churchill Hospital, Oxford University Hospitals NHS Foundation Trust, Old Road, Headington, Oxford, OX3 7LE, UK
- Department of Oncology, Old Road Campus Research Building, University of Oxford, Roosevelt Drive, Oxford, OX3 7DQ, UK
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30
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Gupta A, Kikano EG, Bera K, Baruah D, Saboo SS, Lennartz S, Hokamp NG, Gholamrezanezhad A, Gilkeson RC, Laukamp KR. Dual energy imaging in cardiothoracic pathologies: A primer for radiologists and clinicians. Eur J Radiol Open 2021; 8:100324. [PMID: 33532519 PMCID: PMC7822965 DOI: 10.1016/j.ejro.2021.100324] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 01/05/2021] [Accepted: 01/06/2021] [Indexed: 12/12/2022] Open
Abstract
Recent advances in dual-energy imaging techniques, dual-energy subtraction radiography (DESR) and dual-energy CT (DECT), offer new and useful additional information to conventional imaging, thus improving assessment of cardiothoracic abnormalities. DESR facilitates detection and characterization of pulmonary nodules. Other advantages of DESR include better depiction of pleural, lung parenchymal, airway and chest wall abnormalities, detection of foreign bodies and indwelling devices, improved visualization of cardiac and coronary artery calcifications helping in risk stratification of coronary artery disease, and diagnosing conditions like constrictive pericarditis and valvular stenosis. Commercially available DECT approaches are classified into emission based (dual rotation/spin, dual source, rapid kilovoltage switching and split beam) and detector-based (dual layer) systems. DECT provide several specialized image reconstructions. Virtual non-contrast images (VNC) allow for radiation dose reduction by obviating need for true non contrast images, low energy virtual mono-energetic images (VMI) boost contrast enhancement and help in salvaging otherwise non-diagnostic vascular studies, high energy VMI reduce beam hardening artifacts from metallic hardware or dense contrast material, and iodine density images allow quantitative and qualitative assessment of enhancement/iodine distribution. The large amount of data generated by DECT can affect interpreting physician efficiency but also limit clinical adoption of the technology. Optimization of the existing workflow and streamlining the integration between post-processing software and picture archiving and communication system (PACS) is therefore warranted.
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Key Words
- AI, artificial intelligence
- BT, blalock-taussig
- CAD, computer-aided detection
- CR, computed radiography
- DECT, dual-energy computed tomography
- DESR, dual-energy subtraction radiography
- Dual energy CT
- Dual energy radiography
- NIH, national institute of health
- NPV, negative predictive value
- PACS, picture archiving and communication system
- PCD, photon-counting detector
- PET, positron emission tomography
- PPV, positive predictive value
- Photoelectric effect
- SNR, signal to noise ratio
- SPECT, single photon emission computed tomography
- SVC, superior vena cava
- TAVI, transcatheter aortic valve implantation
- TNC, true non contrast
- VMI, virtual mono-energetic images
- VNC, virtual non-contrast images
- eGFR, estimated glomerular filtration rate
- kV, kilo volt
- keV, kilo electron volt
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Affiliation(s)
- Amit Gupta
- Department of Radiology, University Hospitals Cleveland Medical Center/Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Elias G Kikano
- Department of Radiology, University Hospitals Cleveland Medical Center/Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Dhiraj Baruah
- Department of Radiology, Medical University of South Carolina, Charleston, SC, USA
| | - Sachin S Saboo
- Department of Radiology, University Of Texas Health Science Center, San Antonio, TX, USA
| | - Simon Lennartz
- Institute for Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany
| | - Nils Große Hokamp
- Institute for Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany
| | - Ali Gholamrezanezhad
- Department of Radiology, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Robert C Gilkeson
- Department of Radiology, University Hospitals Cleveland Medical Center/Case Western Reserve University, 11100 Euclid Ave, Cleveland, OH, 44106, USA
| | - Kai R Laukamp
- Institute for Diagnostic and Interventional Radiology, University Hospital Cologne, Cologne, Germany
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Moldovanu CG, Boca B, Lebovici A, Tamas-Szora A, Feier DS, Crisan N, Andras I, Buruian MM. Preoperative Predicting the WHO/ISUP Nuclear Grade of Clear Cell Renal Cell Carcinoma by Computed Tomography-Based Radiomics Features. J Pers Med 2020; 11:jpm11010008. [PMID: 33374569 PMCID: PMC7822466 DOI: 10.3390/jpm11010008] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2020] [Revised: 12/19/2020] [Accepted: 12/21/2020] [Indexed: 12/11/2022] Open
Abstract
Nuclear grade is important for treatment selection and prognosis in patients with clear cell renal cell carcinoma (ccRCC). This study aimed to determine the ability of preoperative four-phase multiphasic multidetector computed tomography (MDCT)-based radiomics features to predict the WHO/ISUP nuclear grade. In all 102 patients with histologically confirmed ccRCC, the training set (n = 62) and validation set (n = 40) were randomly assigned. In both datasets, patients were categorized according to the WHO/ISUP grading system into low-grade ccRCC (grades 1 and 2) and high-grade ccRCC (grades 3 and 4). The feature selection process consisted of three steps, including least absolute shrinkage and selection operator (LASSO) regression analysis, and the radiomics scores were developed using 48 radiomics features (10 in the unenhanced phase, 17 in the corticomedullary (CM) phase, 14 in the nephrographic (NP) phase, and 7 in the excretory phase). The radiomics score (Rad-Score) derived from the CM phase achieved the best predictive ability, with a sensitivity, specificity, and an area under the curve (AUC) of 90.91%, 95.00%, and 0.97 in the training set. In the validation set, the Rad-Score derived from the NP phase achieved the best predictive ability, with a sensitivity, specificity, and an AUC of 72.73%, 85.30%, and 0.84. We constructed a complex model, adding the radiomics score for each of the phases to the clinicoradiological characteristics, and found significantly better performance in the discrimination of the nuclear grades of ccRCCs in all MDCT phases. The highest AUC of 0.99 (95% CI, 0.92-1.00, p < 0.0001) was demonstrated for the CM phase. Our results showed that the MDCT radiomics features may play a role as potential imaging biomarkers to preoperatively predict the WHO/ISUP grade of ccRCCs.
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Affiliation(s)
- Claudia-Gabriela Moldovanu
- Department of Radiology and Medical Imaging, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania; (C.-G.M.); (M.M.B.)
- Department of Radiology, Emergency Clinical County Hospital of Cluj-Napoca, 400006 Cluj-Napoca, Romania;
| | - Bianca Boca
- Department of Radiology and Medical Imaging, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania; (C.-G.M.); (M.M.B.)
- Department of Radiology, Emergency Clinical County Hospital of Cluj-Napoca, 400006 Cluj-Napoca, Romania;
- Correspondence: (B.B.); (A.L.)
| | - Andrei Lebovici
- Department of Radiology, Emergency Clinical County Hospital of Cluj-Napoca, 400006 Cluj-Napoca, Romania;
- Department of Radiology, Faculty of Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
- Correspondence: (B.B.); (A.L.)
| | - Attila Tamas-Szora
- Department of Radiology, Clinical Municipal Hospital, 400139 Cluj-Napoca, Romania;
| | - Diana Sorina Feier
- Department of Radiology, Emergency Clinical County Hospital of Cluj-Napoca, 400006 Cluj-Napoca, Romania;
- Department of Radiology, Faculty of Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania
| | - Nicolae Crisan
- Department of Urology, Faculty of Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; (N.C.); (I.A.)
| | - Iulia Andras
- Department of Urology, Faculty of Medicine, Iuliu Hațieganu University of Medicine and Pharmacy, 400012 Cluj-Napoca, Romania; (N.C.); (I.A.)
| | - Mircea Marian Buruian
- Department of Radiology and Medical Imaging, Faculty of Medicine, George Emil Palade University of Medicine, Pharmacy, Science and Technology of Târgu Mureș, 540139 Târgu Mureș, Romania; (C.-G.M.); (M.M.B.)
- Department of Radiology, Emergency Clinical County Hospital Târgu Mureș, 540136 Târgu Mureș, Romania
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Giannini V, Rosati S, Defeudis A, Balestra G, Vassallo L, Cappello G, Mazzetti S, De Mattia C, Rizzetto F, Torresin A, Sartore-Bianchi A, Siena S, Vanzulli A, Leone F, Zagonel V, Marsoni S, Regge D. Radiomics predicts response of individual HER2-amplified colorectal cancer liver metastases in patients treated with HER2-targeted therapy. Int J Cancer 2020; 147:3215-3223. [PMID: 32875550 DOI: 10.1002/ijc.33271] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2020] [Revised: 08/06/2020] [Accepted: 08/12/2020] [Indexed: 12/20/2022]
Abstract
The aim of our study was to develop and validate a machine learning algorithm to predict response of individual HER2-amplified colorectal cancer liver metastases (lmCRC) undergoing dual HER2-targeted therapy. Twenty-four radiomics features were extracted after 3D manual segmentation of 141 lmCRC on pretreatment portal CT scans of a cohort including 38 HER2-amplified patients; feature selection was then performed using genetic algorithms. lmCRC were classified as nonresponders (R-), if their largest diameter increased more than 10% at a CT scan performed after 3 months of treatment, responders (R+) otherwise. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values in correctly classifying individual lesion and overall patient response were assessed on a training dataset and then validated on a second dataset using a Gaussian naïve Bayesian classifier. Per-lesion sensitivity, specificity, NPV and PPV were 89%, 85%, 93%, 78% and 90%, 42%, 73%, 71% respectively in the testing and validation datasets. Per-patient sensitivity and specificity were 92% and 86%. Heterogeneous response was observed in 9 of 38 patients (24%). Five of nine patients were carriers of nonresponder lesions correctly classified as such by our radiomics signature, including four of seven harboring only one nonresponder lesion. The developed method has been proven effective in predicting behavior of individual metastases to targeted treatment in a cohort of HER2 amplified patients. The model accurately detects responder lesions and identifies nonresponder lesions in patients with heterogeneous response, potentially paving the way to multimodal treatment in selected patients. Further validation will be needed to confirm our findings.
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Affiliation(s)
- Valentina Giannini
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Samanta Rosati
- Department of Electronics and Telecommunications, Polytechnic of Turin, Turin, Italy
| | - Arianna Defeudis
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Gabriella Balestra
- Department of Electronics and Telecommunications, Polytechnic of Turin, Turin, Italy
| | | | - Giovanni Cappello
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - Simone Mazzetti
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Surgical Sciences, University of Turin, Turin, Italy
| | - Cristina De Mattia
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Francesco Rizzetto
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Alberto Torresin
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
- Department of Physics, Università degli Studi di Milano, Milan, Italy
| | - Andrea Sartore-Bianchi
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
- Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Salvatore Siena
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
- Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Angelo Vanzulli
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Milan, Italy
| | - Francesco Leone
- Medical Oncology, ASL Biella, Biella, Italy
- Department of Oncology, University of Turin, Turin, Italy
| | - Vittorina Zagonel
- Medical Oncology Unit 1, Istituto Oncologico Veneto-IRCCS, Padova, Italy
| | - Silvia Marsoni
- Precision Oncology, IFOM-The FIRC Institute of Molecular Oncology, Milan, Italy
| | - Daniele Regge
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
- Department of Surgical Sciences, University of Turin, Turin, Italy
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33
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Vaidya P, Bera K, Patil PD, Gupta A, Jain P, Alilou M, Khorrami M, Velcheti V, Madabhushi A. Novel, non-invasive imaging approach to identify patients with advanced non-small cell lung cancer at risk of hyperprogressive disease with immune checkpoint blockade. J Immunother Cancer 2020; 8:jitc-2020-001343. [PMID: 33051342 PMCID: PMC7555103 DOI: 10.1136/jitc-2020-001343] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/10/2020] [Indexed: 12/11/2022] Open
Abstract
Purpose Hyperprogression is an atypical response pattern to immune checkpoint inhibition that has been described within non-small cell lung cancer (NSCLC). The paradoxical acceleration of tumor growth after immunotherapy has been associated with significantly shortened survival, and currently, there are no clinically validated biomarkers to identify patients at risk of hyperprogression. Experimental design A total of 109 patients with advanced NSCLC who underwent monotherapy with Programmed cell death protein-1 (PD1)/Programmed death-ligand-1 (PD-L1) inhibitors were included in the study. Using RECIST measurements, we divided the patients into responders (n=50) (complete/partial response or stable disease) and non-responders (n=59) (progressive disease). Tumor growth kinetics were used to further identify hyperprogressors (HPs, n=19) among non-responders. Patients were randomized into a training set (D1=30) and a test set (D2=79) with the essential caveat that HPs were evenly distributed among the two sets. A total of 198 radiomic textural patterns from within and around the target nodules and features relating to tortuosity of the nodule associated vasculature were extracted from the pretreatment CT scans. Results The random forest classifier using the top features associated with hyperprogression was able to distinguish between HP and other radiographical response patterns with an area under receiver operating curve of 0.85±0.06 in the training set (D1=30) and 0.96 in the validation set (D2=79). These features included one peritumoral texture feature from 5 to 10 mm outside the tumor and two nodule vessel-related tortuosity features. Kaplan-Meier survival curves showed a clear stratification between classifier predicted HPs versus non-HPs for overall survival (D2: HR=2.66, 95% CI 1.27 to 5.55; p=0.009). Conclusions Our study suggests that image-based radiomics markers extracted from baseline CTs of advanced NSCLC treated with PD-1/PD-L1 inhibitors may help identify patients at risk of hyperprogressions.
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Affiliation(s)
- Pranjal Vaidya
- Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Kaustav Bera
- Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | - Pradnya D Patil
- Hematology and Medical Oncology, Cleveland Clinic, Cleveland, Ohio, USA
| | - Amit Gupta
- Department of Radiology, University Hospitals of Cleveland, Cleveland, Ohio, USA
| | - Prantesh Jain
- Department of Radiology, University Hospitals of Cleveland, Cleveland, Ohio, USA
| | - Mehdi Alilou
- Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA
| | | | | | - Anant Madabhushi
- Biomedical Engineering, Case Western Reserve University, Cleveland, Ohio, USA .,Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio, USA 44106, Cleveland, Ohio, USA
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Giannini V, Defeudis A, Rosati S, Cappello G, Mazzetti S, Panic J, Regge D, Balestra G. An innovative radiomics approach to predict response to chemotherapy of liver metastases based on CT images. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:1339-1342. [PMID: 33018236 DOI: 10.1109/embc44109.2020.9176627] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Liver metastases (mts) from colorectal cancer (CRC) can have different responses to chemotherapy in the same patient. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts. 22 radiomic features (RF) were computed on pretreatment portal CT scans following a manual segmentation of mts. RFs were extracted from 7x7 Region of Interests (ROIs) that moved across the image by step of 2 pixels. Liver mts were classified as non-responder (R-) if their largest diameter increased more than 3 mm after 3 months of treatment and responder (R+), otherwise. Features selection (FS) was performed by a genetic algorithm and classification by a Support Vector Machine (SVM) classifier. Sensitivity, specificity, negative (NPV) and positive (PPV) predictive values were evaluated for all lesions in the training and validation sets, separately. On the training set, we obtained sensitivity of 86%, specificity of 67%, PPV of 89% and NPV of 61%, while, on the validation set, we reached a sensitivity of 73%, specificity of 47%, PPV of 64% and NPV of 57%. Specificity was biased by the low number of R- lesions on the validation set. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance- to personalize treatment of patients with metastastic colorectal cancer, based on the likelihood of response to chemotherapy of each liver metastasis.
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Porcu M, Solinas C, Mannelli L, Micheletti G, Lambertini M, Willard-Gallo K, Neri E, Flanders AE, Saba L. Radiomics and "radi-…omics" in cancer immunotherapy: a guide for clinicians. Crit Rev Oncol Hematol 2020; 154:103068. [PMID: 32805498 DOI: 10.1016/j.critrevonc.2020.103068] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 07/13/2020] [Accepted: 07/23/2020] [Indexed: 02/06/2023] Open
Abstract
In recent years the concept of precision medicine has become a popular topic particularly in medical oncology. Besides the identification of new molecular prognostic and predictive biomarkers and the development of new targeted and immunotherapeutic drugs, imaging has started to play a central role in this new era. Terms such as "radiomics", "radiogenomics" or "radi…-omics" are becoming increasingly common in the literature and soon they will represent an integral part of clinical practice. The use of artificial intelligence, imaging and "-omics" data can be used to develop models able to predict, for example, the features of the tumor immune microenvironment through imaging, and to monitor the therapeutic response beyond the standard radiological criteria. The aims of this narrative review are to provide a simplified guide for clinicians to these concepts, and to summarize the existing evidence on radiomics and "radi…-omics" in cancer immunotherapy.
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Affiliation(s)
- Michele Porcu
- Department of Radiology, AOU of Cagliari, University of Cagliari, Italy.
| | - Cinzia Solinas
- Medical Oncology, Azienda Tutela Salute Sardegna, Hospital Antonio Segni, Ozieri, SS, Italy
| | | | - Giulio Micheletti
- Department of Radiology, AOU of Cagliari, University of Cagliari, Italy
| | - Matteo Lambertini
- Department of Medical Oncology, U.O.C. Clinica di Oncologia Medica, IRCCS Ospedale Policlinico San Martino, Genova, Italy; Department of Internal Medicine and Medical Specialties (DiMI), School of Medicine, University of Genova, Genova, Italy
| | | | | | - Adam E Flanders
- Department of Radiology, Division of Neuroradiology, Thomas Jefferson University Hospital, Philadelphia, PA, USA
| | - Luca Saba
- Department of Radiology, AOU of Cagliari, University of Cagliari, Italy
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A Review of the Mechanisms and Clinical Implications of Precision Cancer Therapy-Related Toxicity: A Primer for the Radiologist. AJR Am J Roentgenol 2020; 215:770-780. [PMID: 32755160 DOI: 10.2214/ajr.20.22837] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
OBJECTIVE. The purpose of this review is to elucidate the mechanisms, types, and clinical significance of molecular targeted therapy (MTT) and immune checkpoint inhibitors (ICIs) and their related toxicity, emphasizing the radiologic manifestations. CONCLUSION. The related toxicities of MTT and ICIs can have acute, recurrent, chronic, and delayed presentations. These toxicities may serve as markers of response and survival. By understanding the clinical significance of drug toxicities, radiologists can play an important role in personalized cancer therapy.
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37
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Homayounieh F, Ebrahimian S, Babaei R, Mobin HK, Zhang E, Bizzo BC, Mohseni I, Digumarthy SR, Kalra MK. CT Radiomics, Radiologists, and Clinical Information in Predicting Outcome of Patients with COVID-19 Pneumonia. Radiol Cardiothorac Imaging 2020; 2:e200322. [PMID: 33778612 PMCID: PMC7380121 DOI: 10.1148/ryct.2020200322] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Revised: 06/29/2020] [Accepted: 07/10/2020] [Indexed: 01/08/2023]
Abstract
Purpose To compare prediction of disease outcome, severity, and patient triage in coronavirus disease 2019 (COVID-19) pneumonia with whole lung radiomics, radiologists' interpretation, and clinical variables. Materials and Methods This institutional review board-approved retrospective study included 315 adult patients (mean age, 56 years [range, 21-100 years], 190 men, 125 women) with COVID-19 pneumonia who underwent noncontrast chest CT. All patients (inpatients, n = 210; outpatients, n = 105) were followed-up for at least 2 weeks to record disease outcome. Clinical variables, such as presenting symptoms, laboratory data, peripheral oxygen saturation, and comorbid diseases, were recorded. Two radiologists assessed each CT in consensus and graded the extent of pulmonary involvement (by percentage of involved lobe) and type of opacities within each lobe. Radiomics were obtained for the entire lung, and multiple logistic regression analyses with areas under the curve (AUCs) as outputs were performed. Results Most patients (276/315, 88%) recovered from COVID-19 pneumonia; 36/315 patients (11%) died, and 3/315 patients (1%) remained admitted in the hospital. Radiomics differentiated chest CT in outpatient versus inpatient with an AUC of 0.84 (P < .005), while radiologists' interpretations of disease extent and opacity type had an AUC of 0.69 (P < .0001). Whole lung radiomics were superior to the radiologists' interpretation for predicting patient outcome in terms of intensive care unit (ICU) admission (AUC: 0.75 vs 0.68) and death (AUC: 0.81 vs 0.68) (P < .002). The addition of clinical variables to radiomics improved the AUC to 0.84 for predicting ICU admission. Conclusion Radiomics from noncontrast chest CT were superior to radiologists' assessment of extent and type of pulmonary opacities in predicting COVID-19 pneumonia outcome, disease severity, and patient triage.© RSNA, 2020.
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Affiliation(s)
- Fatemeh Homayounieh
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
| | - Shadi Ebrahimian
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
| | - Rosa Babaei
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
| | - Hadi Karimi Mobin
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
| | - Eric Zhang
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
| | - Bernardo Canedo Bizzo
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
| | - Iman Mohseni
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
| | - Subba R Digumarthy
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
| | - Mannudeep K Kalra
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, 75 Blossom Ct, Room 248, Boston, MA 02114 (F.H., S.E., E.Z., B.C.B., S.R.D., M.K.K.); and Department of Radiology, Firoozgar Hospital, Iran University of Medical Sciences, Tehran, Iran (R.B., H.K.M., I.M.)
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Kos Z, Roblin E, Kim RS, Michiels S, Gallas BD, Chen W, van de Vijver KK, Goel S, Adams S, Demaria S, Viale G, Nielsen TO, Badve SS, Symmans WF, Sotiriou C, Rimm DL, Hewitt S, Denkert C, Loibl S, Luen SJ, Bartlett JMS, Savas P, Pruneri G, Dillon DA, Cheang MCU, Tutt A, Hall JA, Kok M, Horlings HM, Madabhushi A, van der Laak J, Ciompi F, Laenkholm AV, Bellolio E, Gruosso T, Fox SB, Araya JC, Floris G, Hudeček J, Voorwerk L, Beck AH, Kerner J, Larsimont D, Declercq S, Van den Eynden G, Pusztai L, Ehinger A, Yang W, AbdulJabbar K, Yuan Y, Singh R, Hiley C, Bakir MA, Lazar AJ, Naber S, Wienert S, Castillo M, Curigliano G, Dieci MV, André F, Swanton C, Reis-Filho J, Sparano J, Balslev E, Chen IC, Stovgaard EIS, Pogue-Geile K, Blenman KRM, Penault-Llorca F, Schnitt S, Lakhani SR, Vincent-Salomon A, Rojo F, Braybrooke JP, Hanna MG, Soler-Monsó MT, Bethmann D, Castaneda CA, Willard-Gallo K, Sharma A, Lien HC, Fineberg S, Thagaard J, Comerma L, Gonzalez-Ericsson P, Brogi E, Loi S, Saltz J, Klaushen F, Cooper L, Amgad M, Moore DA, Salgado R. Pitfalls in assessing stromal tumor infiltrating lymphocytes (sTILs) in breast cancer. NPJ Breast Cancer 2020; 6:17. [PMID: 32411819 PMCID: PMC7217863 DOI: 10.1038/s41523-020-0156-0] [Citation(s) in RCA: 89] [Impact Index Per Article: 22.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2019] [Accepted: 03/02/2020] [Indexed: 02/08/2023] Open
Abstract
Stromal tumor-infiltrating lymphocytes (sTILs) are important prognostic and predictive biomarkers in triple-negative (TNBC) and HER2-positive breast cancer. Incorporating sTILs into clinical practice necessitates reproducible assessment. Previously developed standardized scoring guidelines have been widely embraced by the clinical and research communities. We evaluated sources of variability in sTIL assessment by pathologists in three previous sTIL ring studies. We identify common challenges and evaluate impact of discrepancies on outcome estimates in early TNBC using a newly-developed prognostic tool. Discordant sTIL assessment is driven by heterogeneity in lymphocyte distribution. Additional factors include: technical slide-related issues; scoring outside the tumor boundary; tumors with minimal assessable stroma; including lymphocytes associated with other structures; and including other inflammatory cells. Small variations in sTIL assessment modestly alter risk estimation in early TNBC but have the potential to affect treatment selection if cutpoints are employed. Scoring and averaging multiple areas, as well as use of reference images, improve consistency of sTIL evaluation. Moreover, to assist in avoiding the pitfalls identified in this analysis, we developed an educational resource available at www.tilsinbreastcancer.org/pitfalls.
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Affiliation(s)
- Zuzana Kos
- Department of Pathology, BC Cancer - Vancouver, Vancouver, BC Canada
| | - Elvire Roblin
- Department of Biostatistics and Epidemiology, Gustave Roussy, University Paris-Saclay, Villejuif, France
- Oncostat U1018, Inserm, University Paris-Saclay, labeled Ligue Contre le Cancer, Villejuif, France
| | - Rim S. Kim
- National Surgical Adjuvant Breast and Bowel Project (NSABP)/NRG Oncology, Pittsburgh, PA USA
| | - Stefan Michiels
- Department of Biostatistics and Epidemiology, Gustave Roussy, University Paris-Saclay, Villejuif, France
- Oncostat U1018, Inserm, University Paris-Saclay, labeled Ligue Contre le Cancer, Villejuif, France
| | - Brandon D. Gallas
- Division of Imaging, Diagnostics, and Software Reliability (DIDSR); Office of Science and Engineering Laboratories (OSEL); Center for Devices and Radiological Health (CDRH), US Food and Drug Administration (US FDA), Silver Spring, MD USA
| | - Weijie Chen
- Division of Imaging, Diagnostics, and Software Reliability (DIDSR); Office of Science and Engineering Laboratories (OSEL); Center for Devices and Radiological Health (CDRH), US Food and Drug Administration (US FDA), Silver Spring, MD USA
| | - Koen K. van de Vijver
- Department of Pathology, University Hospital Antwerp, Antwerp, Belgium
- Department of Pathology, Ghent University Hospital, Cancer Research Institute Ghent (CRIG), Ghent, Belgium
| | - Shom Goel
- The Sir Peter MacCallum Cancer Centre, Melbourne, VIC Australia
- Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria Australia
| | - Sylvia Adams
- Perlmutter Cancer Center, New York University Medical School, New York, NY USA
| | - Sandra Demaria
- Departments of Radiation Oncology and Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY USA
| | - Giuseppe Viale
- Department of Pathology, Istituto Europeo di Oncologia, University of Milan, Milan, Italy
| | - Torsten O. Nielsen
- Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada
| | - Sunil S. Badve
- Department of Pathology and Laboratory Medicine, Indiana University, Indianapolis, USA
| | - W. Fraser Symmans
- Department of Pathology, The University of Texas M.D. Anderson Cancer Center, Houston, TX USA
| | - Christos Sotiriou
- Department of Medical Oncology, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - David L. Rimm
- Department of Pathology, Yale School of Medicine, New Haven, CT USA
| | - Stephen Hewitt
- Laboratory of Pathology, National Cancer Institute, NIH, Bethesda, MD USA
| | - Carsten Denkert
- Institute of Pathology, Universitätsklinikum Gießen und Marburg GmbH, Standort Marburg and Philipps-Universität Marburg, Marburg, Germany
| | | | - Stephen J. Luen
- Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria Australia
- Division of Research and Cancer Medicine, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC Australia
| | - John M. S. Bartlett
- Ontario Institute for Cancer Research, Toronto, ON Canada
- University of Edinburgh Cancer Research Centre, Edinburgh, UK
| | - Peter Savas
- Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria Australia
- Division of Research and Cancer Medicine, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC Australia
| | - Giancarlo Pruneri
- Department of Pathology, IRCCS Fondazione Instituto Nazionale Tumori and University of Milan, School of Medicine, Milan, Italy
| | - Deborah A. Dillon
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA USA
- Department of Pathology, Dana Farber Cancer Institute, Boston, MA USA
| | - Maggie Chon U. Cheang
- Institute of Cancer Research Clinical Trials and Statistics Unit, The Institute of Cancer Research, Surrey, UK
| | - Andrew Tutt
- Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK
| | | | - Marleen Kok
- Department of Medical Oncology and Division of Tumor Biology & Immunology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Hugo M. Horlings
- Department of Pathology, University Hospital Antwerp, Antwerp, Belgium
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH USA
- Louis Stokes Cleveland Veterans Affairs Medical Center, Cleveland, OH USA
| | - Jeroen van der Laak
- Computational Pathology Group, Department of Pathology, Radboud University Medical Center, Nijmegen, Netherlands
| | - Francesco Ciompi
- Computational Pathology Group, Department of Pathology, Radboud University Medical Center, Nijmegen, Netherlands
| | | | - Enrique Bellolio
- Departamento de Anatomía Patológica, Universidad de La Frontera, Temuco, Chile
| | | | - Stephen B. Fox
- The Sir Peter MacCallum Cancer Centre, Melbourne, VIC Australia
- Department of Pathology, Peter MacCallum Cancer Centre Department of Pathology, Melbourne, VIC Australia
| | | | - Giuseppe Floris
- KU Leuven- Univerisity of Leuven, Department of Imaging and Pathology, Laboratory of Translational Cell & Tissue Research and KU Leuven- University Hospitals Leuven, Department of Pathology, Leuven, Belgium
| | - Jan Hudeček
- Department of Research IT, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Leonie Voorwerk
- Division of Tumor Biology & Immunology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | | | - Denis Larsimont
- Department of Pathology, Jules Bordet Institute, Brussels, Belgium
| | | | | | - Lajos Pusztai
- Department of Internal Medicine, Section of Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT USA
| | - Anna Ehinger
- Department of Clinical Genetics and Pathology, Skåne University Hospital, Lund University, Lund, Sweden
| | - Wentao Yang
- Department of Pathology, Fudan University Shanghai Cancer Centre, Shanghai, China
| | - Khalid AbdulJabbar
- Centre for Evolution and Cancer; Division of Molecular Pathology, The Institute of Cancer Research, London, UK
| | - Yinyin Yuan
- Centre for Evolution and Cancer; Division of Molecular Pathology, The Institute of Cancer Research, London, UK
| | - Rajendra Singh
- Icahn School of Medicine at Mt. Sinai, New York, NY 10029 USA
| | - Crispin Hiley
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, University College London, London, UK
| | - Maise al Bakir
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, University College London, London, UK
| | - Alexander J. Lazar
- Departments of Pathology, Genomic Medicine, Dermatology, and Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, TX USA
| | - Stephen Naber
- Department of Pathology and Laboratory Medicine, Tufts Medical Center, Boston, USA
| | - Stephan Wienert
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Institute of Pathology, Charitéplatz 1, 10117 Berlin, Germany
| | - Miluska Castillo
- Department of Medical Oncology and Research, Instituto Nacional de Enfermedades Neoplasicas, Lima, 15038 Peru
| | | | - Maria-Vittoria Dieci
- Medical Oncology 2, Istituto Oncologico Veneto IOV - IRCCS, Padova, Italy
- Department of Surgery, Oncology and Gastroenterology, University of Padova, Padova, Italy
| | - Fabrice André
- Department of Medical Oncology, Institut Gustave Roussy, Villejuif, France
| | - Charles Swanton
- Cancer Research UK Lung Cancer Centre of Excellence, University College London Cancer Institute, University College London, London, UK
- Francis Crick Institute, Midland Road, London, UK
| | - Jorge Reis-Filho
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY USA
- Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Joseph Sparano
- Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY USA
| | - Eva Balslev
- Department of Pathology, Herlev and Gentofte Hospital, Herlev, Denmark
| | - I-Chun Chen
- Department of Oncology, National Taiwan University Cancer Center, Taipei, Taiwan
- Department of Oncology, National Taiwan University Hospital, Taipei, Taiwan
- Graduate Institute of Oncology, College of Medicine, National Taiwan University, Taipei, Taiwan
| | | | - Katherine Pogue-Geile
- National Surgical Adjuvant Breast and Bowel Project (NSABP)/NRG Oncology, Pittsburgh, PA USA
| | - Kim R. M. Blenman
- Department of Internal Medicine, Section of Medical Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT USA
| | | | - Stuart Schnitt
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA USA
| | - Sunil R. Lakhani
- The University of Queensland Centre for Clinical Research and Pathology Queensland, Brisbane, QLD Australia
| | - Anne Vincent-Salomon
- Institut Curie, Paris Sciences Lettres Université, Inserm U934, Department of Pathology, Paris, France
| | - Federico Rojo
- Pathology Department, Instituto de Investigación Sanitaria Fundación Jiménez Díaz (IIS-FJD) - CIBERONC, Madrid, Spain
- GEICAM-Spanish Breast Cancer Research Group, Madrid, Spain
| | - Jeremy P. Braybrooke
- Nuffield Department of Population Health, University of Oxford, Oxford and Department of Medical Oncology, University Hospitals Bristol NHS Foundation Trust, Bristol, UK
| | - Matthew G. Hanna
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - M. Teresa Soler-Monsó
- Department of Pathology, Bellvitge University Hospital, IDIBELL. Breast Unit. Catalan Institut of Oncology. L ‘Hospitalet del Llobregat’, Barcelona, 08908 Catalonia Spain
| | - Daniel Bethmann
- University Hospital Halle (Saale), Institute of Pathology, Halle (Saale), Germany
| | - Carlos A. Castaneda
- Department of Medical Oncology and Research, Instituto Nacional de Enfermedades Neoplasicas, Lima, 15038 Peru
| | - Karen Willard-Gallo
- Molecular Immunology Unit, Institut Jules Bordet, Universitè Libre de Bruxelles, Brussels, Belgium
| | - Ashish Sharma
- Department of Biomedical Informatics, Emory University, Atlanta, GA USA
| | - Huang-Chun Lien
- Department of Pathology, National Taiwan University Hospital, Taipei, Taiwan
| | - Susan Fineberg
- Department of Pathology, Montefiore Medical Center and the Albert Einstein College of Medicine, Bronx, NY USA
| | - Jeppe Thagaard
- DTU Compute, Department of Applied Mathematics, Technical University of Denmark; Visiopharm A/S, Hørsholm, Denmark
| | - Laura Comerma
- GEICAM-Spanish Breast Cancer Research Group, Madrid, Spain
- Pathology Department, Hospital del Mar, Parc de Salut Mar, Barcelona, Spain
| | - Paula Gonzalez-Ericsson
- Breast Cancer Program, Vanderbilt-Ingram Cancer Center, Vanderbilt University Medical Center, Nashville, TN USA
| | - Edi Brogi
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY USA
| | - Sherene Loi
- Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria Australia
- Division of Research and Cancer Medicine, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC Australia
| | - Joel Saltz
- Biomedical Informatics Department, Stony Brook University, Stony Brook, NY USA
| | - Frederick Klaushen
- Institute of Pathology, Charité Universitätsmedizin Berlin, Berlin, Germany
| | - Lee Cooper
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, IL USA
| | - Mohamed Amgad
- Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA USA
| | - David A. Moore
- Department of Pathology, UCL Cancer Institute, UCL, London, UK
- University College Hospitals NHS Trust, London, UK
| | - Roberto Salgado
- Division of Research and Cancer Medicine, Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC Australia
- Department of Pathology, GZA-ZNA, Antwerp, Belgium
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Chen BT, Chen Z, Ye N, Mambetsariev I, Fricke J, Daniel E, Wang G, Wong CW, Rockne RC, Colen RR, Nasser MW, Batra SK, Holodny AI, Sampath S, Salgia R. Differentiating Peripherally-Located Small Cell Lung Cancer From Non-small Cell Lung Cancer Using a CT Radiomic Approach. Front Oncol 2020; 10:593. [PMID: 32391274 PMCID: PMC7188953 DOI: 10.3389/fonc.2020.00593] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 03/31/2020] [Indexed: 01/06/2023] Open
Abstract
Lung cancer can be classified into two main categories: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), which are different in treatment strategy and survival probability. The lung CT images of SCLC and NSCLC are similar such that their subtle differences are hardly visually discernible by the human eye through conventional imaging evaluation. We hypothesize that SCLC/NSCLC differentiation could be achieved via computerized image feature analysis and classification in feature space, as termed a radiomic model. The purpose of this study was to use CT radiomics to differentiate SCLC from NSCLC adenocarcinoma. Patients with primary lung cancer, either SCLC or NSCLC adenocarcinoma, were retrospectively identified. The post-diagnosis pre-treatment lung CT images were used to segment the lung cancers. Radiomic features were extracted from histogram-based statistics, textural analysis of tumor images and their wavelet transforms. A minimal-redundancy-maximal-relevance method was used for feature selection. The predictive model was constructed with a multilayer artificial neural network. The performance of the SCLC/NSCLC adenocarcinoma classifier was evaluated by the area under the receiver operating characteristic curve (AUC). Our study cohort consisted of 69 primary lung cancer patients with SCLC (n = 35; age mean ± SD = 66.91± 9.75 years), and NSCLC adenocarcinoma (n = 34; age mean ± SD = 58.55 ± 11.94 years). The SCLC group had more male patients and smokers than the NSCLC group (P < 0.05). Our SCLC/NSCLC classifier achieved an overall performance of AUC of 0.93 (95% confidence interval = [0.85, 0.97]), sensitivity = 0.85, and specificity = 0.85). Adding clinical data such as smoking history could improve the performance slightly. The top ranking radiomic features were mostly textural features. Our results showed that CT radiomics could quantitatively represent tumor heterogeneity and therefore could be used to differentiate primary lung cancer subtypes with satisfying results. CT image processing with the wavelet transformation technique enhanced the radiomic features for SCLC/NSCLC classification. Our pilot study should motivate further investigation of radiomics as a non-invasive approach for early diagnosis and treatment of lung cancer.
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Affiliation(s)
- Bihong T Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Zikuan Chen
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Ningrong Ye
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Isa Mambetsariev
- Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center and Beckman Research Institute, Duarte, CA, United States
| | - Jeremy Fricke
- Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center and Beckman Research Institute, Duarte, CA, United States
| | - Ebenezer Daniel
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - George Wang
- Department of Diagnostic Radiology, City of Hope National Medical Center, Duarte, CA, United States
| | - Chi Wah Wong
- Applied AI and Data Science, City of Hope National Medical Center, Duarte, CA, United States
| | - Russell C Rockne
- Division of Mathematical Oncology, City of Hope National Medical Center, Duarte, CA, United States
| | - Rivka R Colen
- Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA, United States.,Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA, United States
| | - Mohd W Nasser
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, United States
| | - Surinder K Batra
- Department of Biochemistry and Molecular Biology, University of Nebraska Medical Center, Omaha, NE, United States
| | - Andrei I Holodny
- Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY, United States
| | - Sagus Sampath
- Department of Radiation Oncology, City of Hope National Medical Center, Duarte, CA, United States
| | - Ravi Salgia
- Department of Medical Oncology and Therapeutics Research, City of Hope Comprehensive Cancer Center and Beckman Research Institute, Duarte, CA, United States
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40
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Mazzaschi G, Milanese G, Pagano P, Madeddu D, Gnetti L, Trentini F, Falco A, Frati C, Lorusso B, Lagrasta C, Minari R, Ampollini L, Silva M, Sverzellati N, Quaini F, Roti G, Tiseo M. Integrated CT imaging and tissue immune features disclose a radio-immune signature with high prognostic impact on surgically resected NSCLC. Lung Cancer 2020; 144:30-39. [PMID: 32361033 DOI: 10.1016/j.lungcan.2020.04.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Revised: 03/06/2020] [Accepted: 04/07/2020] [Indexed: 02/07/2023]
Abstract
OBJECTIVES Qualitative and quantitative CT imaging features might intercept the multifaceted tumor immune microenvironment (TIME), providing a non-invasive approach to design new prognostic models in NSCLC patients. MATERIALS AND METHODS Our study population consisted of 100 surgically resected NSCLC patients among which 31 served as a validation cohort for quantitative image analysis. TIME was classified according to PD-L1 expression and the magnitude of Tumor Infiltrating Lymphocytes (TILs) and further defined as hot or cold by the tissue analysis of effector (CD8-to-CD3high/PD-1-to-CD8low) or inert (CD8-to-CD3low/PD-1-to-CD8high) phenotypes. CT datasets acted as source for qualitative (semantic, CT-SFs) and quantitative (radiomic, CT-RFs) features which were correlated with clinico-pathological and TIME profiles to determine their impact on survival outcome. RESULTS Specific CT-SFs (texture [TXT], effect [EFC] and margins [MRG]) strongly correlated to PD-L1 and TILs status and showed significant impact on survival outcome (TXT, HR:3.39, 95 % CI 1.12-10-27, P < 0.05; EFC, HR:0.41, 95 % CI 0.18-0.93, P < 0.05; MRG, HR:1.93, 95 % CI 0.88-4.25, P = 0.09). Seven CT derived radiomic features were able to sharply discriminate cases with hot (inflamed) vs cold (desert) TIME, which also exhibited opposite OS (long vs short, HR:0.09, 95 % CI 0.04-0.23, P < 0.001) and DFS (long vs short, HR:0.31, 95 % CI 0.16-0.58, P < 0.001). Moreover, we identified 6 prognostic radiomic features among which ClusterProminence displayed the highest statistical significance (HR:0.13, 95 % CI 0.06-0.31, P < 0.001). These findings were independently validated in an additional cohort of NSCLC (HR:0.11, 95 % CI 0.03-0.40, P = 0.001). Finally, in our training cohort we developed a multiparametric prognostic model, interlacing TIME and clinico-pathological characteristics with CT-SFs (ROC curve AUC:0.83, 95 % CI 0.71-0.92, P < 0.001) or CT-RFs (AUC: 0.91, 95 % CI 0.83-0.99, P < 0.001), which appeared to outperform pTNM staging (AUC: 0.66, 95 % CI 0.51-0.80, P < 0.05) in the risk assessment of NSCLC. CONCLUSION Higher order CT extracted features associated with specific TIME profiles may reveal a radio-immune signature with prognostic impact on resected NSCLC.
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Affiliation(s)
- Giulia Mazzaschi
- Department of Medicine and Surgery, University of Parma, Medical Oncology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy.
| | - Gianluca Milanese
- Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy.
| | - Paolo Pagano
- Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy.
| | - Denise Madeddu
- Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy.
| | - Letizia Gnetti
- Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy.
| | - Francesca Trentini
- Department of Medicine and Surgery, University of Parma, Medical Oncology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy.
| | - Angela Falco
- Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy.
| | - Caterina Frati
- Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy.
| | - Bruno Lorusso
- Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy.
| | - Costanza Lagrasta
- Department of Medicine and Surgery, University of Parma, Pathology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy.
| | - Roberta Minari
- Department of Medicine and Surgery, University of Parma, Medical Oncology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy.
| | - Luca Ampollini
- Department of Medicine and Surgery, University of Parma, Thoracic Surgery, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy.
| | - Mario Silva
- Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy.
| | - Nicola Sverzellati
- Department of Medicine and Surgery, University of Parma, Institute of Radiologic Science, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy.
| | - Federico Quaini
- Department of Medicine and Surgery, Hematology and Bone Marrow Transplantation, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy.
| | - Giovanni Roti
- Department of Medicine and Surgery, Hematology and Bone Marrow Transplantation, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy.
| | - Marcello Tiseo
- Department of Medicine and Surgery, University of Parma, Medical Oncology Unit, University Hospital of Parma, Via Gramsci 14, 43126, Parma, Italy.
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41
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Delta-radiomics increases multicentre reproducibility: a phantom study. Med Oncol 2020; 37:38. [PMID: 32236847 DOI: 10.1007/s12032-020-01359-9] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 03/06/2020] [Indexed: 12/19/2022]
Abstract
Texture analysis (TA) can provide quantitative features from medical imaging that can be correlated to clinical endpoints. The challenges relevant to robustness of radiomics features have been analyzed by many researchers, as it seems to be influenced by acquisition and reconstruction protocols. Delta-texture analysis (D-TA), conversely, consist in the analysis of TA feature variations at different acquisition times, usually before and after a therapy. Aim of this study was to investigate the influence of different CT scanners and acquisition parameters in the robustness of TA and D-TA. We scanned a commercial phantom (CIRS model 467, Gammex, Middleton, WI, USA), that is used for the calibration of electron density, two times by varying the disposition of plugs, using three different scanners. After the segmentation, we extracted TA features with LifeX and calculated TA features and D-TA features, defined as the variation of each TA parameters extracted from the same position by varying the plugs with the formula (Y-X)/X. The robustness of TA and D-TA features were then tested with intraclass coefficient correlation (ICC) analysis. The reliability of TA parameters across different scans, with different acquisition parameters and ROI positions has shown poor reliability in 12/37 and moderate reliability in the remaining 25/37, with no parameters showing good reliability. The reliability of D-TA, conversely, showed poor reliability in 10/37 parameters, moderate reliability in 10/37 parameters, and good reliability in 17/37 parameters. The comparison between TA and D-TA ICCs showed a significant difference for the whole group of parameters (p:0.004) and for the subclasses of GLCM parameters (p:0.033), whereas for the other subclasses of matrices (GLRLM, NGLDM, GLZLM, Histogram), the difference was not significant. D-TA features seem to be more robust than TA features. These findings reinforce the potentiality for using D-TA features for early assessment of treatment response and for developing tailored therapies. More work is needed in a clinical setting to confirm the results of the present study.
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Khorrami M, Bera K, Leo P, Vaidya P, Patil P, Thawani R, Velu P, Rajiah P, Alilou M, Choi H, Feldman MD, Gilkeson RC, Linden P, Fu P, Pass H, Velcheti V, Madabhushi A. Stable and discriminating radiomic predictor of recurrence in early stage non-small cell lung cancer: Multi-site study. Lung Cancer 2020; 142:90-97. [PMID: 32120229 DOI: 10.1016/j.lungcan.2020.02.018] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2019] [Revised: 02/03/2020] [Accepted: 02/25/2020] [Indexed: 01/14/2023]
Abstract
OBJECTIVES To evaluate whether combining stability and discriminability criteria in building radiomic classifiers will improve the prognosis of cancer recurrence in early stage non-small cell lung cancer on non-contrast computer tomography (CT). MATERIALS AND METHODS CT scans of 610 patients with early stage (IA, IB, IIA) NSCLC from four independent cohorts were evaluated. A total of 350 patients from Cleveland Clinic Foundation and University of Pennsylvania were divided into two equal sets for training (D1) and validation set (D2). 80 patients from The Cancer Genome Atlas Lung Adenocarcinoma and Squamous Cell Carcinoma and 195 patients from The Cancer Imaging Archive, were used as independent second (D3) and third (D4) validation sets. A linear discriminant analysis (LDA) classifier was built based on the most stable and discriminate features. In addition, a radiomic risk score (RRS) was generated by using least absolute shrinkage and selection operator, Cox regression model to predict time to progression (TTP) following surgery. RESULTS A feature selection strategy focusing on both feature discriminability and stability resulted in the classifier having a higher discriminability on validation datasets compared to the discriminability alone criteria in discriminating cancer recurrence (D2, AUC of 0.75 vs. 0.65; D3, 0.74 vs. 0.62; D4, 0.76 vs. 0.63). The RRS generated by most stable-discriminating features was significantly associated with TTP compared to discriminating alone criteria (HR = 1.66, C-index of 0.72 vs. HR = 1.04, C-index of 0.62). CONCLUSION Accounting for both stability and discriminability yielded a more generalizable classifier for predicting cancer recurrence and TTP in early stage NSCLC.
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Affiliation(s)
- Mohammadhadi Khorrami
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Patrick Leo
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Pranjal Vaidya
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Pradnya Patil
- Department of Solid Tumor Oncology, Cleveland Clinic, Cleveland, OH, USA
| | - Rajat Thawani
- Department of Internal Medicine, Maimonides Medical Center, Brooklyn, NY, USA
| | - Priya Velu
- Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, USA
| | - Prabhakar Rajiah
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX, USA
| | - Mehdi Alilou
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Humberto Choi
- Department of Pulmonary Medicine, Cleveland Clinic, Cleveland, OH, USA
| | - Michael D Feldman
- Pathology and Laboratory Medicine, Hospital of the University of Pennsylvania, USA
| | | | - Philip Linden
- Thoracic and Esophageal Surgery Department, University Hospitals of Cleveland, OH, USA
| | - Pingfu Fu
- Department of Population and Quantitative Health Sciences, CWRU, Cleveland, OH, USA
| | - Harvey Pass
- Department of Hematology and Oncology, NYU Langone Health, New York, NY, USA
| | - Vamsidhar Velcheti
- Department of Hematology and Oncology, NYU Langone Health, New York, NY, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA.
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Vaidya P, Bera K, Gupta A, Wang X, Corredor G, Fu P, Beig N, Prasanna P, Patil PD, Velu PD, Rajiah P, Gilkeson R, Feldman MD, Choi H, Velcheti V, Madabhushi A. CT derived radiomic score for predicting the added benefit of adjuvant chemotherapy following surgery in stage I, II resectable non-small cell lung cancer: a retrospective multicohort study for outcome prediction. LANCET DIGITAL HEALTH 2020; 2:e116-e128. [PMID: 33334576 DOI: 10.1016/s2589-7500(20)30002-9] [Citation(s) in RCA: 66] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 01/02/2020] [Accepted: 01/08/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND Use of adjuvant chemotherapy in patients with early-stage lung cancer is controversial because no definite biomarker exists to identify patients who would receive added benefit from it. We aimed to develop and validate a quantitative radiomic risk score (QuRiS) and associated nomogram (QuRNom) for early-stage non-small cell lung cancer (NSCLC) that is prognostic of disease-free survival and predictive of the added benefit of adjuvant chemotherapy following surgery. METHODS We did a retrospective multicohort study of individuals with early-stage NSCLC (stage I and II) who either received surgery alone or surgery plus adjuvant chemotherapy. We selected patients for whom we had available pre-treatment diagnostic CT scans and corresponding survival information. We used radiomic texture features derived from within and outside the primary lung nodule on chest CT scans of patients from the Cleveland Clinic Foundation (Cleveland, OH, USA; cohort D1) to develop QuRiS. A least absolute shrinkage and selection operator-Cox regularisation model was used for data dimension reduction, feature selection, and QuRiS construction. QuRiS was independently validated on a cohort of patients from the University of Pennsylvania (Philadephia, PA, USA; cohort D2) and a cohort of patients whose CT scans were derived from The Cancer Imaging Archive (cohort D3). QuRNom was constructed by integrating QuRiS with tumour and node descriptors (according to the tumour, node, metastasis staging system) and lymphovascular invasion. The primary endpoint of the study was the assessment of the performance of QuRiS and QuRNom in predicting disease-free survival. The added benefit of adjuvant chemotherapy estimated using QuRiS and QuRNom was validated by comparing patients who received adjuvant chemotherapy versus patients who underwent surgery alone in cohorts D1-D3. FINDINGS We included: 329 patients in cohort D1 (73 [22%] had surgery plus adjuvant chemotherapy and 256 (78%) had surgery alone); 114 patients in cohort D2 (33 [29%] had surgery plus adjuvant chemotherapy and 81 (71%) had surgery alone); and 82 patients in cohort D3 (24 [29%] had surgery plus adjuvant chemotherapy and 58 (71%) had surgery alone). QuRiS comprised three intratumoral and 10 peritumoral CT-radiomic features and was found to be significantly associated with disease-free survival (ie, prognostic validation of QuRiS; hazard ratio for predicted high-risk vs predicted low-risk groups 1·56, 95% CI 1·08-2·23, p=0·016 for cohort D1; 2·66, 1·24-5·68, p=0·011 for cohort D2; and 2·67, 1·39-5·11, p=0·0029 for cohort D3). To validate the predictive performance of QuRiS, patients were partitioned into three risk groups (high, intermediate, and low risk) on the basis of their corresponding QuRiS. Patients in the high-risk group were observed to have significantly longer survival with adjuvant chemotherapy than patients who underwent surgery alone (0·27, 0·08-0·95, p=0·042, for cohort D1; 0·08, 0·01-0·42, p=0·0029, for cohorts D2 and D3 combined). As concerns QuRNom, the nomogram-estimated survival benefit was predictive of the actual efficacy of adjuvant chemotherapy (0·25, 0·12-0·55, p<0·0001, for cohort D1; 0·13, <0·01-0·99, p=0·0019 for cohort D3). INTERPRETATION QuRiS and QuRNom were validated as being prognostic of disease-free survival and predictive of the added benefit of adjuvant chemotherapy, especially in clinically defined low-risk groups. Since QuRiS is based on routine chest CT imaging, with additional multisite independent validation it could potentially be employed for decision management in non-invasive treatment of resectable lung cancer. FUNDING National Cancer Institute of the US National Institutes of Health, National Center for Research Resources, US Department of Veterans Affairs Biomedical Laboratory Research and Development Service, Department of Defence, National Institute of Diabetes and Digestive and Kidney Diseases, Wallace H Coulter Foundation, Case Western Reserve University, and Dana Foundation.
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Affiliation(s)
- Pranjal Vaidya
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Kaustav Bera
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Amit Gupta
- University Hospitals, Cleveland, OH, USA
| | - Xiangxue Wang
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Germán Corredor
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Pingfu Fu
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Niha Beig
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA
| | - Prateek Prasanna
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Department of Biomedical Informatics, Stony Brook University, Stony Brook, NY, USA
| | | | | | | | | | - Michael D Feldman
- University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA
| | | | - Vamsidhar Velcheti
- NYU Langone- Laura and Isaac Perlmutter Cancer Center, New York, NY, USA
| | - Anant Madabhushi
- Department of Biomedical Engineering, Case Western Reserve University, Cleveland, OH, USA; Louis Stokes Cleveland Veterans Administration Medical Center, Cleveland, Ohio, USA.
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Decazes P, Bohn P. Immunotherapy by Immune Checkpoint Inhibitors and Nuclear Medicine Imaging: Current and Future Applications. Cancers (Basel) 2020; 12:E371. [PMID: 32041105 PMCID: PMC7072145 DOI: 10.3390/cancers12020371] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2019] [Revised: 01/21/2020] [Accepted: 02/04/2020] [Indexed: 12/12/2022] Open
Abstract
Immunotherapy by using immune checkpoint inhibitors is a revolutionary development in oncology. Medical imaging is also impacted by this new therapy, particularly nuclear medicine imaging (also called radionuclide imaging), which uses radioactive tracers to visualize metabolic functions. Our aim was to review the current applications of nuclear medicine imaging in immunotherapy, along with their limitations, and the perspectives offered by this imaging modality. Method: Articles describing the use of radionuclide imaging in immunotherapy were researched using PubMed by April 2019 and analyzed. Results: More than 5000 articles were analyzed, and nearly 100 of them were retained. Radionuclide imaging, notably 18F-FDG PET/CT, already has a major role in many cancers for pre-therapeutic and therapeutic evaluation, diagnoses of adverse effects, called immune-related adverse events (IrAE), and end-of-treatment evaluations. However, these current applications can be hindered by immunotherapy, notably due to atypical response patterns such as pseudoprogression, which is defined as an increase in the size of lesions, or the visualization of new lesions, followed by a response, and hyperprogression, which is an accelerated tumor growth rate after starting treatment. To overcome these difficulties, new opportunities are offered, particularly therapeutic evaluation criteria adapted to immunotherapy and immuno-PET allowing us to predict responses to immunotherapy. Moreover, some new technological solutions are also promising, such as radiomic analyses and body composition on associated anatomical images. However, more research has to be done, notably for the diagnosis of hyperprogression and pseudoprogression. Conclusion: Immunotherapy, by its major impact on cancer and by the new patterns generated on images, is revolutionary in the field of medical images. Nuclear medicine imaging is already established and will be able to help meet new challenges through its plasticity.
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Affiliation(s)
- Pierre Decazes
- Department of Nuclear Medicine, Henri Becquerel Cancer Center, 76000 Rouen, France;
- LITIS-QuantIF-EA (Equipe d’Accueil) 4108, IRIB, Faculty of Medicine, University of Rouen, 76000 Rouen, France
| | - Pierre Bohn
- Department of Nuclear Medicine, Henri Becquerel Cancer Center, 76000 Rouen, France;
- LITIS-QuantIF-EA (Equipe d’Accueil) 4108, IRIB, Faculty of Medicine, University of Rouen, 76000 Rouen, France
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Guerrisi A, Loi E, Ungania S, Russillo M, Bruzzaniti V, Elia F, Desiderio F, Marconi R, Solivetti FM, Strigari L. Novel cancer therapies for advanced cutaneous melanoma: The added value of radiomics in the decision making process-A systematic review. Cancer Med 2020; 9:1603-1612. [PMID: 31951322 PMCID: PMC7050080 DOI: 10.1002/cam4.2709] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2019] [Revised: 10/24/2019] [Accepted: 10/25/2019] [Indexed: 12/11/2022] Open
Abstract
Advanced malignant melanoma represents a public health matter due to its rising incidence and aggressiveness. Novel therapies such as immunotherapy are showing promising results with improved progression free and overall survival in melanoma patients. However, novel targeted and immunotherapies could generate atypical patterns of response which are nowadays a big challenge since imaging criteria (ie Recist 1.1) have not been proven to be always reliable to assess response. Radiomics and in particular texture analysis (TA) represent new quantitative methodologies which could reduce the impact of these limitations providing most robust data in support of clinical decision process. The aim of this paper was to review the state of the art of radiomics/TA when it is applied to the imaging of metastatic melanoma patients.
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Affiliation(s)
- Antonino Guerrisi
- Radiology and Diagnostic Imaging Unit, Department of Clinic and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Rome, Italy
| | - Emiliano Loi
- Medical Physics and Expert Systems Laboratory, Department of Research and Advanced Technologies, Istituti Fisioterapici Ospitalieri -Regina Elena Institute IRCCS, Rome, Italy
| | - Sara Ungania
- Medical Physics and Expert Systems Laboratory, Department of Research and Advanced Technologies, Istituti Fisioterapici Ospitalieri -Regina Elena Institute IRCCS, Rome, Italy
| | - Michelangelo Russillo
- Medical Oncology Unit 1, Department of Clinic and Cancer Research, Regina Elena Institute, IRCCS, Rome, Italy
| | - Vicente Bruzzaniti
- Medical Physics and Expert Systems Laboratory, Department of Research and Advanced Technologies, Istituti Fisioterapici Ospitalieri -Regina Elena Institute IRCCS, Rome, Italy
| | - Fulvia Elia
- Radiology and Diagnostic Imaging Unit, Department of Clinic and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Rome, Italy
| | - Flora Desiderio
- Radiology and Diagnostic Imaging Unit, Department of Clinic and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Rome, Italy
| | - Raffaella Marconi
- Medical Physics and Expert Systems Laboratory, Department of Research and Advanced Technologies, Istituti Fisioterapici Ospitalieri -Regina Elena Institute IRCCS, Rome, Italy
| | - Francesco Maria Solivetti
- Radiology and Diagnostic Imaging Unit, Department of Clinic and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Rome, Italy
| | - Lidia Strigari
- Medical Physics and Expert Systems Laboratory, Department of Research and Advanced Technologies, Istituti Fisioterapici Ospitalieri -Regina Elena Institute IRCCS, Rome, Italy
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Nardone V, Tini P, Pastina P, Botta C, Reginelli A, Carbone SF, Giannicola R, Calabrese G, Tebala C, Guida C, Giudice A, Barbieri V, Tassone P, Tagliaferri P, Cappabianca S, Capasso R, Luce A, Caraglia M, Mazzei MA, Pirtoli L, Correale P. Radiomics predicts survival of patients with advanced non-small cell lung cancer undergoing PD-1 blockade using Nivolumab. Oncol Lett 2019; 19:1559-1566. [PMID: 31966081 DOI: 10.3892/ol.2019.11220] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 08/13/2019] [Indexed: 12/27/2022] Open
Abstract
Immune checkpoint blockade is an emerging anticancer strategy, and Nivolumab is a human mAb to PD-1 that is used in the treatment of a number of different malignancies, including non-small cell lung cancer (NSCLC), kidney cancer, urothelial carcinoma and melanoma. Although the use of Nivolumab prolongs survival in a number of patients, this treatment is hampered by high cost. Therefore, the identification of predictive markers of response to treatment in patients is required. In this context, PD-1/PDL1 blockade antitumor effects occur through the reactivation of a pre-existing immune response, and the efficacy of these effects is strictly associated with the presence of necrosis, hypoxia and inflammation at the tumour sites. It has been indicated that these events can be evaluated by specific assessments using a computed tomography (CT) texture analysis (TA) or radiomics. Therefore, a retrospective study was performed, which aimed to evaluate the potential use of this analysis in the identification of patients with NSCLC who may benefit from Nivolumab treatment. A retrospective analysis was performed of 59 patients with metastatic NSCLC who received Nivolumab treatment between January 2015 and July 2017 at Siena University Hospital (35 patients, training dataset), Catanzaro University Hospital and Reggio Calabria Grand Metropolitan Hospital, Italy (24 patients, validation dataset). Pre- and post-contrast CT sequences were used to contour the gross tumour volume (GTV) of the target lesions prior to Nivolumab treatment. The impact of variations on contouring was analysed using two delineations, which were performed on each patient, and the TA parameters were tested for reliability using the Intraclass Coefficient Correlation method (ICC). All analyses for the current study were performed using LifeX Software©. Imaging, clinical and pathological parameters were correlated with progression free survival and overall survival (OS) using Kaplan Meier analysis. An external validation testing was performed for the TA Score using the validation dataset. A total of 59 patients were included in the analysis of the present study. The reliability ICC analysis of 14 TA parameters indicated a highly reproducibility (ICC >0.70, single measure) in 12 (85%) pre- contrast and 13 (93%) post-contrast exams. A specific cut-off was detected for each of the following parameters: volume (score 1 >36 ml), histogram entropy (score 1 > 1.30), compacity (score 1 <3), gray level co-occurrence matrix (GLCM)-entropy (score 1 >1.80), GLCM-Dissimilarity (score 1 >5) and GLCM-Correlation (score 1<0.54). The global texture score allowed the classification of two subgroups of Low (Score 0-1; 36 patients; 61%) and High Risk patients (Score >1; 23 patients; 39%) that respectively, showed a median OS of 26 (mean +/- SD: 18 +/- 1.98 months; 95% CI 14-21 months) and 5 months (mean +/- SD: 6 +/- 0.99 months; 95% CI: 4-8 months; P=0.002). The current study indicated that TA parameters can identify patients that will benefit from PD-1 blockage by defining the radiological settings that are potentially suggestive of an active immune response. These results require further confirmation in prospective trials.
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Affiliation(s)
- Valerio Nardone
- Unit of Radiation Oncology, Integrated Department of Diagnostic Radiology and Radiotherapy, Ospedale del Mare, I-80147 Naples, Italy
| | - Paolo Tini
- Unit of Radiation Oncology, Oncology Department, University Hospital of Siena, I-53100 Siena, Italy
| | - Pierpaolo Pastina
- Unit of Radiation Oncology, Oncology Department, University Hospital of Siena, I-53100 Siena, Italy
| | - Cirino Botta
- Integrated Area of Medical Oncology, AOU Mater Domini and Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, I-88100 Catanzaro, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania 'L. Vanvitelli', I-80138 Naples, Italy
| | - Salvatore Francesco Carbone
- Unit of Medical Imaging, Emergency Department and Diagnostic Services, University Hospital of Siena, I-53100 Siena, Italy
| | - Rocco Giannicola
- Unit of Medical Oncology, Oncology Department, Grand Metropolitan Hospital 'Bianchi Melacrino Morelli' Reggio Calabria I-89124, Italy
| | - Grazia Calabrese
- Unit of Radiology, Department of Diagnostic Services, Grand Metropolitan Hospital 'Bianchi Melacrino Morelli' Reggio Calabria I-89124, Italy
| | - Carmela Tebala
- Unit of Radiology, Department of Diagnostic Services, Grand Metropolitan Hospital 'Bianchi Melacrino Morelli' Reggio Calabria I-89124, Italy
| | - Cesare Guida
- Unit of Radiation Oncology, Integrated Department of Diagnostic Radiology and Radiotherapy, Ospedale del Mare, I-80147 Naples, Italy
| | - Aldo Giudice
- Epidemiology Unit, IRCCS Istituto Nazionale Tumori 'Fondazione G. Pascale', I-80131 Naples, Italy
| | - Vito Barbieri
- Integrated Area of Medical Oncology, AOU Mater Domini and Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, I-88100 Catanzaro, Italy
| | - Pierfrancesco Tassone
- Integrated Area of Medical Oncology, AOU Mater Domini and Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, I-88100 Catanzaro, Italy
| | - Pierosandro Tagliaferri
- Integrated Area of Medical Oncology, AOU Mater Domini and Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, I-88100 Catanzaro, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania 'L. Vanvitelli', I-80138 Naples, Italy
| | - Rosanna Capasso
- Department of Precision Medicine, University of Campania 'L. Vanvitelli', I-80138 Naples, Italy
| | - Amalia Luce
- Department of Precision Medicine, University of Campania 'L. Vanvitelli', I-80138 Naples, Italy
| | - Michele Caraglia
- Department of Precision Medicine, University of Campania 'L. Vanvitelli', I-80138 Naples, Italy
| | - Maria Antonietta Mazzei
- Unit of Medical Imaging, Emergency Department and Diagnostic Services, University Hospital of Siena, I-53100 Siena, Italy
| | - Luigi Pirtoli
- Unit of Radiation Oncology, Oncology Department, University Hospital of Siena, I-53100 Siena, Italy
| | - Pierpaolo Correale
- Unit of Medical Oncology, Oncology Department, Grand Metropolitan Hospital 'Bianchi Melacrino Morelli' Reggio Calabria I-89124, Italy
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Kasten BB, Udayakumar N, Leavenworth JW, Wu AM, Lapi SE, McConathy JE, Sorace AG, Bag AK, Markert JM, Warram JM. Current and Future Imaging Methods for Evaluating Response to Immunotherapy in Neuro-Oncology. Theranostics 2019; 9:5085-5104. [PMID: 31410203 PMCID: PMC6691392 DOI: 10.7150/thno.34415] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 04/20/2019] [Indexed: 12/28/2022] Open
Abstract
Imaging plays a central role in evaluating responses to therapy in neuro-oncology patients. The advancing clinical use of immunotherapies has demonstrated that treatment-related inflammatory responses mimic tumor growth via conventional imaging, thus spurring the development of new imaging approaches to adequately distinguish between pseudoprogression and progressive disease. To this end, an increasing number of advanced imaging techniques are being evaluated in preclinical and clinical studies. These novel molecular imaging approaches will serve to complement conventional response assessments during immunotherapy. The goal of these techniques is to provide definitive metrics of tumor response at earlier time points to inform treatment decisions, which has the potential to improve patient outcomes. This review summarizes the available immunotherapy regimens, clinical response criteria, current state-of-the-art imaging approaches, and groundbreaking strategies for future implementation to evaluate the anti-tumor and immune responses to immunotherapy in neuro-oncology applications.
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Affiliation(s)
- Benjamin B. Kasten
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Neha Udayakumar
- School of Medicine, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Jianmei W. Leavenworth
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Anna M. Wu
- Crump Institute for Molecular Imaging, Department of Molecular and Medical Pharmacology, David Geffen School of Medicine at University of California Los Angeles, Los Angeles, CA, United States
| | - Suzanne E. Lapi
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Jonathan E. McConathy
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Anna G. Sorace
- Department of Radiology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Asim K. Bag
- Department of Diagnostic Imaging, St. Jude Children's Research Hospital, Memphis, TN, United States
| | - James M. Markert
- Department of Neurosurgery, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Jason M. Warram
- Department of Otolaryngology, University of Alabama at Birmingham, Birmingham, AL, United States
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