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Peisen F, Gerken A, Hering A, Dahm I, Nikolaou K, Gatidis S, Eigentler TK, Amaral T, Moltz JH, Othman AE. Can Delta Radiomics Improve the Prediction of Best Overall Response, Progression-Free Survival, and Overall Survival of Melanoma Patients Treated with Immune Checkpoint Inhibitors? Cancers (Basel) 2024; 16:2669. [PMID: 39123397 PMCID: PMC11312160 DOI: 10.3390/cancers16152669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2024] [Revised: 07/16/2024] [Accepted: 07/26/2024] [Indexed: 08/12/2024] Open
Abstract
BACKGROUND The prevalence of metastatic melanoma is increasing, necessitating the identification of patients who do not benefit from immunotherapy. This study aimed to develop a radiomic biomarker based on the segmentation of all metastases at baseline and the first follow-up CT for the endpoints best overall response (BOR), progression-free survival (PFS), and overall survival (OS), encompassing various immunotherapies. Additionally, this study investigated whether reducing the number of segmented metastases per patient affects predictive capacity. METHODS The total tumour load, excluding cerebral metastases, from 146 baseline and 146 first follow-up CTs of melanoma patients treated with first-line immunotherapy was volumetrically segmented. Twenty-one random forest models were trained and compared for the endpoints BOR; PFS at 6, 9, and 12 months; and OS at 6, 9, and 12 months, using as input either only clinical parameters, whole-tumour-load delta radiomics plus clinical parameters, or delta radiomics from the largest ten metastases plus clinical parameters. RESULTS The whole-tumour-load delta radiomics model performed best for BOR (AUC 0.81); PFS at 6, 9, and 12 months (AUC 0.82, 0.80, and 0.77); and OS at 6 months (AUC 0.74). The model using delta radiomics from the largest ten metastases performed best for OS at 9 and 12 months (AUC 0.71 and 0.75). Although the radiomic models were numerically superior to the clinical model, statistical significance was not reached. CONCLUSIONS The findings indicate that delta radiomics may offer additional value for predicting BOR, PFS, and OS in metastatic melanoma patients undergoing first-line immunotherapy. Despite its complexity, volumetric whole-tumour-load segmentation could be advantageous.
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Affiliation(s)
- Felix Peisen
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (I.D.); (K.N.); (S.G.)
| | - Annika Gerken
- Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Straße 2, 28359 Bremen, Germany; (A.G.); (A.H.); (J.H.M.)
| | - Alessa Hering
- Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Straße 2, 28359 Bremen, Germany; (A.G.); (A.H.); (J.H.M.)
- Diagnostic Image Analysis Group, Radboudumc, Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Isabel Dahm
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (I.D.); (K.N.); (S.G.)
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (I.D.); (K.N.); (S.G.)
- Cluster of Excellence iFIT (EXC 2180) “Image-Guided and Functionally Instructed Tumor Therapies”, Faculty of Medicine, Eberhard Karls University, 72076 Tuebingen, Germany
| | - Sergios Gatidis
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (I.D.); (K.N.); (S.G.)
- Max Planck Institute for Intelligent Systems, Max-Planck-Ring 4, 72076 Tuebingen, Germany
| | - Thomas K. Eigentler
- Center of Dermato-Oncology, Department of Dermatology, Eberhard Karls University, Tuebingen University Hospital, Liebermeisterstraße 25, 72076 Tuebingen, Germany; (T.K.E.); (T.A.)
- Department of Dermatology, Venereology and Allergology, Charité—Universitätsmedizin Berlin, Freie Universität Berlin and Humboldt-Universität zu Berlin, Luisenstraße 2, 10117 Berlin, Germany
| | - Teresa Amaral
- Center of Dermato-Oncology, Department of Dermatology, Eberhard Karls University, Tuebingen University Hospital, Liebermeisterstraße 25, 72076 Tuebingen, Germany; (T.K.E.); (T.A.)
| | - Jan H. Moltz
- Fraunhofer Institute for Digital Medicine MEVIS, Max-von-Laue-Straße 2, 28359 Bremen, Germany; (A.G.); (A.H.); (J.H.M.)
| | - Ahmed E. Othman
- Institute of Neuroradiology, Johannes Gutenberg University Hospital Mainz, Langenbeckstraße 1, 55131 Mainz, Germany;
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Valenti A, Falcone I, Valenti F, Ricciardi E, Di Martino S, Maccallini MT, Cerro M, Desiderio F, Miseo L, Russillo M, Guerrisi A. Biobanks as an Indispensable Tool in the "Era" of Precision Medicine: Key Role in the Management of Complex Diseases, Such as Melanoma. J Pers Med 2024; 14:731. [PMID: 39063985 PMCID: PMC11278009 DOI: 10.3390/jpm14070731] [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: 06/08/2024] [Revised: 07/02/2024] [Accepted: 07/03/2024] [Indexed: 07/28/2024] Open
Abstract
In recent years, medicine has undergone profound changes, strongly entering a new phase defined as the "era of precision medicine". In this context, patient clinical management involves various scientific approaches that allow for a comprehensive pathology evaluation: from preventive processes (where applicable) to genetic and diagnostic studies. In this scenario, biobanks play an important role and, over the years, have gained increasing prestige, moving from small deposits to large collections of samples of various natures. Disease-oriented biobanks are rapidly developing as they provide useful information for the management of complex diseases, such as melanoma. Indeed, melanoma, given its highly heterogeneous characteristics, is one of the oncologic diseases with the greatest clinical and therapeutic management complexity. So, the possibility of extrapolating tissue, genetic and imaging data from dedicated biobanks could result in more selective study approaches. In this review, we specifically analyze the several biobank types to evaluate their role in technology development, patient monitoring and research of new biomarkers, especially in the melanoma context.
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Affiliation(s)
- Alessandro Valenti
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy; (F.D.); (L.M.); (A.G.)
| | - Italia Falcone
- SAFU, Department of Research, Advanced Diagnostics, and Technological Innovation, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy;
| | - Fabio Valenti
- UOC Oncological Translational Research, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy; (F.V.); (E.R.)
| | - Elena Ricciardi
- UOC Oncological Translational Research, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy; (F.V.); (E.R.)
| | - Simona Di Martino
- UOC Pathology Unit, Biobank IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy;
| | - Maria Teresa Maccallini
- Department of Clinical and Molecular Medicine, Università La Sapienza di Roma, 00185 Rome, Italy; (M.T.M.); (M.C.)
| | - Marianna Cerro
- Department of Clinical and Molecular Medicine, Università La Sapienza di Roma, 00185 Rome, Italy; (M.T.M.); (M.C.)
| | - Flora Desiderio
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy; (F.D.); (L.M.); (A.G.)
| | - Ludovica Miseo
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy; (F.D.); (L.M.); (A.G.)
| | - Michelangelo Russillo
- Division of Medical Oncology, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy;
| | - Antonino Guerrisi
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy; (F.D.); (L.M.); (A.G.)
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Peisen F, Gerken A, Dahm I, Nikolaou K, Eigentler T, Amaral T, Moltz JH, Othman AE, Gatidis S. Pre-treatment 18F-FDG-PET/CT parameters as biomarkers for progression free survival, best overall response and overall survival in metastatic melanoma patients undergoing first-line immunotherapy. PLoS One 2024; 19:e0296253. [PMID: 38180971 PMCID: PMC10769042 DOI: 10.1371/journal.pone.0296253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2023] [Accepted: 12/08/2023] [Indexed: 01/07/2024] Open
Abstract
BACKGROUND Checkpoint inhibitors have drastically improved the therapy of patients with advanced melanoma. 18F-FDG-PET/CT parameters might act as biomarkers for response and survival and thus can identify patients that do not benefit from immunotherapy. However, little literature exists on the association of baseline 18F-FDG-PET/CT parameters with progression free survival (PFS), best overall response (BOR), and overall survival (OS). MATERIALS AND METHODS Using a whole tumor volume segmentation approach, we investigated in a retrospective registry study (n = 50) whether pre-treatment 18F-FDG-PET/CT parameters of three subgroups (tumor burden, tumor glucose uptake and non-tumoral hematopoietic tissue metabolism), can act as biomarkers for the primary endpoints PFS and BOR as well as for the secondary endpoint OS. RESULTS Compared to the sole use of clinical parameters, baseline 18F-FDG-PET/CT parameters did not significantly improve a Cox proportional-hazard model for PFS (C-index/AIC: 0.70/225.17 and 0.68/223.54, respectively; p = 0.14). A binomial logistic regression analysis for BOR was not statistically significant (χ2(15) = 16.44, p = 0.35), with a low amount of explained variance (Nagelkerke's R2 = 0.38). Mean FDG uptake of the spleen contributed significantly to a Cox proportional-hazard model for OS (HR 3.55, p = 0.04). CONCLUSIONS The present study could not confirm the capability of the pre-treatment 18F-FDG-PET/CT parameters tumor burden, tumor glucose uptake and non-tumoral hematopoietic tissue metabolism to act as biomarkers for PFS and BOR in metastatic melanoma patients receiving first-line immunotherapy. The documented potential of 18F-FDG uptake by immune-mediating tissues such as the spleen to act as a biomarker for OS has been reproduced.
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Affiliation(s)
- Felix Peisen
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen, Germany
| | | | - Isabel Dahm
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen, Germany
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen, Germany
- Image-guided and Functionally Instructed Tumor Therapies (iFIT), The Cluster of Excellence (EXC 2180), Tuebingen, Germany
| | - Thomas Eigentler
- Center of Dermato-Oncology, Department of Dermatology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen, Germany
- Department of Dermatology, Venereology and Allergology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humbolt-Universität zu Berlin, Berlin, Germany
| | - Teresa Amaral
- Center of Dermato-Oncology, Department of Dermatology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen, Germany
| | | | - Ahmed E. Othman
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen, Germany
- Institute of Neuroradiology, Johannes Gutenberg University Hospital Mainz, Mainz, Germany
| | - Sergios Gatidis
- Department of Diagnostic and Interventional Radiology, Eberhard Karls University, Tuebingen University Hospital, Tuebingen, Germany
- Max Planck Institute for Intelligent Systems, Tuebingen, Germany
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Poletto S, Paruzzo L, Nepote A, Caravelli D, Sangiolo D, Carnevale-Schianca F. Predictive Factors in Metastatic Melanoma Treated with Immune Checkpoint Inhibitors: From Clinical Practice to Future Perspective. Cancers (Basel) 2023; 16:101. [PMID: 38201531 PMCID: PMC10778365 DOI: 10.3390/cancers16010101] [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: 10/10/2023] [Revised: 12/11/2023] [Accepted: 12/21/2023] [Indexed: 01/12/2024] Open
Abstract
The introduction of immunotherapy revolutionized the treatment landscape in metastatic melanoma. Despite the impressive results associated with immune checkpoint inhibitors (ICIs), only a portion of patients obtain a response to this treatment. In this scenario, the research of predictive factors is fundamental to identify patients who may have a response and to exclude patients with a low possibility to respond. These factors can be host-associated, immune system activation-related, and tumor-related. Patient-related factors can vary from data obtained by medical history (performance status, age, sex, body mass index, concomitant medications, and comorbidities) to analysis of the gut microbiome from fecal samples. Tumor-related factors can reflect tumor burden (metastatic sites, lactate dehydrogenase, C-reactive protein, and circulating tumor DNA) or can derive from the analysis of tumor samples (driver mutations, tumor-infiltrating lymphocytes, and myeloid cells). Biomarkers evaluating the immune system activation, such as IFN-gamma gene expression profile and analysis of circulating immune cell subsets, have emerged in recent years as significantly correlated with response to ICIs. In this manuscript, we critically reviewed the most updated literature data on the landscape of predictive factors in metastatic melanoma treated with ICIs. We focus on the principal limits and potentiality of different methods, shedding light on the more promising biomarkers.
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Affiliation(s)
- Stefano Poletto
- Department of Oncology, University of Turin, AOU S. Luigi Gonzaga, 10043 Orbassano, Italy;
| | - Luca Paruzzo
- Department of Oncology, University of Turin, 10124 Turin, Italy; (L.P.); (D.S.)
- Division of Hematology and Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alessandro Nepote
- Department of Oncology, University of Turin, AOU S. Luigi Gonzaga, 10043 Orbassano, Italy;
| | - Daniela Caravelli
- Medical Oncology Division, Candiolo Cancer Institute, FPO-IRCCs, 10060 Candiolo, Italy; (D.C.); (F.C.-S.)
| | - Dario Sangiolo
- Department of Oncology, University of Turin, 10124 Turin, Italy; (L.P.); (D.S.)
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Peisen F, Gerken A, Hering A, Dahm I, Nikolaou K, Gatidis S, Eigentler TK, Amaral T, Moltz JH, Othman AE. Can Whole-Body Baseline CT Radiomics Add Information to the Prediction of Best Response, Progression-Free Survival, and Overall Survival of Stage IV Melanoma Patients Receiving First-Line Targeted Therapy: A Retrospective Register Study. Diagnostics (Basel) 2023; 13:3210. [PMID: 37892030 PMCID: PMC10605712 DOI: 10.3390/diagnostics13203210] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/06/2023] [Accepted: 10/12/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND The aim of this study was to investigate whether the combination of radiomics and clinical parameters in a machine-learning model offers additive information compared with the use of only clinical parameters in predicting the best response, progression-free survival after six months, as well as overall survival after six and twelve months in patients with stage IV malignant melanoma undergoing first-line targeted therapy. METHODS A baseline machine-learning model using clinical variables (demographic parameters and tumor markers) was compared with an extended model using clinical variables and radiomic features of the whole tumor burden, utilizing repeated five-fold cross-validation. Baseline CTs of 91 stage IV malignant melanoma patients, all treated in the same university hospital, were identified in the Central Malignant Melanoma Registry and all metastases were volumetrically segmented (n = 4727). RESULTS Compared with the baseline model, the extended radiomics model did not add significantly more information to the best-response prediction (AUC [95% CI] 0.548 (0.188, 0.808) vs. 0.487 (0.139, 0.743)), the prediction of PFS after six months (AUC [95% CI] 0.699 (0.436, 0.958) vs. 0.604 (0.373, 0.867)), or the overall survival prediction after six and twelve months (AUC [95% CI] 0.685 (0.188, 0.967) vs. 0.766 (0.433, 1.000) and AUC [95% CI] 0.554 (0.163, 0.781) vs. 0.616 (0.271, 1.000), respectively). CONCLUSIONS The results showed no additional value of baseline whole-body CT radiomics for best-response prediction, progression-free survival prediction for six months, or six-month and twelve-month overall survival prediction for stage IV melanoma patients receiving first-line targeted therapy. These results need to be validated in a larger cohort.
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Affiliation(s)
- Felix Peisen
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Eberhard Karls University, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (I.D.); (K.N.); (S.G.); (A.E.O.)
| | - Annika Gerken
- Fraunhofer MEVIS, Max-von-Laue-Straße 2, 28359 Bremen, Germany; (A.G.); (A.H.); (J.H.M.)
| | - Alessa Hering
- Fraunhofer MEVIS, Max-von-Laue-Straße 2, 28359 Bremen, Germany; (A.G.); (A.H.); (J.H.M.)
- Diagnostic Image Analysis Group, Radboud University Medical Center (Radboudumc), Geert Grooteplein Zuid 10, 6525 GA Nijmegen, The Netherlands
| | - Isabel Dahm
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Eberhard Karls University, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (I.D.); (K.N.); (S.G.); (A.E.O.)
| | - Konstantin Nikolaou
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Eberhard Karls University, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (I.D.); (K.N.); (S.G.); (A.E.O.)
- Image-Guided and Functionally Instructed Tumor Therapies (iFIT), The Cluster of Excellence (EXC 2180), 72076 Tuebingen, Germany
| | - Sergios Gatidis
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Eberhard Karls University, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (I.D.); (K.N.); (S.G.); (A.E.O.)
- Max Planck Institute for Intelligent Systems, Max-Planck-Ring 4, 72076 Tuebingen, Germany
| | - Thomas K. Eigentler
- Center of Dermato-Oncology, Department of Dermatology, Tuebingen University Hospital, Eberhard Karls University, Liebermeisterstraße 25, 72076 Tuebingen, Germany; (T.K.E.); (T.A.)
- Department of Dermatology, Venereology and Allergology, Charité—Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humbolt-Universität zu Berlin, Luisenstraße 2, 10117 Berlin, Germany
| | - Teresa Amaral
- Center of Dermato-Oncology, Department of Dermatology, Tuebingen University Hospital, Eberhard Karls University, Liebermeisterstraße 25, 72076 Tuebingen, Germany; (T.K.E.); (T.A.)
| | - Jan H. Moltz
- Fraunhofer MEVIS, Max-von-Laue-Straße 2, 28359 Bremen, Germany; (A.G.); (A.H.); (J.H.M.)
| | - Ahmed E. Othman
- Department of Diagnostic and Interventional Radiology, Tuebingen University Hospital, Eberhard Karls University, Hoppe-Seyler-Straße 3, 72076 Tuebingen, Germany; (I.D.); (K.N.); (S.G.); (A.E.O.)
- Institute of Neuroradiology, Johannes Gutenberg University Hospital Mainz, Langenbeckstraße 1, 55131 Mainz, Germany
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Tian BW, Han CL, Wang HC, Yan LJ, Ding ZN, Liu H, Mao XC, Tian JC, Xue JS, Yang LS, Tan SY, Dong ZR, Yan YC, Wang DX, Li T. Effect of liver metastasis on the efficacy of immune checkpoint inhibitors in cancer patients: a systemic review and meta-analysis. Clin Exp Metastasis 2023; 40:255-287. [PMID: 37308706 DOI: 10.1007/s10585-023-10217-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Accepted: 06/05/2023] [Indexed: 06/27/2024]
Abstract
Liver metastasis is a frequent phenomenon in advanced tumor disease. Immune checkpoint inhibitors (ICIs) are a new class of therapeutics that can improve the prognosis of cancer patients. The purpose of this study is to elucidate the relationship between liver metastasis and survival outcomes of patients receiving ICIs treatment. We searched four main databases, including PubMed, EMBASE, Cochrane Library, and Web of Science. Overall survival (OS) and progression-free survival (PFS) were the survival outcomes of our concern. Hazard ratio (HR) with 95% confidence interval (CI) were used to evaluate the relationship between liver metastasis and OS/ PFS. Finally, 163 articles were included in the study. The pooled results showed that patients with liver metastasis receiving ICIs treatment had worse OS (HR=1.82, 95%CI:1.59-2.08) and PFS (HR=1.68, 95%CI:1.49-1.89) than patients without liver metastasis. The effect of liver metastasis on ICIs efficacy differed in different tumor types, and patients with urinary system tumors (renal cell carcinoma OS: HR=2.47, 95%CI:1.76-3.45; urothelial carcinoma OS: HR=2.37, 95%CI:2.03-2.76) had the worst prognosis, followed by patients with melanoma (OS: HR=2.04, 95%CI:1.68-2.49) or non-small cell lung cancer (OS: HR=1.81, 95%CI:1.72-1.91). ICIs efficacy in digestive system tumors (colorectal cancer OS: HR=1.35, 95%CI:1.07-1.71; gastric cancer/ esophagogastric cancer OS: HR=1.17, 95%CI:0.90-1.52) was less affected, and peritoneal metastasis and the number of metastases have a greater clinical significance than liver metastasis based on univariate data. For cancer patients receiving ICIs treatment, the occurrence of liver metastasis is associated with poor prognosis. Different cancer types and metastatic sites may hold a different prognostic effect on the efficacy of ICIs treatment in cancer patients.
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Affiliation(s)
- Bao-Wen Tian
- Department of general surgery, Qilu Hospital, Shandong University, 107 West Wen Hua Road, Jinan, 250012, People's Republic of China
| | - Cheng-Long Han
- Department of general surgery, Qilu Hospital, Shandong University, 107 West Wen Hua Road, Jinan, 250012, People's Republic of China
| | - Han-Chao Wang
- Institute for Financial Studies, Shandong Univeristy, Jinan, 250100, People's Republic of China
| | - Lun-Jie Yan
- Department of general surgery, Qilu Hospital, Shandong University, 107 West Wen Hua Road, Jinan, 250012, People's Republic of China
| | - Zi-Niu Ding
- Department of general surgery, Qilu Hospital, Shandong University, 107 West Wen Hua Road, Jinan, 250012, People's Republic of China
| | - Hui Liu
- Department of general surgery, Qilu Hospital, Shandong University, 107 West Wen Hua Road, Jinan, 250012, People's Republic of China
| | - Xin-Cheng Mao
- Department of general surgery, Qilu Hospital, Shandong University, 107 West Wen Hua Road, Jinan, 250012, People's Republic of China
| | - Jin-Cheng Tian
- Department of general surgery, Qilu Hospital, Shandong University, 107 West Wen Hua Road, Jinan, 250012, People's Republic of China
| | - Jun-Shuai Xue
- Department of general surgery, Qilu Hospital, Shandong University, 107 West Wen Hua Road, Jinan, 250012, People's Republic of China
| | - Long-Shan Yang
- Department of general surgery, Qilu Hospital, Shandong University, 107 West Wen Hua Road, Jinan, 250012, People's Republic of China
| | - Si-Yu Tan
- Department of general surgery, Qilu Hospital, Shandong University, 107 West Wen Hua Road, Jinan, 250012, People's Republic of China
| | - Zhao-Ru Dong
- Department of general surgery, Qilu Hospital, Shandong University, 107 West Wen Hua Road, Jinan, 250012, People's Republic of China
| | - Yu-Chuan Yan
- Department of general surgery, Qilu Hospital, Shandong University, 107 West Wen Hua Road, Jinan, 250012, People's Republic of China
| | - Dong-Xu Wang
- Department of general surgery, Qilu Hospital, Shandong University, 107 West Wen Hua Road, Jinan, 250012, People's Republic of China
| | - Tao Li
- Department of general surgery, Qilu Hospital, Shandong University, 107 West Wen Hua Road, Jinan, 250012, People's Republic of China.
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Durot C, Durot E, Mulé S, Morland D, Godard F, Quinquenel A, Delmer A, Soyer P, Hoeffel C. Pretreatment CT Texture Parameters as Predictive Biomarkers of Progression-Free Survival in Follicular Lymphoma Treated with Immunochemotherapy and Rituximab Maintenance. Diagnostics (Basel) 2023; 13:2237. [PMID: 37443630 DOI: 10.3390/diagnostics13132237] [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: 05/03/2023] [Revised: 06/20/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023] Open
Abstract
The purpose of this study was to determine whether texture analysis features present on pretreatment unenhanced computed tomography (CT) images, derived from 18F-fluorodeoxyglucose positron emission/computed tomography (18-FDG PET/CT), can predict progression-free survival (PFS), progression-free survival at 24 months (PFS 24), time to next treatment (TTNT), and overall survival in patients with high-tumor-burden follicular lymphoma treated with immunochemotherapy and rituximab maintenance. Seventy-two patients with follicular lymphoma were retrospectively included. Texture analysis was performed on unenhanced CT images extracted from 18-FDG PET/CT examinations that were obtained within one month before treatment. Skewness at a fine texture scale (SSF = 2) was an independent predictor of PFS (hazard ratio = 3.72 (95% CI: 1.15, 12.11), p = 0.028), PFS 24 (hazard ratio = 13.38; 95% CI: 1.29, 138.13; p = 0.029), and TTNT (hazard ratio = 5.11; 95% CI: 1.18, 22.13; p = 0.029). Skewness values above -0.015 at SSF = 2 were significantly associated with lower PFS, PFS 24, and TTNT. Kurtosis without filtration was an independent predictor of PFS (SSF = 0; HR = 1.22 (95% CI: 1.04, 1.44), p = 0.013), and TTNT (SSF = 0; hazard ratio = 1.23; 95% CI: 1.04, 1.46; p = 0.013). This study shows that pretreatment unenhanced CT texture analysis-derived tumor skewness and kurtosis may be used as predictive biomarkers of PFS and TTNT in patients with high-tumor-burden follicular lymphoma treated with immunochemotherapy and rituximab maintenance.
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Affiliation(s)
- Carole Durot
- Department of Radiology, Reims University Hospital, 45 Rue Cognacq-Jay, 51092 Reims, France
| | - Eric Durot
- Department of Hematology, Reims University Hospital, 45 Rue Cognacq-Jay, 51092 Reims, France
| | - Sébastien Mulé
- Department of Radiology, Henri Mondor University Hospital, 51 Avenue du Maréchal de Lattre de Tassigny, 94010 Créteil, France
- Faculté de Médecine, Université Paris-Est Créteil, 61 Avenue du Général de Gaulle, 94000 Créteil, France
| | - David Morland
- Department of Nuclear Medicine, Godinot Institute, 1 Rue du Général Koenig, 51100 Reims, France
- CReSTIC, EA 3804, University of Reims Champagne-Ardenne, UFR Moulin de la Housse, 51867 Reims, France
| | - François Godard
- Department of Radiology, Henri Mondor University Hospital, 51 Avenue du Maréchal de Lattre de Tassigny, 94010 Créteil, France
| | - Anne Quinquenel
- Department of Hematology, Reims University Hospital, 45 Rue Cognacq-Jay, 51092 Reims, France
| | - Alain Delmer
- Department of Hematology, Reims University Hospital, 45 Rue Cognacq-Jay, 51092 Reims, France
| | - Philippe Soyer
- Department of Radiology, Hôpital Cochin, AP-HP, 27 Rue du Faubourg Saint-Jacques, 75014 Paris, France
- Faculté de Médecine, Université Paris Cité, 75006 Paris, France
| | - Christine Hoeffel
- Department of Radiology, Reims University Hospital, 45 Rue Cognacq-Jay, 51092 Reims, France
- CReSTIC, EA 3804, University of Reims Champagne-Ardenne, UFR Moulin de la Housse, 51867 Reims, France
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8
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Tabari A, Cox M, D'Amore B, Mansur A, Dabbara H, Boland G, Gee MS, Daye D. Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma. Cancers (Basel) 2023; 15:2700. [PMID: 37345037 DOI: 10.3390/cancers15102700] [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: 03/14/2023] [Revised: 04/12/2023] [Accepted: 04/27/2023] [Indexed: 06/23/2023] Open
Abstract
Pretreatment LDH is a standard prognostic biomarker for advanced melanoma and is associated with response to ICI. We assessed the role of machine learning-based radiomics in predicting responses to ICI and in complementing LDH for prognostication of metastatic melanoma. From 2008-2022, 79 patients with 168 metastatic hepatic lesions were identified. All patients had arterial phase CT images 1-month prior to initiation of ICI. Response to ICI was assessed on follow-up CT at 3 months using RECIST criteria. A machine learning algorithm was developed using radiomics. Maximum relevance minimum redundancy (mRMR) was used to select features. ROC analysis and logistic regression analyses evaluated performance. Shapley additive explanations were used to identify the variables that are the most important in predicting a response. mRMR selection revealed 15 features that are associated with a response to ICI. The machine learning model combining both radiomics features and pretreatment LDH resulted in better performance for response prediction compared to models that included radiomics or LDH alone (AUC of 0.89 (95% CI: [0.76-0.99]) vs. 0.81 (95% CI: [0.65-0.94]) and 0.81 (95% CI: [0.72-0.91]), respectively). Using SHAP analysis, LDH and two GLSZM were the most predictive of the outcome. Pre-treatment CT radiomic features performed equally well to serum LDH in predicting treatment response.
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Affiliation(s)
- Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02215, USA
| | | | - Brian D'Amore
- Harvard Medical School, Boston, MA 02215, USA
- Department of Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | | | - Harika Dabbara
- Boston University Chobanian & Avedisian School of Medicine, 72 East Concord Street, Boston, MA 02118, USA
| | - Genevieve Boland
- Harvard Medical School, Boston, MA 02215, USA
- Department of Surgery, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
| | - Michael S Gee
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02215, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02215, USA
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9
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Zhou H, Luo Q, Wu W, Li N, Yang C, Zou L. Radiomics-guided checkpoint inhibitor immunotherapy for precision medicine in cancer: A review for clinicians. Front Immunol 2023; 14:1088874. [PMID: 36936913 PMCID: PMC10014595 DOI: 10.3389/fimmu.2023.1088874] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Accepted: 02/16/2023] [Indexed: 03/05/2023] Open
Abstract
Immunotherapy using immune checkpoint inhibitors (ICIs) is a breakthrough in oncology development and has been applied to multiple solid tumors. However, unlike traditional cancer treatment approaches, immune checkpoint inhibitors (ICIs) initiate indirect cytotoxicity by generating inflammation, which causes enlargement of the lesion in some cases. Therefore, rather than declaring progressive disease (PD) immediately, confirmation upon follow-up radiological evaluation after four-eight weeks is suggested according to immune-related Response Evaluation Criteria in Solid Tumors (ir-RECIST). Given the difficulty for clinicians to immediately distinguish pseudoprogression from true disease progression, we need novel tools to assist in this field. Radiomics, an innovative data analysis technique that quantifies tumor characteristics through high-throughput extraction of quantitative features from images, can enable the detection of additional information from early imaging. This review will summarize the recent advances in radiomics concerning immunotherapy. Notably, we will discuss the potential of applying radiomics to differentiate pseudoprogression from PD to avoid condition exacerbation during confirmatory periods. We also review the applications of radiomics in hyperprogression, immune-related biomarkers, efficacy, and immune-related adverse events (irAEs). We found that radiomics has shown promising results in precision cancer immunotherapy with early detection in noninvasive ways.
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Affiliation(s)
- Huijie Zhou
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Qian Luo
- Department of Hematology, the Second Affiliated Hospital Zhejiang University School of Medicine, Zhejiang, China
| | - Wanchun Wu
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Na Li
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Chunli Yang
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
| | - Liqun Zou
- Division of Medical Oncology, Cancer Center and State Key Laboratory of Biotherapy, Sichuan University West China Hospital, Chengdu, China
- *Correspondence: Liqun Zou,
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10
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Guerrisi A, Falcone I, Valenti F, Rao M, Gallo E, Ungania S, Maccallini MT, Fanciulli M, Frascione P, Morrone A, Caterino M. Artificial Intelligence and Advanced Melanoma: Treatment Management Implications. Cells 2022; 11:cells11243965. [PMID: 36552729 PMCID: PMC9777238 DOI: 10.3390/cells11243965] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Revised: 11/29/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022] Open
Abstract
Artificial intelligence (AI), a field of research in which computers are applied to mimic humans, is continuously expanding and influencing many aspects of our lives. From electric cars to search motors, AI helps us manage our daily lives by simplifying functions and activities that would be more complex otherwise. Even in the medical field, and specifically in oncology, many studies in recent years have highlighted the possible helping role that AI could play in clinical and therapeutic patient management. In specific contexts, clinical decisions are supported by "intelligent" machines and the development of specific softwares that assist the specialist in the management of the oncology patient. Melanoma, a highly heterogeneous disease influenced by several genetic and environmental factors, to date is still difficult to manage clinically in its advanced stages. Therapies often fail, due to the establishment of intrinsic or secondary resistance, making clinical decisions complex. In this sense, although much work still needs to be conducted, numerous evidence shows that AI (through the processing of large available data) could positively influence the management of the patient with advanced melanoma, helping the clinician in the most favorable therapeutic choice and avoiding unnecessary treatments that are sure to fail. In this review, the most recent applications of AI in melanoma will be described, focusing especially on the possible finding of this field in the management of drug treatments.
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Affiliation(s)
- Antonino Guerrisi
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy
| | - Italia Falcone
- SAFU, Department of Research, Advanced Diagnostics, and Technological Innovation, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
- Correspondence:
| | - Fabio Valenti
- UOC Oncological Translational Research, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Marco Rao
- Enea-FSN-TECFIS-APAM, C.R. Frascati, via Enrico Fermi, 45, 00146 Rome, Italy
| | - Enzo Gallo
- Pathology Unit, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Sara Ungania
- Medical Physics and Expert Systems Laboratory, Department of Research and Advanced Technologies, IRCCS-Regina Elena Institute, 00144 Rome, Italy
| | - Maria Teresa Maccallini
- Departement of Clinical and Molecular Medicine, Università La Sapienza di Roma, 00185 Rome, Italy
| | - Maurizio Fanciulli
- SAFU, Department of Research, Advanced Diagnostics, and Technological Innovation, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy
| | - Pasquale Frascione
- Oncologic and Preventative Dermatology, IFO-San Gallicano Dermatological Institute-IRCCS, 00144 Rome, Italy
| | - Aldo Morrone
- Scientific Direction, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy
| | - Mauro Caterino
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy
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11
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Ter Maat LS, van Duin IAJ, Elias SG, van Diest PJ, Pluim JPW, Verhoeff JJC, de Jong PA, Leiner T, Veta M, Suijkerbuijk KPM. Imaging to predict checkpoint inhibitor outcomes in cancer. A systematic review. Eur J Cancer 2022; 175:60-76. [PMID: 36096039 DOI: 10.1016/j.ejca.2022.07.034] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 07/17/2022] [Accepted: 07/21/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND Checkpoint inhibition has radically improved the perspective for patients with metastatic cancer, but predicting who will not respond with high certainty remains difficult. Imaging-derived biomarkers may be able to provide additional insights into the heterogeneity in tumour response between patients. In this systematic review, we aimed to summarise and qualitatively assess the current evidence on imaging biomarkers that predict response and survival in patients treated with checkpoint inhibitors in all cancer types. METHODS PubMed and Embase were searched from database inception to 29th November 2021. Articles eligible for inclusion described baseline imaging predictive factors, radiomics and/or imaging machine learning models for predicting response and survival in patients with any kind of malignancy treated with checkpoint inhibitors. Risk of bias was assessed using the QUIPS and PROBAST tools and data was extracted. RESULTS In total, 119 studies including 15,580 patients were selected. Of these studies, 73 investigated simple imaging factors. 45 studies investigated radiomic features or deep learning models. Predictors of worse survival were (i) higher tumour burden, (ii) presence of liver metastases, (iii) less subcutaneous adipose tissue, (iv) less dense muscle and (v) presence of symptomatic brain metastases. Hazard rate ratios did not exceed 2.00 for any predictor in the larger and higher quality studies. The added value of baseline fluorodeoxyglucose positron emission tomography parameters in predicting response to treatment was limited. Pilot studies of radioactive drug tracer imaging showed promising results. Reports on radiomics were almost unanimously positive, but numerous methodological concerns exist. CONCLUSIONS There is well-supported evidence for several imaging biomarkers that can be used in clinical decision making. Further research, however, is needed into biomarkers that can more accurately identify which patients who will not benefit from checkpoint inhibition. Radiomics and radioactive drug labelling appear to be promising approaches for this purpose.
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Affiliation(s)
- Laurens S Ter Maat
- Image Science Institute, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Isabella A J van Duin
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Sjoerd G Elias
- Department of Epidemiology, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Paul J van Diest
- Department of Pathology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Josien P W Pluim
- Image Science Institute, University Medical Center Utrecht, Utrecht, the Netherlands; Medical Image Analysis, Department Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Joost J C Verhoeff
- Department of Radiotherapy, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Pim A de Jong
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Tim Leiner
- Utrecht University, Utrecht, the Netherlands; Department of Radiology, Mayo Clinical, Rochester, MN, USA
| | - Mitko Veta
- Medical Image Analysis, Department Biomedical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands; Utrecht University, Utrecht, the Netherlands
| | - Karijn P M Suijkerbuijk
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht, the Netherlands; Utrecht University, Utrecht, the Netherlands.
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12
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Combination of Whole-Body Baseline CT Radiomics and Clinical Parameters to Predict Response and Survival in a Stage-IV Melanoma Cohort Undergoing Immunotherapy. Cancers (Basel) 2022; 14:cancers14122992. [PMID: 35740659 PMCID: PMC9221470 DOI: 10.3390/cancers14122992] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2022] [Revised: 06/13/2022] [Accepted: 06/15/2022] [Indexed: 11/17/2022] Open
Abstract
Simple Summary The use of immunotherapeutic agents significantly improved stage-IV melanoma patients’ overall progression-free survival. To identify patients who do not benefit from immunotherapy, both clinical parameters and experimental biomarkers such as radiomics are currently being evaluated. However, no radiomic biomarker is widely accepted for routine clinical use. In a large cohort of 262 stage-IV melanoma patients given first-line immunotherapy treatment, we investigated whether radiomics—based on the segmentation of all baseline metastases in the whole body—in combination with clinical parameters offered added value compared to the usage of clinical parameters alone in a machine-learning prediction model. The primary endpoints were response at three months, and survival rates at six and twelve months. The study indicated a potential, but non-significant, added value of radiomics for six-month and twelve-month survival prediction, thus underlining the relevance of clinical parameters. Abstract Background: This study investigated whether a machine-learning-based combination of radiomics and clinical parameters was superior to the use of clinical parameters alone in predicting therapy response after three months, and overall survival after six and twelve months, in stage-IV malignant melanoma patients undergoing immunotherapy with PD-1 checkpoint inhibitors and CTLA-4 checkpoint inhibitors. Methods: A random forest model using clinical parameters (demographic variables and tumor markers = baseline model) was compared to a random forest model using clinical parameters and radiomics (extended model) via repeated 5-fold cross-validation. For this purpose, the baseline computed tomographies of 262 stage-IV malignant melanoma patients treated at a tertiary referral center were identified in the Central Malignant Melanoma Registry, and all visible metastases were three-dimensionally segmented (n = 6404). Results: The extended model was not significantly superior compared to the baseline model for survival prediction after six and twelve months (AUC (95% CI): 0.664 (0.598, 0.729) vs. 0.620 (0.545, 0.692) and AUC (95% CI): 0.600 (0.526, 0.667) vs. 0.588 (0.481, 0.629), respectively). The extended model was not significantly superior compared to the baseline model for response prediction after three months (AUC (95% CI): 0.641 (0.581, 0.700) vs. 0.656 (0.587, 0.719)). Conclusions: The study indicated a potential, but non-significant, added value of radiomics for six-month and twelve-month survival prediction of stage-IV melanoma patients undergoing immunotherapy.
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13
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Ungan G, Lavandier AF, Rouanet J, Hordonneau C, Chauveau B, Pereira B, Boyer L, Garcier JM, Mansard S, Bartoli A, Magnin B. Metastatic melanoma treated by immunotherapy: discovering prognostic markers from radiomics analysis of pretreatment CT with feature selection and classification. Int J Comput Assist Radiol Surg 2022; 17:1867-1877. [PMID: 35650345 DOI: 10.1007/s11548-022-02662-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 04/26/2022] [Indexed: 11/29/2022]
Abstract
PURPOSE Immunotherapy has dramatically improved the prognosis of patients with metastatic melanoma (MM). Yet, there is a lack of biomarkers to predict whether a patient will benefit from immunotherapy. Our aim was to create radiomics models on pretreatment computed tomography (CT) to predict overall survival (OS) and treatment response in patients with MM treated with anti-PD-1 immunotherapy. METHODS We performed a monocentric retrospective analysis of 503 metastatic lesions in 71 patients with 46 radiomics features extracted following lesion segmentation. Predictive accuracies for OS < 1 year versus > 1 year and treatment response versus no response was compared for five feature selection methods (sequential forward selection, recursive, Boruta, relief, random forest) and four classifiers (support vector machine (SVM), random forest, K-nearest neighbor, logistic regression (LR)) used with or without SMOTE data augmentation. A fivefold cross-validation was performed at the patient level, with a tumour-based classification. RESULTS The highest accuracy level for OS predictions was obtained with 3D lesions (0.91) without clinical data integration when combining Boruta feature selection and the LR classifier, The highest accuracy for treatment response prediction was obtained with 3D lesions (0.88) without clinical data integration when combining Boruta feature selection, the LR classifier and SMOTE data augmentation. The accuracy was significantly higher concerning OS prediction with 3D segmentation (0.91 vs 0.86) while clinical data integration led to improved accuracy notably in 2D lesions (0.76 vs 0.87) regarding treatment response prediction. Skewness was the only feature found to be an independent predictor of OS (HR (CI 95%) 1.34, p-value 0.001). CONCLUSION This is the first study to investigate CT texture parameter selection and classification methods for predicting MM prognosis with treatment by immunotherapy. Combining pretreatment CT radiomics features from a single tumor with data selection and classifiers may accurately predict OS and treatment response in MM treated with anti-PD-1.
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Affiliation(s)
- Gulnur Ungan
- EnCoV, Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France
| | - Anne-Flore Lavandier
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Jacques Rouanet
- Dermatology Department, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Constance Hordonneau
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Benoit Chauveau
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Bruno Pereira
- Biostatistics Unit, DRCI, CHU Clermont Ferrand, 58 rue Montalembert, 63000, Clermont-Ferrand, France
| | - Louis Boyer
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Jean-Marc Garcier
- Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France.,Anatomy Department, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France
| | - Sandrine Mansard
- Dermatology Department, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France
| | - Adrien Bartoli
- EnCoV, Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France
| | - Benoit Magnin
- EnCoV, Institut Pascal, UMR 6602 CNRS, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France. .,Department of Medical Imaging, CHU Clermont Ferrand, 1 place Lucie Aubrac, 63100, Clermont-Ferrand, France. .,Anatomy Department, Université Clermont Auvergne, 28 place Henri Dunant, 63000, Clermont-Ferrand, France.
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14
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Doillon M, Durot C, Pluchart C, Marcus C, Djelouah M, Carsin-Vu A. Response to Induction Therapy in Pediatric Hodgkin's Lymphoma: Performance of First-Order Texture Parameters of CT Images. J Belg Soc Radiol 2022; 106:46. [PMID: 35647484 PMCID: PMC9104423 DOI: 10.5334/jbsr.2752] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Accepted: 04/22/2022] [Indexed: 11/20/2022] Open
Abstract
Objectives The aim of this study was to examine whether texture analysis features on pretreatment contrast-enhanced CT images could predict adequate response (AR) or inadequate response (IR) after two cycles of chemotherapy in pediatric Hodgkin's lymphoma (PHL). Materials and methods This retrospective single-center study included 32 children and adolescents with HL. Texture analysis was independently performed by two radiologists using pretreatment CT scans. The mean gray level, standard deviation, entropy, kurtosis, and skewness were derived from pixel distribution histograms before and after spatial filtration ranging from two (fine texture) to six (coarse texture). Interobserver reliability was studied using interobserver correlation coefficients (ICCs) to select texture parameters. Relationships between early response assessment (ERA) to induction therapy and associated factors were studied using Student's t-tests and a lasso penalized logistic regression analysis. Results Of the 32 patients, IR was observed in 13 and AR in 19. Inter-reader agreement was good to excellent (ICC > 0.75) for all parameters except skewness and kurtosis without filtration and at spatial scale filtration (SSF) = 2. These parameters were excluded from the analysis. The t-test identified only entropy at SSF = 2 (p value = 0.039) as a potential predictor of ERA. No parameters were significantly associated with ERA, according to a lasso penalized logistic regression. Conclusion No textural parameters were identified as predictors of ERA after two cycles of chemotherapy in PHL.
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Affiliation(s)
- Margaux Doillon
- Department of Radiology, American Memorial Hospital, 47 rue Cognacq-jay, FR
| | - Carole Durot
- Department of Radiology, American Memorial Hospital, 47 rue Cognacq-jay, FR
| | - Claire Pluchart
- Department of Oncopediatrics, American Memorial Hospital, 47 rue Cognacq-jay, FR
| | - Claude Marcus
- Department of Radiology, American Memorial Hospital, 47 rue Cognac q-jay, FR
| | - Manel Djelouah
- Department of Radiology, American Memorial Hospital, 47 rue Cognacq-jay, FR
| | - Aline Carsin-Vu
- Department of Radiology, American Memorial Hospital, 47 rue Cognacq-jay, FR
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15
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Dercle L, Zhao B, Gönen M, Moskowitz CS, Firas A, Beylergil V, Connors DE, Yang H, Lu L, Fojo T, Carvajal R, Karovic S, Maitland ML, Goldmacher GV, Oxnard GR, Postow MA, Schwartz LH. Early Readout on Overall Survival of Patients With Melanoma Treated With Immunotherapy Using a Novel Imaging Analysis. JAMA Oncol 2022; 8:385-392. [PMID: 35050320 PMCID: PMC8778619 DOI: 10.1001/jamaoncol.2021.6818] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
IMPORTANCE Existing criteria to estimate the benefit of a therapy in patients with cancer rely almost exclusively on tumor size, an approach that was not designed to estimate survival benefit and is challenged by the unique properties of immunotherapy. More accurate prediction of survival by treatment could enhance treatment decisions. OBJECTIVE To validate, using radiomics and machine learning, the performance of a signature of quantitative computed tomography (CT) imaging features for estimating overall survival (OS) in patients with advanced melanoma treated with immunotherapy. DESIGN, SETTING, AND PARTICIPANTS This prognostic study used radiomics and machine learning to retrospectively analyze CT images obtained at baseline and first follow-up and their associated clinical metadata. Data were prospectively collected in the KEYNOTE-002 (Study of Pembrolizumab [MK-3475] Versus Chemotherapy in Participants With Advanced Melanoma; 2017 analysis) and KEYNOTE-006 (Study to Evaluate the Safety and Efficacy of Two Different Dosing Schedules of Pembrolizumab [MK-3475] Compared to Ipilimumab in Participants With Advanced Melanoma; 2016 analysis) multicenter clinical trials. Participants included 575 patients with a diagnosis of advanced melanoma who were randomly assigned to training and validation sets. Data for the present study were collected from November 20, 2012, to June 3, 2019, and analyzed from July 1, 2019, to September 15, 2021. INTERVENTIONS KEYNOTE-002 featured trial groups testing intravenous pembrolizumab, 2 mg/kg or 10 mg/kg every 2 or every 3 weeks based on randomization, or investigator-choice chemotherapy; KEYNOTE-006 featured trial groups testing intravenous ipilimumab, 3 mg/kg every 3 weeks and intravenous pembrolizumab, 10 mg/kg every 2 or 3 weeks based on randomization. MAIN OUTCOMES AND MEASURES The performance of the signature CT imaging features for estimating OS at the month 6 posttreatment landmark in patients who received pembrolizumab was measured using an area under the time-dependent receiver operating characteristics curve (AUC). RESULTS A random forest model combined 25 imaging features extracted from tumors segmented on CT images to identify the combination (signature) that best estimated OS with pembrolizumab in 575 patients. The signature combined 4 imaging features, 2 related to tumor size and 2 reflecting changes in tumor imaging phenotype. In the validation set (287 patients treated with pembrolizumab), the signature reached an AUC for estimation of OS status of 0.92 (95% CI, 0.89-0.95). The standard method, Response Evaluation Criteria in Solid Tumors 1.1, achieved an AUC of 0.80 (95% CI, 0.75-0.84) and classified tumor outcomes as partial or complete response (93 of 287 [32.4%]), stable disease (90 of 287 [31.3%]), or progressive disease (104 of 287 [36.2%]). CONCLUSIONS AND RELEVANCE The findings of this prognostic study suggest that the radiomic signature discerned from conventional CT images at baseline and on first follow-up may be used in clinical settings to provide an accurate early readout of future OS probability in patients with melanoma treated with single-agent programmed cell death 1 blockade.
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Affiliation(s)
- Laurent Dercle
- Department of Radiology, Columbia University Medical Center, New York, New York,Department of Radiology, New York Presbyterian Hospital, New York, New York
| | - Binsheng Zhao
- Department of Radiology, Columbia University Medical Center, New York, New York,Department of Radiology, New York Presbyterian Hospital, New York, New York
| | - Mithat Gönen
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Chaya S. Moskowitz
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Ahmed Firas
- Department of Radiology, Columbia University Medical Center, New York, New York,Department of Radiology, New York Presbyterian Hospital, New York, New York
| | - Volkan Beylergil
- Department of Radiology, Columbia University Medical Center, New York, New York,Department of Radiology, New York Presbyterian Hospital, New York, New York
| | - Dana E. Connors
- Foundation for the National Institutes of Health, North Bethesda, Maryland
| | - Hao Yang
- Department of Radiology, Columbia University Medical Center, New York, New York,Department of Radiology, New York Presbyterian Hospital, New York, New York
| | - Lin Lu
- Department of Radiology, Columbia University Medical Center, New York, New York,Department of Radiology, New York Presbyterian Hospital, New York, New York
| | - Tito Fojo
- Columbia University Herbert Irving Comprehensive Cancer Center, New York, New York
| | - Richard Carvajal
- Columbia University Herbert Irving Comprehensive Cancer Center, New York, New York
| | - Sanja Karovic
- Inova Center for Personalized Health and Schar Cancer Institute, Fairfax, Virginia
| | - Michael L. Maitland
- Inova Center for Personalized Health and Schar Cancer Institute, Fairfax, Virginia,University of Virginia Cancer Center, Charlottesville
| | | | - Geoffrey R. Oxnard
- Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
| | - Michael A. Postow
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York,Department of Medicine, Weill Cornell Medical College, New York, New York
| | - Lawrence H. Schwartz
- Department of Radiology, Columbia University Medical Center, New York, New York,Department of Radiology, New York Presbyterian Hospital, New York, New York
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16
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Park HJ, Qin L, Bakouny Z, Krajewski KM, Van Allen EM, Choueiri TK, Shinagare AB. OUP accepted manuscript. Oncologist 2022; 27:389-397. [PMID: 35348767 PMCID: PMC9074990 DOI: 10.1093/oncolo/oyac034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Accepted: 01/07/2022] [Indexed: 11/15/2022] Open
Abstract
Background Materials and Methods Results Conclusion
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Affiliation(s)
- Hyo Jung Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Republic of Korea
| | - Lei Qin
- Department of Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Ziad Bakouny
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Katherine M Krajewski
- Department of Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Eliezer M Van Allen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Toni K Choueiri
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | - Atul B Shinagare
- Department of Imaging, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Corresponding author: Atul B. Shinagare, Department of Radiology, Brigham and Womens Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA. Tel.: +1 6176322988; Fax: +1 6175828574;
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Cheng S, Jin Z, Xue H. Assessment of Response to Chemotherapy in Pancreatic Cancer with Liver Metastasis: CT Texture as a Predictive Biomarker. Diagnostics (Basel) 2021; 11:diagnostics11122252. [PMID: 34943489 PMCID: PMC8700536 DOI: 10.3390/diagnostics11122252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 11/21/2021] [Accepted: 11/26/2021] [Indexed: 11/16/2022] Open
Abstract
In this paper, we assess changes in CT texture of metastatic liver lesions after treatment with chemotherapy in patients with pancreatic cancer and determine if texture parameters correlate with measured time to progression (TTP). This retrospective study included 110 patients with pancreatic cancer with liver metastasis, and mean, entropy, kurtosis, skewness, mean of positive pixels, and standard deviation (SD) values were extracted during texture analysis. Response assessment was also obtained by using RECIST 1.1, Choi and modified Choi criteria, respectively. The correlation of texture parameters and existing assessment criteria with TTP were evaluated using Kaplan-Meier and Cox regression analyses in the training cohort. Kaplan-Meier curves of the proportion of patients without disease progression were significantly different for several texture parameters, and were better than those for RECIST 1.1-, Choi-, and modified Choi-defined response (p < 0.05 vs. p = 0.398, p = 0.142, and p = 0.536, respectively). Cox regression analysis showed that percentage change in SD was an independent predictor of TTP (p = 0.016) and confirmed in the validation cohort (p = 0.019). In conclusion, CT texture parameters have the potential to become predictive imaging biomarkers for response evaluation in pancreatic cancer with liver metastasis.
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18
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Guerrisi A, Russillo M, Loi E, Ganeshan B, Ungania S, Desiderio F, Bruzzaniti V, Falcone I, Renna D, Ferraresi V, Caterino M, Solivetti FM, Cognetti F, Morrone A. Exploring CT Texture Parameters as Predictive and Response Imaging Biomarkers of Survival in Patients With Metastatic Melanoma Treated With PD-1 Inhibitor Nivolumab: A Pilot Study Using a Delta-Radiomics Approach. Front Oncol 2021; 11:704607. [PMID: 34692481 PMCID: PMC8529867 DOI: 10.3389/fonc.2021.704607] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Accepted: 09/08/2021] [Indexed: 01/08/2023] Open
Abstract
In the era of artificial intelligence and precision medicine, the use of quantitative imaging methodological approaches could improve the cancer patient’s therapeutic approaches. Specifically, our pilot study aims to explore whether CT texture features on both baseline and first post-treatment contrast-enhanced CT may act as a predictor of overall survival (OS) and progression-free survival (PFS) in metastatic melanoma (MM) patients treated with the PD-1 inhibitor Nivolumab. Ninety-four lesions from 32 patients treated with Nivolumab were analyzed. Manual segmentation was performed using a free-hand polygon approach by drawing a region of interest (ROI) around each target lesion (up to five lesions were selected per patient according to RECIST 1.1). Filtration-histogram-based texture analysis was employed using a commercially available research software called TexRAD (Feedback Medical Ltd, London, UK; https://fbkmed.com/texrad-landing-2/) Percentage changes in texture features were calculated to perform delta-radiomics analysis. Texture feature kurtosis at fine and medium filter scale predicted OS and PFS. A higher kurtosis is correlated with good prognosis; kurtosis values greater than 1.11 for SSF = 2 and 1.20 for SSF = 3 were indicators of higher OS (fine texture: 192 HR = 0.56, 95% CI = 0.32–0.96, p = 0.03; medium texture: HR = 0.54, 95% CI = 0.29–0.99, p = 0.04) and PFS (fine texture: HR = 0.53, 95% CI = 0.29–0.95, p = 0.03; medium texture: HR = 0.49, 209 95% CI = 0.25–0.96, p = 0.03). In delta-radiomics analysis, the entropy percentage variation correlated with OS and PFS. Increasing entropy indicates a worse outcome. An entropy variation greater than 5% was an indicator of bad prognosis. CT delta-texture analysis quantified as entropy predicted OS and PFS. Baseline CT texture quantified as kurtosis also predicted survival baseline. Further studies with larger cohorts are mandatory to confirm these promising exploratory results.
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Affiliation(s)
- Antonino Guerrisi
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Rome, Italy
| | - Michelangelo Russillo
- Medical Oncology Unit 1, Department of Clinical and Cancer Research IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Emiliano Loi
- Medical Physics and Expert Systems Laboratory, 3 Department of Research and Advanced Technologies, Istituti Fisioterapici Ospitalieri - IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Balaji Ganeshan
- Institute of Nuclear Medicine, Imaging Department, University College Hospital, London, United Kingdom
| | - Sara Ungania
- Medical Physics and Expert Systems Laboratory, 3 Department of Research and Advanced Technologies, Istituti Fisioterapici Ospitalieri - IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Flora Desiderio
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Rome, Italy
| | - Vicente Bruzzaniti
- Medical Physics and Expert Systems Laboratory, 3 Department of Research and Advanced Technologies, Istituti Fisioterapici Ospitalieri - IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Italia Falcone
- Medical Oncology Unit 1, Department of Clinical and Cancer Research IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Davide Renna
- Medical Oncology 1, IRCCS-Regina Elena National Cancer Institute, Rome, Italy
| | - Virginia Ferraresi
- Medical Oncology Unit 1, Department of Clinical and Cancer Research IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Mauro Caterino
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Rome, Italy
| | - Francesco Maria Solivetti
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, Rome, Italy
| | - Francesco Cognetti
- Medical Oncology Unit 1, Department of Clinical and Cancer Research IRCCS Regina Elena National Cancer Institute, Rome, Italy
| | - Aldo Morrone
- Scientific Director, San Gallicano Dermatological Institute IRCCS, Rome, Italy
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Bonnin A, Durot C, Barat M, Djelouah M, Grange F, Mulé S, Soyer P, Hoeffel C. CT texture analysis as a predictor of favorable response to anti-PD1 monoclonal antibodies in metastatic skin melanoma. Diagn Interv Imaging 2021; 103:97-102. [PMID: 34666945 DOI: 10.1016/j.diii.2021.09.009] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2021] [Revised: 09/24/2021] [Accepted: 09/24/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE The purpose of this study was to determine whether texture analysis features on pretreatment contrast-enhanced computed tomography (CT) images and their evolution can predict treatment response of metastatic skin melanoma (SM) treated with anti-PD1 monoclonal antibodies. MATERIALS AND METHODS Sixty patients (29 men, 31 women; median age, 56 years; age range: 27-91 years) with metastatic SM treated with pembrolizumab (43/60; 72%) or nivolumab (17/60; 28%) were included. Texture analysis of SM metastases was performed on baseline and first post-treatment evaluation CT examinations. Mean gray-level, entropy, kurtosis, skewness, and standard deviation values were derived from the pixel distribution histogram before and after spatial filtration at different anatomic scales, ranging from fine to coarse. Lasso penalized Cox regression analyses were performed to identify independent variables associated with favorable response to treatment. RESULTS A total of 127 metastases were analyzed, with a median of two metastases per patient. Skewness at fine texture scale (spatial scale filtration [SSF] = 2; Hazard ratio [HR]: 3.51; 95% CI: 2.08-8.57; P = 0.010), skewness at medium texture scale (SSF = 3; HR: 0.56; 95% CI: 0.11-1.59; P = 0.014), variation of entropy at fine texture scale (SSF = 2; HR: 37.76; 95% CI: 3.48-496.22; P = 0.008) and LDH above the threshold of 248 UI/L (HR: 3.56; 95% CI: 1.78-21.35; P = 0.032] were independent predictors of response to treatment. CONCLUSION Pretreatment CT texture analysis-derived tumor skewness and variation of entropy between baseline and first control CT examination may be used as predictors of favorable response to anti-PD1 monoclonal antibodies in patients with metastatic SM.
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Affiliation(s)
- Angèle Bonnin
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France; Department of Radiology, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, Faculté de Médecine, 75006 Paris, France
| | - Carole Durot
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France
| | - Maxime Barat
- Department of Radiology, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, Faculté de Médecine, 75006 Paris, France
| | - Manel Djelouah
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France
| | - Florent Grange
- Department of Dermatology, Valence Hospital, 26000 Valence, France
| | - Sébastien Mulé
- Department of Radiology, Henri Mondor University Hospital, APH-HP, 94000 Créteil, France
| | - Philippe Soyer
- Department of Radiology, Cochin Hospital, AP-HP, 75014 Paris, France; Université de Paris, Faculté de Médecine, 75006 Paris, France
| | - Christine Hoeffel
- Department of Abdominal Radiology, Reims University Hospital, 51092 Reims, France; CRESTIC, Reims Champagne-Ardenne University, 51000 Reims, France.
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20
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Flaus A, Habouzit V, De Leiris N, Vuillez JP, Leccia MT, Perrot JL, Prevot N, Cachin F. FDG PET biomarkers for prediction of survival in metastatic melanoma prior to anti-PD1 immunotherapy. Sci Rep 2021; 11:18795. [PMID: 34552135 PMCID: PMC8458464 DOI: 10.1038/s41598-021-98310-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 08/31/2021] [Indexed: 12/22/2022] Open
Abstract
Our aim was to analyse whether biomarkers extracted from baseline 18F-FDG PET before anti-PD1 treatment contribute to prognostic survival information for early risk stratification in metastatic melanoma. Fifty-six patients, without prior systemic treatment, BRAF wild type, explored using 18F-FDG PET were included retrospectively. Our primary endpoint was overall survival (OS). Total metabolic tumoral volume (MTV) and forty-one IBSI compliant parameters were extracted from PET. Parameters associated with outcome were evaluated by a cox regression model and when significant helped build a prognostic score. Median follow-up was 22.1 months and 21 patients died. Total MTV and long zone emphasis (LZE) correlated with shorter OS and served to define three risk categories for the prognostic score. For low, intermediate and high risk groups, survival rates were respectively 91.1% (IC 95 80–1), 56.1% (IC 95 37.1–85) and 19% (IC 95 0.06–60.2) and hazard ratios were respectively 0.11 (IC 95 0.025–0.46), P = 0.0028, 1.2 (IC 95 0.48–2.8), P = 0.74 and 5.9 (IC 95 2.5–14), P < 0.0001. To conclude, a prognostic score based on total MTV and LZE separated metastatic melanoma patients in 3 categories with dramatically different outcomes. Innovative therapies should be tested in the group with the lowest prognosis score for future clinical trials.
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Affiliation(s)
- A Flaus
- Nuclear Medecine Department, Saint-Etienne University Hospital, University of Saint-Etienne, Saint-Etienne, France. .,Nuclear Medicine Department, East Group Hospital, Hospices Civils de Lyon, Lyon, France. .,Service de Medecine Nucléaire, Hôpital Nord, CHU de Saint-Etienne, 42 055, Saint-Etienne, Cedex 2, France.
| | - V Habouzit
- Nuclear Medecine Department, Saint-Etienne University Hospital, University of Saint-Etienne, Saint-Etienne, France
| | - N De Leiris
- Nuclear Medecine Department, CHU Grenoble Alpes, University Grenoble Alpes, Grenoble, France.,Laboratoire Radiopharmaceutiques Biocliniques, University Grenoble Alpes, INSERM, CHU Grenoble Alpes, 38000, Grenoble, France
| | - J P Vuillez
- Nuclear Medecine Department, CHU Grenoble Alpes, University Grenoble Alpes, Grenoble, France.,Laboratoire Radiopharmaceutiques Biocliniques, University Grenoble Alpes, INSERM, CHU Grenoble Alpes, 38000, Grenoble, France
| | - M T Leccia
- Dermatology Department, CHU Grenoble Alpes, University Grenoble Alpes, Grenoble, France
| | - J L Perrot
- Dermatology Department, Saint-Etienne University Hospital, University of Saint-Etienne, Saint-Etienne, France
| | - N Prevot
- Nuclear Medecine Department, Saint-Etienne University Hospital, University of Saint-Etienne, Saint-Etienne, France
| | - F Cachin
- Nuclear Medicine Department, Jean Perrin Cancer Center of Clermont-Ferrand, Clermont-Ferrand, France
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Khene ZE, Kokorian R, Mathieu R, Gasmi A, Nathalie RL, Solène-Florence KJ, Shariat S, de Crevoisier R, Laguerre B, Bensalah K. Metastatic clear cell renal cell carcinoma: computed tomography texture analysis as predictive biomarkers of survival in patients treated with nivolumab. Int J Clin Oncol 2021; 26:2087-2093. [PMID: 34338919 DOI: 10.1007/s10147-021-02003-w] [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: 04/07/2021] [Accepted: 07/26/2021] [Indexed: 11/24/2022]
Abstract
INTRODUCTION To evaluate the value of image-based texture analysis for predicting progression-free survival (PFS) and overall survival (OS) in patients with metastatic clear cell renal carcinoma (cCCR) treated with nivolumab. METHODS This retrospective study included 48 patients with metastatic cCCR treated with nivolumab. Nivolumab was used as a second- or third-line monotherapy. Texture analysis of metastatic lesions was performed on CT scanners obtained within 1 month before treatment. Texture features related to the gray-level histogram, gray-level co-occurrence, run-length matrix features, autoregressive model features, and Haar wavelet feature were extracted. Lasso penalized Cox regression analyses were performed to identify independent predictors of PFS and OS. RESULTS Median PFS and OS were 5.7 and 13.8 months. 39 patients experienced progression and 27 died. The Lasso penalized Cox regression analysis identified three texture parameters as potential predictors of PFS: skewness, S.2.2. Correlat and S.1.1. SumVarnc. Multivariate Cox regression analysis confirmed skewness (HR (95% CI) 1.49 [1.21-1.85], p < 0.001) as an independent predictor of PFS. Regarding OS, the Lasso penalized Cox regression analysis identified three texture parameters as potential predictors of OS: S20SumVarnc, S22Contrast and S22Entropy. Multivariate Cox regression analysis confirmed S22Entropy (HR (95% CI) 1.68 (1.31-2.14), p < 0.001) as an independent predictor of OS. CONCLUSIONS Results from this preliminary study suggest that CT texture analysis might be a promising quantitative imaging tool that predicts oncological outcomes after starting nivolumab treatment.
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Affiliation(s)
- Zine-Eddine Khene
- Department of Urology, Rennes University Hospital, Rennes, France. .,Department of Medical Oncology, Centre Eugene Marquis, Rennes, France. .,LTSI, Inserm U1099, Université de Rennes 1, Rennes, France.
| | - Romain Kokorian
- Department of Medical Oncology, Centre Eugene Marquis, Rennes, France
| | - Romain Mathieu
- Department of Urology, Rennes University Hospital, Rennes, France
| | - Anis Gasmi
- Department of Urology, Rennes University Hospital, Rennes, France
| | | | | | - Shahrokh Shariat
- Department of Urology, Medical University Vienna, General Hospital, Vienna, Austria
| | - Renaud de Crevoisier
- Department of Medical Oncology, Centre Eugene Marquis, Rennes, France.,LTSI, Inserm U1099, Université de Rennes 1, Rennes, France
| | - Brigitte Laguerre
- Department of Medical Oncology, Centre Eugene Marquis, Rennes, France
| | - Karim Bensalah
- Department of Urology, Rennes University Hospital, Rennes, France.,LTSI, Inserm U1099, Université de Rennes 1, Rennes, France
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Ganeshan B, Miles K, Afaq A, Punwani S, Rodriguez M, Wan S, Walls D, Hoy L, Khan S, Endozo R, Shortman R, Hoath J, Bhargava A, Hanson M, Francis D, Arulampalam T, Dindyal S, Chen SH, Ng T, Groves A. Texture Analysis of Fractional Water Content Images Acquired during PET/MRI: Initial Evidence for an Association with Total Lesion Glycolysis, Survival and Gene Mutation Profile in Primary Colorectal Cancer. Cancers (Basel) 2021; 13:2715. [PMID: 34072712 PMCID: PMC8199380 DOI: 10.3390/cancers13112715] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 05/19/2021] [Accepted: 05/21/2021] [Indexed: 01/07/2023] Open
Abstract
To assess the capability of fractional water content (FWC) texture analysis (TA) to generate biologically relevant information from routine PET/MRI acquisitions for colorectal cancer (CRC) patients. Thirty consecutive primary CRC patients (mean age 63.9, range 42-83 years) prospectively underwent FDG-PET/MRI. FWC tumor parametric images generated from Dixon MR sequences underwent TA using commercially available research software (TexRAD). Data analysis comprised (1) identification of functional imaging correlates for texture features (TF) with low inter-observer variability (intraclass correlation coefficient: ICC > 0.75), (2) evaluation of prognostic performance for FWC-TF, and (3) correlation of prognostic imaging signatures with gene mutation (GM) profile. Of 32 FWC-TF with ICC > 0.75, 18 correlated with total lesion glycolysis (TLG, highest: rs = -0.547, p = 0.002). Using optimized cut-off values, five MR FWC-TF identified a good prognostic group with zero mortality (lowest: p = 0.017). For the most statistically significant prognostic marker, favorable prognosis was significantly associated with a higher number of GM per patient (medians: 7 vs. 1.5, p = 0.009). FWC-TA derived from routine PET/MRI Dixon acquisitions shows good inter-operator agreement, generates biological relevant information related to TLG, GM count, and provides prognostic information that can unlock new clinical applications for CRC patients.
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Affiliation(s)
- Balaji Ganeshan
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Kenneth Miles
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Asim Afaq
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
- Department of Radiology, Carver College of Medicine, University of Iowa, Iowa City, IA 52242, USA
| | - Shonit Punwani
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Manuel Rodriguez
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - Simon Wan
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - Darren Walls
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Luke Hoy
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Saif Khan
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - Raymond Endozo
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - Robert Shortman
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - John Hoath
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
| | - Aman Bhargava
- Institute of Health Barts and London Medical School, Queen Mary University of London (QMUL), London E1 2AD, UK;
| | - Matthew Hanson
- Division of Cancer and Clinical Support, Barking, Havering and Redbridge University Hospitals NHS Trust, Queens and King George Hospitals, Essex IG3 8YB, UK;
| | - Daren Francis
- Department of Colorectal Surgery, Royal Free London NHS Foundation Trust, Barnet and Chase Farm Hospitals, London NW3 2QG, UK;
| | - Tan Arulampalam
- Department of Surgery, East Suffolk and North Essex NHS Foundation Trust, Colchester General Hospital, Colchester CO4 5JL, UK;
| | - Sanjay Dindyal
- Imaging Division, Surgery and Cancer Board, University College London Hospitals (UCLH) NHS Foundation Trust, University College Hospital (UCH), London NW1 2BU, UK; (A.A.); (M.R.); (S.W.); (S.K.); (R.E.); (R.S.); (S.D.)
| | - Shih-Hsin Chen
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
- Department of Nuclear Medicine, Keelung Chang Gung Memorial Hospital, Keelung 204, Taiwan
| | - Tony Ng
- School of Cancer & Pharmaceutical Sciences, King’s College London (KCL), London WC2R 2LS, UK;
| | - Ashley Groves
- Research Department of Imaging, Division of Medicine, University College London (UCL), London WC1E 6BT, UK; (K.M.); (S.P.); (D.W.); (L.H.); (J.H.); (S.-H.C.); (A.G.)
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Barat M, Guegan-Bart S, Cottereau AS, Guillo E, Hoeffel C, Barret M, Gaujoux S, Dohan A, Soyer P. CT, MRI and PET/CT features of abdominal manifestations of cutaneous melanoma: a review of current concepts in the era of tumor-specific therapies. Abdom Radiol (NY) 2021; 46:2219-2235. [PMID: 33135115 DOI: 10.1007/s00261-020-02837-4] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 10/15/2020] [Accepted: 10/20/2020] [Indexed: 12/12/2022]
Abstract
Abdominal manifestations in patients with cutaneous melanoma include involvement due to metastatic spread and immune checkpoint inhibitor induced adverse events. The purpose of this review is to provide a critical overview of abdominal manifestations in patients with cutaneous melanoma and highlight the current imaging challenges in the era of tumor-specific therapies. Immune checkpoint inhibitors represent a treatment with demonstrated efficacy in the treatment of advanced cutaneous melanoma but are associated with several abdominal adverse events that must be recognized. CT has a role in the identification of colitis, enteritis and pancreatitis, whereas MRI has an important role in the diagnosis of autoimmune pancreatitis. Current evidence demonstrates that MRI should be the preferred imaging technique for the detection and characterization of hepatic and splenic metastases from cutaneous melanoma. The role of 18F-FDG-PET/CT should be further evaluated but current literature suggests an efficacy in the detection of pancreatic metastases not seen on CT and MRI.
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Affiliation(s)
- Maxime Barat
- Department of Abdominal & Interventional Radiology, Hôpital Cochin, AP-HP, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France
- Université de Paris, Descartes-Paris 5, rue de l'Ecole de Médecine, F-75006, Paris, France
| | - Sarah Guegan-Bart
- Université de Paris, Descartes-Paris 5, rue de l'Ecole de Médecine, F-75006, Paris, France
- Department of Dermatology, Hôpital Cochin, AP-HP, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France
| | - Anne-Ségolène Cottereau
- Université de Paris, Descartes-Paris 5, rue de l'Ecole de Médecine, F-75006, Paris, France
- Department of Nuclear Medicine, Hôpital Cochin, AP-HP, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France
| | - Enora Guillo
- Department of Abdominal & Interventional Radiology, Hôpital Cochin, AP-HP, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France
| | - Christine Hoeffel
- Department of Radiology, Hôpital Robert Debré, 11 Boulevard Pasteur, 51092, Reims, France
| | - Maximilien Barret
- Université de Paris, Descartes-Paris 5, rue de l'Ecole de Médecine, F-75006, Paris, France
- Department of Gastroenterology, Hôpital Cochin, AP-HP, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France
| | - Sébastien Gaujoux
- Université de Paris, Descartes-Paris 5, rue de l'Ecole de Médecine, F-75006, Paris, France
- Department of Abdominal Surgery, Hôpital Cochin, AP-HP, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France
| | - Anthony Dohan
- Department of Abdominal & Interventional Radiology, Hôpital Cochin, AP-HP, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France
- Université de Paris, Descartes-Paris 5, rue de l'Ecole de Médecine, F-75006, Paris, France
| | - Philippe Soyer
- Department of Abdominal & Interventional Radiology, Hôpital Cochin, AP-HP, 27 Rue du Faubourg Saint-Jacques, 75014, Paris, France.
- Université de Paris, Descartes-Paris 5, rue de l'Ecole de Médecine, F-75006, Paris, France.
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Valenti F, Falcone I, Ungania S, Desiderio F, Giacomini P, Bazzichetto C, Conciatori F, Gallo E, Cognetti F, Ciliberto G, Morrone A, Guerrisi A. Precision Medicine and Melanoma: Multi-Omics Approaches to Monitoring the Immunotherapy Response. Int J Mol Sci 2021; 22:3837. [PMID: 33917181 PMCID: PMC8067863 DOI: 10.3390/ijms22083837] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 03/18/2021] [Accepted: 03/31/2021] [Indexed: 12/15/2022] Open
Abstract
The treatment and management of patients with metastatic melanoma have evolved considerably in the "era" of personalized medicine. Melanoma was one of the first solid tumors to benefit from immunotherapy; life expectancy for patients in advanced stage of disease has improved. However, many progresses have yet to be made considering the (still) high number of patients who do not respond to therapies or who suffer adverse events. In this scenario, precision medicine appears fundamental to direct the most appropriate treatment to the single patient and to guide towards treatment decisions. The recent multi-omics analyses (genomics, transcriptomics, proteomics, metabolomics, radiomics, etc.) and the technological evolution of data interpretation have allowed to identify and understand several processes underlying the biology of cancer; therefore, improving the tumor clinical management. Specifically, these approaches have identified new pharmacological targets and potential biomarkers used to predict the response or adverse events to treatments. In this review, we will analyze and describe the most important omics approaches, by evaluating the methodological aspects and progress in melanoma precision medicine.
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Affiliation(s)
- Fabio Valenti
- Oncogenomics and Epigenetics, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy; (F.V.); (P.G.)
| | - Italia Falcone
- Medical Oncology 1, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy; (I.F.); (C.B.); (F.C.); (F.C.)
| | - Sara Ungania
- Medical Physics and Expert Systems Laboratory, Department of Research and Advanced Technologies, IRCCS-Regina Elena Institute, 00144 Rome, Italy;
| | - Flora Desiderio
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy;
| | - Patrizio Giacomini
- Oncogenomics and Epigenetics, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy; (F.V.); (P.G.)
| | - Chiara Bazzichetto
- Medical Oncology 1, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy; (I.F.); (C.B.); (F.C.); (F.C.)
| | - Fabiana Conciatori
- Medical Oncology 1, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy; (I.F.); (C.B.); (F.C.); (F.C.)
| | - Enzo Gallo
- Pathology Unit, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy;
| | - Francesco Cognetti
- Medical Oncology 1, IRCCS-Regina Elena National Cancer Institute, 00144 Rome, Italy; (I.F.); (C.B.); (F.C.); (F.C.)
| | - Gennaro Ciliberto
- Scientific Direction IRCSS-Regina Elena National Cancer Institute, 00144 Rome, Italy;
| | - Aldo Morrone
- Scientific Direction, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy;
| | - Antonino Guerrisi
- Radiology and Diagnostic Imaging Unit, Department of Clinical and Dermatological Research, San Gallicano Dermatological Institute IRCCS, 00144 Rome, Italy;
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25
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Angus L, Starmans MPA, Rajicic A, Odink AE, Jalving M, Niessen WJ, Visser JJ, Sleijfer S, Klein S, van der Veldt AAM. The BRAF P.V600E Mutation Status of Melanoma Lung Metastases Cannot Be Discriminated on Computed Tomography by LIDC Criteria nor Radiomics Using Machine Learning. J Pers Med 2021; 11:257. [PMID: 33915880 PMCID: PMC8066683 DOI: 10.3390/jpm11040257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/18/2021] [Accepted: 03/24/2021] [Indexed: 11/05/2022] Open
Abstract
Patients with BRAF mutated (BRAF-mt) metastatic melanoma benefit significantly from treatment with BRAF inhibitors. Currently, the BRAF status is determined on archival tumor tissue or on fresh tumor tissue from an invasive biopsy. The aim of this study was to evaluate whether radiomics can predict the BRAF status in a non-invasive manner. Patients with melanoma lung metastases, known BRAF status, and a pretreatment computed tomography scan were included. After semi-automatic annotation of the lung lesions (maximum two per patient), 540 radiomics features were extracted. A chest radiologist scored all segmented lung lesions according to the Lung Image Database Consortium (LIDC) criteria. Univariate analysis was performed to assess the predictive value of each feature for BRAF mutation status. A combination of various machine learning methods was used to develop BRAF decision models based on the radiomics features and LIDC criteria. A total of 169 lung lesions from 103 patients (51 BRAF-mt; 52 BRAF wild type) were included. There were no features with a significant discriminative value in the univariate analysis. Models based on radiomics features and LIDC criteria both performed as poorly as guessing. Hence, the BRAF mutation status in melanoma lung metastases cannot be predicted using radiomics features or visually scored LIDC criteria.
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Affiliation(s)
- Lindsay Angus
- Department of Medical Oncology, Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands; (A.R.); (S.S.); (A.A.M.v.d.V.)
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (M.P.A.S.); (A.E.O.); (W.J.N.); (J.J.V.); (S.K.)
| | - Martijn P. A. Starmans
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (M.P.A.S.); (A.E.O.); (W.J.N.); (J.J.V.); (S.K.)
- Department of Medical Informatics, Erasmus MC, 3015 GD Rotterdam, The Netherlands
| | - Ana Rajicic
- Department of Medical Oncology, Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands; (A.R.); (S.S.); (A.A.M.v.d.V.)
| | - Arlette E. Odink
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (M.P.A.S.); (A.E.O.); (W.J.N.); (J.J.V.); (S.K.)
| | - Mathilde Jalving
- Department of Medical Oncology, University Medical Center Groningen, University of Groningen, 9713 GZ Groningen, The Netherlands;
| | - Wiro J. Niessen
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (M.P.A.S.); (A.E.O.); (W.J.N.); (J.J.V.); (S.K.)
- Department of Medical Informatics, Erasmus MC, 3015 GD Rotterdam, The Netherlands
- Faculty of Applied Sciences, Delft University of Technology, 2628 CJ Delft, The Netherlands
| | - Jacob J. Visser
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (M.P.A.S.); (A.E.O.); (W.J.N.); (J.J.V.); (S.K.)
| | - Stefan Sleijfer
- Department of Medical Oncology, Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands; (A.R.); (S.S.); (A.A.M.v.d.V.)
| | - Stefan Klein
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (M.P.A.S.); (A.E.O.); (W.J.N.); (J.J.V.); (S.K.)
- Department of Medical Informatics, Erasmus MC, 3015 GD Rotterdam, The Netherlands
| | - Astrid A. M. van der Veldt
- Department of Medical Oncology, Erasmus MC Cancer Institute, 3015 GD Rotterdam, The Netherlands; (A.R.); (S.S.); (A.A.M.v.d.V.)
- Department of Radiology and Nuclear Medicine, Erasmus MC, 3015 GD Rotterdam, The Netherlands; (M.P.A.S.); (A.E.O.); (W.J.N.); (J.J.V.); (S.K.)
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CT based radiomic approach on first line pembrolizumab in lung cancer. Sci Rep 2021; 11:6633. [PMID: 33758304 PMCID: PMC7988058 DOI: 10.1038/s41598-021-86113-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2020] [Accepted: 02/24/2021] [Indexed: 02/06/2023] Open
Abstract
Clinical evaluation poorly predicts outcomes in lung cancer treated with immunotherapy. The aim of the study is to assess whether CT-derived texture parameters can predict overall survival (OS) and progression-free survival (PFS) in patients with advanced non-small-cell lung cancer (NSCLC) treated with first line Pembrolizumab. Twenty-one patients with NSLC were prospectively enrolled; they underwent contrast enhanced CT (CECT) at baseline and during Pembrolizumab treatment. Response to therapy was assessed both with clinical and iRECIST criteria. Two radiologists drew a volume of interest of the tumor at baseline CECT, extracting several texture parameters. ROC curves, a univariate Kaplan-Meyer analysis and Cox proportional analysis were performed to evaluate the prognostic value of texture analysis. Twelve (57%) patients showed partial response to therapy while nine (43%) had confirmed progressive disease. Among texture parameters, mean value of positive pixels (MPP) at fine and medium filters showed an AUC of 72% and 74% respectively (P < 0.001). Kaplan-Meyer analysis showed that MPP < 56.2 were significantly associated with lower OS and PFS (P < 0.0035). Cox proportional analysis showed a significant correlation between MPP4 and OS (P = 0.0038; HR = 0.89[CI 95%:0.83,0.96]). In conclusion, MPP could be used as predictive imaging biomarkers of OS and PFS in patients with NSLC with first line immune treatment.
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Abstract
With the ongoing advances in imaging techniques, increasing volumes of anatomical and functional data are being generated as part of the routine clinical workflow. This surge of available imaging data coincides with increasing research in quantitative imaging, particularly in the domain of imaging features. An important and novel approach is radiomics, where high-dimensional image properties are extracted from routine medical images. The fundamental principle of radiomics is the hypothesis that biomedical images contain predictive information, not discernible to the human eye, that can be mined through quantitative image analysis. In this review, a general outline of radiomics and artificial intelligence (AI) will be provided, along with prominent use cases in immunotherapy (e.g. response and adverse event prediction) and targeted therapy (i.e. radiogenomics). While the increased use and development of radiomics and AI in immuno-oncology is highly promising, the technology is still in its early stages, and different challenges still need to be overcome. Nevertheless, novel AI algorithms are being constructed with an ever-increasing scope of applications.
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Affiliation(s)
- Z. Bodalal
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - I. Wamelink
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- Technical Medicine, University of Twente, Enschede, The Netherlands
| | - S. Trebeschi
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - R.G.H. Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
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Gill AB, Rundo L, Wan JCM, Lau D, Zawaideh JP, Woitek R, Zaccagna F, Beer L, Gale D, Sala E, Couturier DL, Corrie PG, Rosenfeld N, Gallagher FA. Correlating Radiomic Features of Heterogeneity on CT with Circulating Tumor DNA in Metastatic Melanoma. Cancers (Basel) 2020; 12:E3493. [PMID: 33255267 PMCID: PMC7759931 DOI: 10.3390/cancers12123493] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 11/17/2020] [Indexed: 12/18/2022] Open
Abstract
Clinical imaging methods, such as computed tomography (CT), are used for routine tumor response monitoring. Imaging can also reveal intratumoral, intermetastatic, and interpatient heterogeneity, which can be quantified using radiomics. Circulating tumor DNA (ctDNA) in the plasma is a sensitive and specific biomarker for response monitoring. Here we evaluated the interrelationship between circulating tumor DNA mutant allele fraction (ctDNAmaf), obtained by targeted amplicon sequencing and shallow whole genome sequencing, and radiomic measurements of CT heterogeneity in patients with stage IV melanoma. ctDNAmaf and radiomic observations were obtained from 15 patients with a total of 70 CT examinations acquired as part of a prospective trial. 26 of 39 radiomic features showed a significant relationship with log(ctDNAmaf). Principal component analysis was used to define a radiomics signature that predicted ctDNAmaf independent of lesion volume. This radiomics signature and serum lactate dehydrogenase were independent predictors of ctDNAmaf. Together, these results suggest that radiomic features and ctDNAmaf may serve as complementary clinical tools for treatment monitoring.
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Affiliation(s)
- Andrew B. Gill
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; (L.R.); (D.L.); (J.P.Z.); (R.W.); (F.Z.); (L.B.); (E.S.); (F.A.G.)
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; (D.G.); (N.R.)
- Imaging Department, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
| | - Leonardo Rundo
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; (L.R.); (D.L.); (J.P.Z.); (R.W.); (F.Z.); (L.B.); (E.S.); (F.A.G.)
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; (D.G.); (N.R.)
| | - Jonathan C. M. Wan
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK; (J.C.M.W.); (D.-L.C.)
| | - Doreen Lau
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; (L.R.); (D.L.); (J.P.Z.); (R.W.); (F.Z.); (L.B.); (E.S.); (F.A.G.)
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; (D.G.); (N.R.)
| | - Jeries P. Zawaideh
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; (L.R.); (D.L.); (J.P.Z.); (R.W.); (F.Z.); (L.B.); (E.S.); (F.A.G.)
| | - Ramona Woitek
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; (L.R.); (D.L.); (J.P.Z.); (R.W.); (F.Z.); (L.B.); (E.S.); (F.A.G.)
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; (D.G.); (N.R.)
| | - Fulvio Zaccagna
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; (L.R.); (D.L.); (J.P.Z.); (R.W.); (F.Z.); (L.B.); (E.S.); (F.A.G.)
| | - Lucian Beer
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; (L.R.); (D.L.); (J.P.Z.); (R.W.); (F.Z.); (L.B.); (E.S.); (F.A.G.)
| | - Davina Gale
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; (D.G.); (N.R.)
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK; (J.C.M.W.); (D.-L.C.)
| | - Evis Sala
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; (L.R.); (D.L.); (J.P.Z.); (R.W.); (F.Z.); (L.B.); (E.S.); (F.A.G.)
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; (D.G.); (N.R.)
| | - Dominique-Laurent Couturier
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK; (J.C.M.W.); (D.-L.C.)
| | - Pippa G. Corrie
- Department of Oncology, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK;
| | - Nitzan Rosenfeld
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; (D.G.); (N.R.)
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge CB2 0RE, UK; (J.C.M.W.); (D.-L.C.)
| | - Ferdia A. Gallagher
- Department of Radiology, University of Cambridge, Cambridge CB2 0QQ, UK; (L.R.); (D.L.); (J.P.Z.); (R.W.); (F.Z.); (L.B.); (E.S.); (F.A.G.)
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge CB2 0RE, UK; (D.G.); (N.R.)
- Imaging Department, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
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29
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García-Figueiras R, Baleato-González S, Luna A, Muñoz-Iglesias J, Oleaga L, Vallejo Casas JA, Martín-Noguerol T, Broncano J, Areses MC, Vilanova JC. Assessing Immunotherapy with Functional and Molecular Imaging and Radiomics. Radiographics 2020; 40:1987-2010. [PMID: 33035135 DOI: 10.1148/rg.2020200070] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Immunotherapy is changing the treatment paradigm for cancer and has introduced new challenges in medical imaging. Because not all patients benefit from immunotherapy, pretreatment imaging should be performed to identify not only prognostic factors but also factors that allow prediction of response to immunotherapy. Follow-up studies must allow detection of nonresponders, without confusion of pseudoprogression with real progression to prevent premature discontinuation of treatment that can benefit the patient. Conventional imaging techniques and classic tumor response criteria are limited for the evaluation of the unusual patterns of response that arise from the specific mechanisms of action of immunotherapy, so advanced imaging methods must be developed to overcome these shortcomings. The authors present the fundamentals of the tumor immune microenvironment and immunotherapy and how they influence imaging findings. They also discuss advances in functional and molecular imaging techniques for the assessment of immunotherapy in clinical practice, including their use to characterize immune phenotypes, assess patient prognosis and response to therapy, and evaluate immune-related adverse events. Finally, the development of radiomics and radiogenomics in these therapies and the future role of imaging biomarkers for immunotherapy are discussed. Online supplemental material is available for this article. ©RSNA, 2020.
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Affiliation(s)
- Roberto García-Figueiras
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - Sandra Baleato-González
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - Antonio Luna
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - José Muñoz-Iglesias
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - Laura Oleaga
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - Juan Antonio Vallejo Casas
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - Teodoro Martín-Noguerol
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - Jordi Broncano
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - María Carmen Areses
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
| | - Joan C Vilanova
- From the Department of Radiology, Oncologic Imaging, Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706, Santiago de Compostela, Spain (R.G.F., S.B.G.); Department of Radiology, HT Medica, Jaén, Spain (A.L, J.B.); Department of Nuclear Medicine, Complexo Hospitalario Universitario de Vigo, Vigo, Spain (J.M.I.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.); Unidad de Gestión Clínica de Medicina Nuclear, Hospital Universitario Reina Sofía de Córdoba, Córdoba, Spain (J.A.V.C.); MRI Unit, HT Medica, Jaén, Spain (T.M.N.); Department of Medical Oncology, Complexo Hospitalario Universitario de Ourense, Ourense, Spain (M.C.A.); and Department of Radiology, Clínica Girona, Institute of Diagnostic Imaging, Girona, Spain (J.C.V.)
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Han R, Arjal R, Dong J, Jiang H, Liu H, Zhang D, Huang L. Three dimensional texture analysis of noncontrast chest CT in differentiating solitary solid lung squamous cell carcinoma from adenocarcinoma and correlation to immunohistochemical markers. Thorac Cancer 2020; 11:3099-3106. [PMID: 32945092 PMCID: PMC7605991 DOI: 10.1111/1759-7714.13592] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2020] [Revised: 07/07/2020] [Accepted: 07/08/2020] [Indexed: 11/30/2022] Open
Abstract
Background The aim of the study was to investigate 3D texture analysis (3D‐TA) in noncontrast enhanced computed tomography (CT) (NCECT) to differentiate squamous cell carcinoma (SCC) from adenocarcinoma (AC), and the correlation with immunohistochemical markers. Methods A total of 70 patients confirmed with SCC (n = 29) and AC (n = 41) were enrolled in this retrospective study. 3D‐TA was utilized to calculate TA parameters of all the tumor lesions based on NCECT images, and all the patients were divided into the training and the test groups. The TA parameters were selected by dimensionality reduction, and the model was established to differentiate SCC from AC according to the training group. The ROC curve was used to evaluate the diagnostic efficiency of the model in both the training and the test groups. Spearman correlation were used to assess the correlation between the selected feature parameters and immunohistochemical markers (P63, P40, and TTF‐1). Results Five TA parameters, including volume count, relative deviation, Haralick correlation, gray‐level nonuniformity and run length nonuniformity, were obtained to differentiate SCC from AC by multistep dimensionality reduction. The new model combined with all five TA parameters yielded a high diagnostic performance to differentiate SCC from AC (AUC 0.803) in test group, with a specificity of 89% and a sensitivity of 77%. There was weak correlation between the five texture feature parameters and P63 as well as P40 in all patients (P < 0.05), respectively. Conclusions The model including five TA parameters on NECT has a good diagnostic performance in differentiating SCC from AC. Key points • Significant findings of the study The model created by five selected textural feature parameters can differentiate solid SCC from AC without contrast media. The selected five texture feature parameters are correlated to the immunohistochemical markers P63 and P40. • What this study adds The textural feature parameters' model can identify SCC from AC without contrast media.
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Affiliation(s)
- Rui Han
- Department of Radiology, Wuhan No.1 Hospital, Wuhan, China
| | - Roshan Arjal
- Department of Radiology, St. Francis Hospital, Evanston, Illinois, USA
| | - Jin Dong
- Department of Radiology, Wuhan No.1 Hospital, Wuhan, China
| | - Hong Jiang
- Department of Radiology, Wuhan No.1 Hospital, Wuhan, China
| | | | - Dongyou Zhang
- Department of Radiology, Wuhan No.1 Hospital, Wuhan, China
| | - Lu Huang
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science & Technology, Wuhan, China
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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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Wei H, Jiang H, Song B. Role of medical imaging for immune checkpoint blockade therapy: From response assessment to prognosis prediction. Cancer Med 2019; 8:5399-5413. [PMID: 31385454 PMCID: PMC6745848 DOI: 10.1002/cam4.2464] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 06/29/2019] [Accepted: 07/15/2019] [Indexed: 02/05/2023] Open
Abstract
Immune checkpoint blockade (ICB) represents a promising approach in cancer therapy. Owing to the peculiar biologic mechanisms of anticancer activity, checkpoint blockers are accompanied with distinctive response patterns and toxicity profiles. Medical imaging is the cornerstone for response assessment to immunotherapy and plays a critical role in monitoring of immune-related adverse events (irAEs). Imaging-based biomarkers have shown tremendous potential for the prediction of therapeutic efficacies and clinical outcomes in patients treated with checkpoint inhibitors. In this article, the landscape of current response assessment systems for immunotherapy was reviewed with a special focus on the latest advances in the assessment of responses to ICB. Emerging imaging biomarkers were discussed along with the challenges regarding their clinical transformation. In addition, the biological mechanisms and clinical applications of ICB and irAEs were also within the scope of this review.
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Affiliation(s)
- Hong Wei
- Department of RadiologySichuan University West China HospitalChengduSichuan ProvinceChina
| | - Hanyu Jiang
- Department of RadiologySichuan University West China HospitalChengduSichuan ProvinceChina
| | - Bin Song
- Department of RadiologySichuan University West China HospitalChengduSichuan ProvinceChina
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Prognostic and predictive value of histogram analysis in patients with non-small cell lung cancer refractory to platinum treated by nivolumab: A multicentre retrospective study. Eur J Radiol 2019; 118:251-256. [PMID: 31439251 DOI: 10.1016/j.ejrad.2019.07.019] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Revised: 05/24/2019] [Accepted: 07/16/2019] [Indexed: 12/23/2022]
Abstract
PURPOSE The aim of this study was to assess computed-tomography histogram analysis (CTHA) as prognostic and predictive factor in platinum-refractory non-small cell lung carcinoma (NSCLC) treated with immune checkpoint inhibitor Nivolumab. METHOD One hundred and four patients were enrolled from 3 different centers. CT was performed using similar parameters among different scanners. CTHA was performed with the proprietary software TexRAD, which extracts histogram features at different spatial scale (spatial scale filters, SSF) producing 30 CTHA features per patients. Cross-validated Least Absolute Shrinkage and Selection Operator LASSO was used to select those features which were related to overall and progression-free survival (OS and PFS, respectively). High- and low-risk subgroups were identified using the best cutoff. RESULTS Median follow-up was 13.8 weeks. Median OS and PFS were 7.3 and 3 months, respectively. LASSO selected kurtosis obtained by SSF = 4 mm as the single feature related to OS, leading to an hazard ratio (HR) of 0.476 (95%CI 0.29-0.77). PFS was related with kurtosis SSF = 6 mm, with HR of 0.556 (95%CI 0.36-0.86). CONCLUSION Despite its limitations, this study is the first which suggests that CTHA could play a role in stratifying prognosis and treatment response in patients with NSCLC treated with Nivolumab.
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Reinert CP, Federmann B, Hofmann J, Bösmüller H, Wirths S, Fritz J, Horger M. Computed tomography textural analysis for the differentiation of chronic lymphocytic leukemia and diffuse large B cell lymphoma of Richter syndrome. Eur Radiol 2019; 29:6911-6921. [PMID: 31236702 DOI: 10.1007/s00330-019-06291-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2019] [Revised: 05/11/2019] [Accepted: 05/28/2019] [Indexed: 10/26/2022]
Abstract
OBJECTIVE To test the hypothesis that both indolent and aggressive chronic lymphocytic leukemia (CLL) can be differentiated from diffuse large B cell lymphoma (DLBCL) of Richter syndrome (RS) by CT texture analysis (CTTA) of involved lymph nodes. MATERIAL AND METHODS We retrospectively included 52 patients with indolent CLL (26/52), aggressive CLL (8/52), and DLBCL of RS (18/52), who underwent standardized contrast-enhanced CT. In main lymphoma tissue, VOIs were generated from which CTTA features including first-, second-, and higher-order textural features were extracted. CTTA features were compared between the entire CLL group, the indolent CLL subtype, the aggressive CLL subtype, and DLBCL using a Kruskal-Wallis test. All p values were adjusted after the Bonferroni correction. ROC analyses for significant CTTA features were performed to determine cut-off values for differentiation between the groups. RESULTS Compared with DLBCL of RS, CTTA of the entire CLL group showed significant differences of entropy heterogeneity (p < 0.001), mean intensity (p < 0.001), mean average (p = 0.02), and number non-uniformity gray-level dependence matrix (NGLDM) (p = 0.03). Indolent CLL significantly differed for entropy (p < 0.001), uniformity of heterogeneity (p = 0.02), mean intensity (p < 0.001), and mean average (p = 0.01). Aggressive CLL showed significant differences in mean intensity (p = 0.04). For differentiation between CLL and DLBCL of RS, cut-off values for mean intensity and entropy of heterogeneity were defined (e.g., 6.63 for entropy heterogeneity [aggressive CLL vs. DLBCL]; sensitivity 0.78; specificity 0.63). CONCLUSIONS CTTA features of ultrastructure and vascularization significantly differ in CLL compared with that in DLBCL of Richter syndrome, allowing complementary to visual features for noninvasive differentiation by contrast-enhanced CT. KEY POINTS • Richter transformation of CLL into DLBCL results in structural changes in lymph node architecture and vascularization that can be detected by CTTA. • First-order CT textural features including intensity and heterogeneity significantly differ between both indolent CLL and aggressive CLL and DLBCL of Richter syndrome. • CT texture analysis allows for noninvasive detection of Richter syndrome which is of prognostic value.
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Affiliation(s)
- C P Reinert
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Hoppe-Seyler-Str.3, 72076, Tübingen, Germany.
| | - B Federmann
- Department of Pathology and Neuropathology, University Hospital Tübingen, Liebermeisterstraße 8, 72076, Tübingen, Germany
| | - J Hofmann
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Hoppe-Seyler-Str.3, 72076, Tübingen, Germany
| | - H Bösmüller
- Department of Pathology and Neuropathology, University Hospital Tübingen, Liebermeisterstraße 8, 72076, Tübingen, Germany
| | - S Wirths
- Department of Hematology and Oncology, University Hospital Tübingen, Otfried-Müller-Str. 10, 72076, Tübingen, Germany
| | - J Fritz
- Russell H. Morgan Department of Radiology and Radiological, Johns Hopkins University School of Medicine, Science, 601 N. Caroline Street, JHOC 3142, Baltimore, MD, 21287, USA
| | - M Horger
- Department of Diagnostic and Interventional Radiology, University Hospital Tübingen, Hoppe-Seyler-Str.3, 72076, Tübingen, Germany
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Vandendorpe B, Durot C, Lebellec L, Le Deley MC, Sylla D, Bimbai AM, Amroun K, Ramiandrisoa F, Cordoba A, Mirabel X, Hoeffel C, Pasquier D, Servagi-Vernat S. Prognostic value of the texture analysis parameters of the initial computed tomographic scan for response to neoadjuvant chemoradiation therapy in patients with locally advanced rectal cancer. Radiother Oncol 2019; 135:153-160. [DOI: 10.1016/j.radonc.2019.03.011] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2018] [Revised: 02/28/2019] [Accepted: 03/11/2019] [Indexed: 12/21/2022]
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