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Nardone V, Reginelli A, Rubini D, Gagliardi F, Del Tufo S, Belfiore MP, Boldrini L, Desideri I, Cappabianca S. Delta radiomics: an updated systematic review. LA RADIOLOGIA MEDICA 2024; 129:1197-1214. [PMID: 39017760 PMCID: PMC11322237 DOI: 10.1007/s11547-024-01853-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/10/2024] [Accepted: 07/04/2024] [Indexed: 07/18/2024]
Abstract
BACKGROUND Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and diverse clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches. METHODS Eligible articles were searched in Embase, Pubmed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with 3 key search terms: 'radiomics,' 'texture,' and 'delta.' Studies were analyzed using QUADAS-2 and the RQS tool. RESULTS Forty-eight studies were finally included. The studies were divided into preclinical/methodological (5 studies, 10.4%); rectal cancer (6 studies, 12.5%); lung cancer (12 studies, 25%); sarcoma (5 studies, 10.4%); prostate cancer (3 studies, 6.3%), head and neck cancer (6 studies, 12.5%); gastrointestinal malignancies excluding rectum (7 studies, 14.6%) and other disease sites (4 studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%. CONCLUSIONS Delta radiomics shows potential benefit for several clinical endpoints in oncology, such asdifferential diagnosis, prognosis and prediction of treatment response, evaluation of side effects. Nevertheless, the studies included in this systematic review suffer from the bias of overall low methodological rigor, so that the conclusions are currently heterogeneous, not robust and hardly replicable. Further research with prospective and multicenter studies is needed for the clinical validation of delta radiomics approaches.
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Affiliation(s)
- Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy.
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Dino Rubini
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Federico Gagliardi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Sara Del Tufo
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Maria Paola Belfiore
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Luca Boldrini
- Dipartimento di Diagnostica per Immagini, Radioterapia Oncologica ed Ematologia, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Isacco Desideri
- Department of Biomedical, Experimental and Clinical Sciences "M. Serio", University of Florence, Florence, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
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Morgagni P, Bencivenga M, Carneiro F, Cascinu S, Derks S, Di Bartolomeo M, Donohoe C, Eveno C, Gisbertz S, Grimminger P, Gockel I, Grabsch H, Kassab P, Langer R, Lonardi S, Maltoni M, Markar S, Moehler M, Marrelli D, Mazzei MA, Melisi D, Milandri C, Moenig PS, Mostert B, Mura G, Polkowski W, Reynolds J, Saragoni L, Van Berge Henegouwen MI, Van Hillegersberg R, Vieth M, Verlato G, Torroni L, Wijnhoven B, Tiberio GAM, Yang HK, Roviello F, de Manzoni G. International consensus on the management of metastatic gastric cancer: step by step in the foggy landscape : Bertinoro Workshop, November 2022. Gastric Cancer 2024; 27:649-671. [PMID: 38634954 PMCID: PMC11193703 DOI: 10.1007/s10120-024-01479-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 02/05/2024] [Indexed: 04/19/2024]
Abstract
BACKGROUND Many gastric cancer patients in Western countries are diagnosed as metastatic with a median overall survival of less than twelve months using standard chemotherapy. Innovative treatments, like targeted therapy or immunotherapy, have recently proved to ameliorate prognosis, but a general agreement on managing oligometastatic disease has yet to be achieved. An international multi-disciplinary workshop was held in Bertinoro, Italy, in November 2022 to verify whether achieving a consensus on at least some topics was possible. METHODS A two-round Delphi process was carried out, where participants were asked to answer 32 multiple-choice questions about CT, laparoscopic staging and biomarkers, systemic treatment for different localization, role and indication of palliative care. Consensus was established with at least a 67% agreement. RESULTS The assembly agreed to define oligometastases as a "dynamic" disease which either regresses or remains stable in response to systemic treatment. In addition, the definition of oligometastases was restricted to the following sites: para-aortic nodal stations, liver, lung, and peritoneum, excluding bones. In detail, the following conditions should be considered as oligometastases: involvement of para-aortic stations, in particular 16a2 or 16b1; up to three technically resectable liver metastases; three unilateral or two bilateral lung metastases; peritoneal carcinomatosis with PCI ≤ 6. No consensus was achieved on how to classify positive cytology, which was considered as oligometastatic by 55% of participants only if converted to negative after chemotherapy. CONCLUSION As assessed at the time of diagnosis, surgical treatment of oligometastases should aim at R0 curativity on the entire disease volume, including both the primary tumor and its metastases. Conversion surgery was defined as surgery on the residual volume of disease, which was initially not resectable for technical and/or oncological reasons but nevertheless responded to first-line treatment.
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Affiliation(s)
- Paolo Morgagni
- Department of General Surgery, Morgagni-Pierantoni Hospital, Forlì, Italy
| | - Maria Bencivenga
- General and Upper GI Surgery, Department of Surgery, University Hospital Verona, University of Verona, Verona, Italy.
| | - Fatima Carneiro
- Department of Pathology, Centro Hospitalar de São João, Institute of Molecular Pathology and Immunology of the University of Porto (Ipatimup), Porto, Portugal
| | - Stefano Cascinu
- Department of Medical Oncology, Comprehensive Cancer Center, Università Vita-Salute, IRCCS Ospedale San Raffaele, Milan, Italy
| | - Sarah Derks
- Department of Medical Oncology, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Maria Di Bartolomeo
- Department of Medical Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Claire Donohoe
- Medicinal Chemistry, Trinity Translational Medicine Institute, Trinity Centre for Health Sciences, Trinity College Dublin, The University of Dublin, St. James's Hospital, Dublin 8, Ireland
| | - Clarisse Eveno
- Department of Digestive and Oncologic Surgery, Claude Huriez University Hospital, Centre Hospitalier Universitaire (CHU) Lille, Université de Lille, Lille, France
| | - Suzanne Gisbertz
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | - Peter Grimminger
- Department of General, Visceral and Transplant Surgery, University Medical Center, University of Mainz, Mainz, Germany
| | - Ines Gockel
- Department of Visceral, Transplant, Thoracic and Vascular Surgery, University Hospital of Leipzig, Leipzig, Germany
| | - Heike Grabsch
- Department of Pathology, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands
- Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom
| | - Paulo Kassab
- Gastric Surgery Division, BP Gastric Surgery Department, Santa Casa Medical School, São Paulo, Brazil
| | - Rupert Langer
- Institute of Pathology and Microbiology, Johannes Kepler University Linz, Altenberger Strasse 69, 4040, Linz, Austria
| | - Sara Lonardi
- Istituto Oncologico Veneto IOV-IRCCS, Padua, Italy
| | - Marco Maltoni
- Unit of Palliative Care, Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Meldola, Forlì-Cesena, Italy
| | - Sheraz Markar
- Surgical Interventional Trials Unit, University of Oxford, Oxford, UK
| | - Markus Moehler
- Department of Medicine, Johannes-Gutenberg University Clinic, Mainz, Germany
| | - Daniele Marrelli
- Unit of General Surgery and Surgical Oncology, Department of Medicine Surgery and Neurosciences, University of Siena, 53100, Siena, Italy
| | - Maria Antonietta Mazzei
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, Azienda Ospedaliero-Universitaria Senese, University of Siena, 53100, Siena, Italy
| | - Davide Melisi
- Medical Oncology at the Department of Medicine, University of Verona, Verona, Italy
| | - Carlo Milandri
- Department of Oncology, San Donato Hospital, 52100, Arezzo, Italy
| | | | - Bianca Mostert
- Department of Medical Oncology, Erasmus MC Cancer Institute, Dr. Molewaterplein 40, 3015 GD, Rotterdam, The Netherlands
| | - Gianni Mura
- Department of Surgery, San Donato Hospital, Arezzo, Italy
| | - Wojciech Polkowski
- Department of Surgical Oncology, Medical University of Lublin, Radziwiłłowska 13 St, 20-080, Lublin, Poland
| | | | - Luca Saragoni
- Pathology Unit, Santa Maria delle Croci Ravenna Hospital, Ravenna, Italy
| | - Mark I Van Berge Henegouwen
- Department of Surgery, Cancer Center Amsterdam, Amsterdam UMC, University of Amsterdam, Amsterdam, The Netherlands
| | | | - Michael Vieth
- Institute of Pathology, Klinikum Bayreuth, Bayreuth, Germany
| | - Giuseppe Verlato
- Department of Diagnostics and Public Health, Section of Epidemiology and Medical Statistics, University of Verona, Verona, Italy
| | - Lorena Torroni
- Department of Diagnostics and Public Health, Section of Epidemiology and Medical Statistics, University of Verona, Verona, Italy
| | - Bas Wijnhoven
- Department of Surgery, Erasmus MC-University Medical Centre Rotterdam, Rotterdam, Netherlands
| | | | - Han-Kwang Yang
- Surgical Department, SNUH National Cancer Center, Seoul, Korea
| | - Franco Roviello
- Unit of General Surgery and Surgical Oncology, Department of Medicine Surgery and Neurosciences, University of Siena, 53100, Siena, Italy
| | - Giovanni de Manzoni
- General and Upper GI Surgery, Department of Surgery, University Hospital Verona, University of Verona, Verona, Italy
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Boldrini L, Chiloiro G, Cusumano D, Yadav P, Yu G, Romano A, Piras A, Votta C, Placidi L, Broggi S, Catucci F, Lenkowicz J, Indovina L, Bassetti MF, Yang Y, Fiorino C, Valentini V, Gambacorta MA. Radiomics-enhanced early regression index for predicting treatment response in rectal cancer: a multi-institutional 0.35 T MRI-guided radiotherapy study. LA RADIOLOGIA MEDICA 2024; 129:615-622. [PMID: 38512616 DOI: 10.1007/s11547-024-01761-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2023] [Accepted: 01/03/2024] [Indexed: 03/23/2024]
Abstract
PURPOSE The accurate prediction of treatment response in locally advanced rectal cancer (LARC) patients undergoing MRI-guided radiotherapy (MRIgRT) is essential for optimising treatment strategies. This multi-institutional study aimed to investigate the potential of radiomics in enhancing the predictive power of a known radiobiological parameter (Early Regression Index, ERITCP) to evaluate treatment response in LARC patients treated with MRIgRT. METHODS Patients from three international sites were included and divided into training and validation sets. 0.35 T T2*/T1-weighted MR images were acquired during simulation and at each treatment fraction. The biologically effective dose (BED) conversion was used to account for different radiotherapy schemes: gross tumour volume was delineated on the MR images corresponding to specific BED levels and radiomic features were then extracted. Multiple logistic regression models were calculated, combining ERITCP with other radiomic features. The predictive performance of the different models was evaluated on both training and validation sets by calculating the receiver operating characteristic (ROC) curves. RESULTS A total of 91 patients was enrolled: 58 were used as training, 33 as validation. Overall, pCR was observed in 25 cases. The model showing the highest performance was obtained combining ERITCP at BED = 26 Gy with a radiomic feature (10th percentile of grey level histogram, 10GLH) calculated at BED = 40 Gy. The area under ROC curve (AUC) of this combined model was 0.98 for training set and 0.92 for validation set, significantly higher (p = 0.04) than the AUC value obtained using ERITCP alone (0.94 in training and 0.89 in validation set). CONCLUSION The integration of the radiomic analysis with ERITCP improves the pCR prediction in LARC patients, offering more precise predictive models to further personalise 0.35 T MRIgRT treatments of LARC patients.
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Affiliation(s)
- Luca Boldrini
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Giuditta Chiloiro
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | | | - Poonam Yadav
- Northwestern Memorial Hospital, Northwestern University Feinberg, Chicago, IL, USA
| | - Gao Yu
- Department of Radiological Sciences, University of California, Los Angeles, CA, USA
| | - Angela Romano
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Antonio Piras
- UO Radioterapia Oncologica, Villa Santa Teresa, Bagheria, Palermo, Italy
| | - Claudio Votta
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Sara Broggi
- Medical Physics, San Raffaele Scientific Institute, Milan, Italy
| | | | - Jacopo Lenkowicz
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Luca Indovina
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
| | - Michael F Bassetti
- Department of Human Oncology, School of Medicine and Public Heath, University of Wisconsin - Madison, Madison, USA
| | - Yingli Yang
- Department of Radiological Sciences, University of California, Los Angeles, CA, USA
| | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milan, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario "Agostino Gemelli" IRCCS, Largo Francesco Vito 1, 00168, Rome, Italy
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Zhong H, Wang T, Hou M, Liu X, Tian Y, Cao S, Li Z, Han Z, Liu G, Sun Y, Meng C, Li Y, Jiang Y, Ji Q, Hao D, Liu Z, Zhou Y. Deep Learning Radiomics Nomogram Based on Enhanced CT to Predict the Response of Metastatic Lymph Nodes to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer. Ann Surg Oncol 2024; 31:421-432. [PMID: 37925653 DOI: 10.1245/s10434-023-14424-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Accepted: 09/26/2023] [Indexed: 11/07/2023]
Abstract
BACKGROUND We aimed to construct and validate a deep learning (DL) radiomics nomogram using baseline and restage enhanced computed tomography (CT) images and clinical characteristics to predict the response of metastatic lymph nodes to neoadjuvant chemotherapy (NACT) in locally advanced gastric cancer (LAGC). METHODS We prospectively enrolled 112 patients with LAGC who received NACT from January 2021 to August 2022. After applying the inclusion and exclusion criteria, 98 patients were randomized 7:3 to the training cohort (n = 68) and validation cohort (n = 30). We established and compared three radiomics signatures based on three phases of CT images before and after NACT, namely radiomics-baseline, radiomics-delta, and radiomics-restage. Then, we developed a clinical model, DL model, and a nomogram to predict the response of LAGC after NACT. We evaluated the predictive accuracy and clinical validity of each model using the receiver operating characteristic curve and decision curve analysis, respectively. RESULTS The radiomics-delta signature was the best predictor among the three radiomics signatures. So, we developed and validated a DL delta radiomics nomogram (DLDRN). In the validation cohort, the DLDRN produced an area under the receiver operating curve of 0.94 (95% confidence interval, 0.82-0.96) and demonstrated adequate differentiation of good response to NACT. Furthermore, the DLDRN significantly outperformed the clinical model and DL model (p < 0.001). The clinical utility of the DLDRN was confirmed through decision curve analysis. CONCLUSIONS In patients with LAGC, the DLDRN effectively predicted a therapeutic response in metastatic lymph nodes, which could provide valuable information for individualized treatment.
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Affiliation(s)
- Hao Zhong
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Tongyu Wang
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Mingyu Hou
- Department of Pathology, Qingdao University Affiliated Qingdao Women and Children's Hospital, Qingdao, Shandong, People's Republic of China
| | - Xiaodong Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yulong Tian
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Shougen Cao
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Zequn Li
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Zhenlong Han
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Gan Liu
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yuqi Sun
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Cheng Meng
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yujun Li
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yanxia Jiang
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Qinglian Ji
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Dapeng Hao
- Department of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Zimin Liu
- Department of Pathology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
- Department of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China
| | - Yanbing Zhou
- Department of Gastrointestinal Surgery, The Affiliated Hospital of Qingdao University, Qingdao, Shandong, People's Republic of China.
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5
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Wang L, Chen Y, Tan J, Ge Y, Xu Z, Wels M, Pan Z. Efficacy and prognostic value of delta radiomics on dual-energy computed tomography for gastric cancer with neoadjuvant chemotherapy: a preliminary study. Acta Radiol 2022; 64:1311-1321. [PMID: 36062762 DOI: 10.1177/02841851221123971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
BACKGROUND A non-invasive tool for tumor regression grade (TRG) evaluation is urgently needed for gastric cancer (GC) treated with neoadjuvant chemotherapy (NAC). PURPOSE To develop and validate a radiomics signature (RS) to evaluate TRG for locally advanced GC after NAC and assess its prognostic value. MATERIAL AND METHODS A total of 103 patients with GC treated with NAC were retrospectively recruited from April 2018 to December 2019 and were randomly allocated into a training cohort (n = 69) and a validation cohort (n = 34). Delineation was performed on both mixed and iodine-uptake images based on dual-energy computed tomography (DECT). A total of 4094 radiomics features were extracted from the pre-NAC, post-NAC, and delta feature sets. Spearman correlation and the least absolute shrinkage and selection operator were used for dimensionality reduction. Multivariable logistic regression was used for TRG evaluation and generated the optimal RS. Kaplan-Meier survival analysis with the log-rank test was implemented in an independent cohort of 40 patients to validate the prognostic value of the optimal RS. RESULTS Three, five, and six radiomics features were finally selected for the pre-NAC, post-NAC, and delta feature sets. The delta model demonstrated the best performance in assessing TRG in both the training and the validation cohorts (AUCs=0.91 and 0.76, respectively; P>0.1). The optimal RS from the delta model showed a significant capability to predict survival in the independent cohort (P<0.05). CONCLUSION Delta radiomics based on DECT images serves as a potential biomarker for TRG evaluation and shows prognostic value for patients with GC treated with NAC.
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Affiliation(s)
- Lingyun Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Jingwen Tan
- Department of Radiology, Ruijin Hospital North, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
| | - Yingqian Ge
- Siemens Healthineers Ltd, Shanghai, PR China
| | - Zhihan Xu
- Siemens Healthineers Ltd, Shanghai, PR China
| | - Michael Wels
- Department of Diagnostic Imaging Computed Tomography Image Analytics, 42406Siemens Healthcare GmbH, Forchheim, Germany
| | - Zilai Pan
- Department of Radiology, Ruijin Hospital North, Shanghai Jiao Tong University School of Medicine, Shanghai, PR China
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Lafata KJ, Wang Y, Konkel B, Yin FF, Bashir MR. Radiomics: a primer on high-throughput image phenotyping. Abdom Radiol (NY) 2022; 47:2986-3002. [PMID: 34435228 DOI: 10.1007/s00261-021-03254-x] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/15/2021] [Accepted: 08/16/2021] [Indexed: 01/18/2023]
Abstract
Radiomics is a high-throughput approach to image phenotyping. It uses computer algorithms to extract and analyze a large number of quantitative features from radiological images. These radiomic features collectively describe unique patterns that can serve as digital fingerprints of disease. They may also capture imaging characteristics that are difficult or impossible to characterize by the human eye. The rapid development of this field is motivated by systems biology, facilitated by data analytics, and powered by artificial intelligence. Here, as part of Abdominal Radiology's special issue on Quantitative Imaging, we provide an introduction to the field of radiomics. The technique is formally introduced as an advanced application of data analytics, with illustrating examples in abdominal radiology. Artificial intelligence is then presented as the main driving force of radiomics, and common techniques are defined and briefly compared. The complete step-by-step process of radiomic phenotyping is then broken down into five key phases. Potential pitfalls of each phase are highlighted, and recommendations are provided to reduce sources of variation, non-reproducibility, and error associated with radiomics.
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Affiliation(s)
- Kyle J Lafata
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA. .,Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA. .,Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA.
| | - Yuqi Wang
- Department of Electrical & Computer Engineering, Duke University Pratt School of Engineering, Durham, NC, USA
| | - Brandon Konkel
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA
| | - Fang-Fang Yin
- Department of Radiation Oncology, Duke University School of Medicine, Durham, NC, USA
| | - Mustafa R Bashir
- Department of Radiology, Duke University School of Medicine, Durham, NC, USA.,Department of Medicine, Gastroenterology, Duke University School of Medicine, Durham, NC, USA
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Ability of Delta Radiomics to Predict a Complete Pathological Response in Patients with Loco-Regional Rectal Cancer Addressed to Neoadjuvant Chemo-Radiation and Surgery. Cancers (Basel) 2022; 14:cancers14123004. [PMID: 35740669 PMCID: PMC9221458 DOI: 10.3390/cancers14123004] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 05/27/2022] [Accepted: 06/15/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary The present study aimed to investigate the possible use of MRI delta texture analysis (D-TA) in order to predict the extent of pathological response in patients with locally advanced rectal cancer addressed to neoadjuvant chemo-radiotherapy (C-RT) followed by surgery. We found that D-TA may really predict the frequency of pCR in this patient setting and, thus, it may be investigated as a potential item to identify candidate patients who may benefit from an aggressive radical surgery. Abstract We performed a pilot study to evaluate the use of MRI delta texture analysis (D-TA) as a methodological item able to predict the frequency of complete pathological responses and, consequently, the outcome of patients with locally advanced rectal cancer addressed to neoadjuvant chemoradiotherapy (C-RT) and subsequently, to radical surgery. In particular, we carried out a retrospective analysis including 100 patients with locally advanced rectal adenocarcinoma who received C-RT and then radical surgery in three different oncological institutions between January 2013 and December 2019. Our experimental design was focused on the evaluation of the gross tumor volume (GTV) at baseline and after C-RT by means of MRI, which was contoured on T2, DWI, and ADC sequences. Multiple texture parameters were extracted by using a LifeX Software, while D-TA was calculated as percentage of variations in the two time points. Both univariate and multivariate analysis (logistic regression) were, therefore, carried out in order to correlate the above-mentioned TA parameters with the frequency of pathological responses in the examined patients’ population focusing on the detection of complete pathological response (pCR, with no viable cancer cells: TRG 1) as main statistical endpoint. ROC curves were performed on three different datasets considering that on the 21 patients, only 21% achieved an actual pCR. In our training dataset series, pCR frequency significantly correlated with ADC GLCM-Entropy only, when univariate and binary logistic analysis were performed (AUC for pCR was 0.87). A confirmative binary logistic regression analysis was then repeated in the two remaining validation datasets (AUC for pCR was 0.92 and 0.88, respectively). Overall, these results support the hypothesis that D-TA may have a significant predictive value in detecting the occurrence of pCR in our patient series. If confirmed in prospective and multicenter trials, these results may have a critical role in the selection of patients with locally advanced rectal cancer who may benefit form radical surgery after neoadjuvant chemoradiotherapy.
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Li HH, Sun B, Tan C, Li R, Fu CX, Grimm R, Zhu H, Peng WJ. The Value of Whole-Tumor Histogram and Texture Analysis Using Intravoxel Incoherent Motion in Differentiating Pathologic Subtypes of Locally Advanced Gastric Cancer. Front Oncol 2022; 12:821586. [PMID: 35223503 PMCID: PMC8864172 DOI: 10.3389/fonc.2022.821586] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 01/20/2022] [Indexed: 01/02/2023] Open
Abstract
Purpose To determine if whole-tumor histogram and texture analyses using intravoxel incoherent motion (IVIM) parameters values could differentiate the pathologic characteristics of locally advanced gastric cancer. Methods Eighty patients with histologically confirmed locally advanced gastric cancer who received surgery in our institution were retrospectively enrolled into our study between April 2017 and December 2018. Patients were excluded if they had lesions with the smallest diameter < 5 mm and severe image artifacts. MR scanning included IVIM sequences (9 b values, 0, 20, 40, 60, 100, 150,200, 500, and 800 s/mm2) used in all patients before treatment. Whole tumors were segmented by manually drawing the lesion contours on each slice of the diffusion-weighted imaging (DWI) images (with b=800). Histogram and texture metrics for IVIM parameters values and apparent diffusion coefficient (ADC) values were measured based on whole-tumor volume analyses. Then, all 24 extracted metrics were compared between well, moderately, and poorly differentiated tumors, and between different Lauren classifications, signet-ring cell carcinomas, and other poorly cohesive carcinomas using univariate analyses. Multivariate logistic analyses and multicollinear tests were used to identify independent influencing factors from the significant variables of the univariate analyses to distinguish tumor differentiation and Lauren classifications. ROC curve analyses were performed to evaluate the diagnostic performance of these independent influencing factors for determining tumor differentiation and Lauren classifications and identifying signet-ring cell carcinomas. The interobserver agreement was also conducted between the two observers for image quality evaluations and parameter metric measurements. Results For diagnosing tumor differentiation, the ADCmedian, pure diffusion coefficient median (Dslowmedian), and pure diffusion coefficient entropy (Dslowentropy) showed the greatest AUCs: 0.937, 0.948, and 0.850, respectively, and no differences were found between the three metrics, P>0.05). The 95th percentile perfusion factor (FP P95th) was the best metric to distinguish diffuse-type GCs vs. intestinal/mixed (AUC=0.896). The ROC curve to distinguish signet-ring cell carcinomas from other poorly cohesive carcinomas showed that the Dslowmedian had AUC of 0.738. For interobserver reliability, image quality evaluations showed excellent agreement (interclass correlation coefficient [ICC]=0.85); metrics measurements of all parameters indicated good to excellent agreement (ICC=0.65-0.89), except for the Dfast metric, which showed moderate agreement (ICC=0.41-0.60). Conclusions The whole-tumor histogram and texture analyses of the IVIM parameters based on the biexponential model provided a non-invasive method to discriminate pathologic tumor subtypes preoperatively in patients with locally advanced gastric cancer. The metric FP P95th derived from IVIM performed better in determining Lauren classifications than the mono-exponential model.
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Affiliation(s)
- Huan-Huan Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Bo Sun
- Department of Gastric Surgery, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Cong Tan
- Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Rong Li
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Cai-Xia Fu
- MR Applications Development, Siemens Shenzhen Magnetic Resonance Ltd, Shenzhen, China
| | - Robert Grimm
- MR Applications Development, Siemens Healthcare, Erlangen, Germany
| | - Hui Zhu
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
| | - Wei-Jun Peng
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
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9
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Chiloiro G, Cusumano D, Boldrini L, Romano A, Placidi L, Nardini M, Meldolesi E, Barbaro B, Coco C, Crucitti A, Persiani R, Petruzziello L, Ricci R, Salvatore L, Sofo L, Alfieri S, Manfredi R, Valentini V, Gambacorta MA. THUNDER 2: THeragnostic Utilities for Neoplastic DisEases of the Rectum by MRI guided radiotherapy. BMC Cancer 2022; 22:67. [PMID: 35033008 PMCID: PMC8760695 DOI: 10.1186/s12885-021-09158-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2021] [Accepted: 12/24/2021] [Indexed: 02/07/2023] Open
Abstract
Background Neoadjuvant chemoradiation therapy (nCRT) is the standard treatment modality in locally advanced rectal cancer (LARC). Since response to radiotherapy (RT) is dose dependent in rectal cancer, dose escalation may lead to higher complete response rates. The possibility to predict patients who will achieve complete response (CR) is fundamental. Recently, an early tumour regression index (ERI) was introduced to predict pathological CR (pCR) after nCRT in LARC patients. The primary endpoints will be the increase of CR rate and the evaluation of feasibility of delta radiomics-based predictive MRI guided Radiotherapy (MRgRT) model. Methods Patients affected by LARC cT2-3, N0-2 or cT4 for anal sphincter involvement N0-2a, M0 without high risk features will be enrolled in the trial. Neoadjuvant CRT will be administered using MRgRT. The initial RT treatment will consist in delivering 55 Gy in 25 fractions on Gross Tumor Volume (GTV) plus the corresponding mesorectum and 45 Gy in 25 fractions on the drainage nodes. Chemotherapy with 5-fluoracil (5-FU) or oral capecitabine will be administered continuously. A 0.35 Tesla MRI will be acquired at simulation and every day during MRgRT. At fraction 10, ERI will be calculated: if ERI will be inferior than 13.1, the patient will continue the original treatment; if ERI will be higher than 13.1 the treatment plan will be reoptimized, intensifying the dose to the residual tumor at the 11th fraction to reach 60.1 Gy. At the end of nCRT instrumental examinations are to be performed in order to restage patients. In case of stable disease or progression, the patient will undergo surgery. In case of major or complete clinical response, conservative approaches may be chosen. Patients will be followed up to evaluate toxicity and quality of life. The number of cases to be enrolled will be 63: all the patients will be treated at Fondazione Policlinico Universitario A. Gemelli IRCCS in Rome. Discussion This clinical trial investigates the impact of RT dose escalation in poor responder LARC patients identified using ERI, with the aim of increasing the probability of CR and consequently an organ preservation benefit in this group of patients. Trial registration ClinicalTrials.gov Identifier: NCT04815694 (25/03/2021).
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Affiliation(s)
- Giuditta Chiloiro
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Davide Cusumano
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Luca Boldrini
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Angela Romano
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy.
| | - Lorenzo Placidi
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Matteo Nardini
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Elisa Meldolesi
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Brunella Barbaro
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Claudio Coco
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Antonio Crucitti
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Roberto Persiani
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Lucio Petruzziello
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Riccardo Ricci
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Lisa Salvatore
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Luigi Sofo
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Sergio Alfieri
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Riccardo Manfredi
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
| | - Vincenzo Valentini
- Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Largo Agostino Gemelli 8, 00168, Rome, Italy
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10
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Sushentsev N, Rundo L, Blyuss O, Nazarenko T, Suvorov A, Gnanapragasam VJ, Sala E, Barrett T. Comparative performance of MRI-derived PRECISE scores and delta-radiomics models for the prediction of prostate cancer progression in patients on active surveillance. Eur Radiol 2022; 32:680-689. [PMID: 34255161 PMCID: PMC8660717 DOI: 10.1007/s00330-021-08151-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 05/27/2021] [Accepted: 06/13/2021] [Indexed: 12/14/2022]
Abstract
OBJECTIVES To compare the performance of the PRECISE scoring system against several MRI-derived delta-radiomics models for predicting histopathological prostate cancer (PCa) progression in patients on active surveillance (AS). METHODS The study included AS patients with biopsy-proven PCa with a minimum follow-up of 2 years and at least one repeat targeted biopsy. Histopathological progression was defined as grade group progression from diagnostic biopsy. The control group included patients with both radiologically and histopathologically stable disease. PRECISE scores were applied prospectively by four uro-radiologists with 5-16 years' experience. T2WI- and ADC-derived delta-radiomics features were computed using baseline and latest available MRI scans, with the predictive modelling performed using the parenclitic networks (PN), least absolute shrinkage and selection operator (LASSO) logistic regression, and random forests (RF) algorithms. Standard measures of discrimination and areas under the ROC curve (AUCs) were calculated, with AUCs compared using DeLong's test. RESULTS The study included 64 patients (27 progressors and 37 non-progressors) with a median follow-up of 46 months. PRECISE scores had the highest specificity (94.7%) and positive predictive value (90.9%), whilst RF had the highest sensitivity (92.6%) and negative predictive value (92.6%) for predicting disease progression. The AUC for PRECISE (84.4%) was non-significantly higher than AUCs of 81.5%, 78.0%, and 80.9% for PN, LASSO regression, and RF, respectively (p = 0.64, 0.43, and 0.57, respectively). No significant differences were observed between AUCs of the three delta-radiomics models (p-value range 0.34-0.77). CONCLUSIONS PRECISE and delta-radiomics models achieved comparably good performance for predicting PCa progression in AS patients. KEY POINTS • The observed high specificity and PPV of PRECISE are complemented by the high sensitivity and NPV of delta-radiomics, suggesting a possible synergy between the two image assessment approaches. • The comparable performance of delta-radiomics to PRECISE scores applied by expert readers highlights the prospective use of the former as an objective and standardisable quantitative tool for MRI-guided AS follow-up. • The marginally superior performance of parenclitic networks compared to conventional machine learning algorithms warrants its further use in radiomics research.
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Affiliation(s)
- Nikita Sushentsev
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK.
- Department of Radiology, University of Cambridge School of Clinical Medicine, Box 218, Cambridge Biomedical Campus, Cambridge, CB2 0QQ, UK.
| | - Leonardo Rundo
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Oleg Blyuss
- School of Physics, Engineering & Computer Science, University of Hertfordshire, Hatfield, UK
- Department of Paediatrics and Paediatric Infectious Diseases, Sechenov First Moscow State Medical University, Moscow, Russia
- Department of Applied Mathematics, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russia
| | - Tatiana Nazarenko
- Department of Mathematics and Institute for Women's Health, University College London, London, UK
| | - Aleksandr Suvorov
- World-Class Research Center "Digital Biodesign and Personalised Healthcare", Sechenov First Moscow State Medical University, Moscow, Russia
| | - Vincent J Gnanapragasam
- Division of Urology, Department of Surgery, University of Cambridge, Cambridge, UK
- Cambridge Urology Translational Research and Clinical Trials Office, University of Cambridge, Cambridge, UK
| | - Evis Sala
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK
- Cancer Research UK Cambridge Centre, University of Cambridge, Cambridge, UK
| | - Tristan Barrett
- Department of Radiology, Addenbrooke's Hospital and University of Cambridge, Cambridge, UK
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11
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Mazzei MA, Bagnacci G, Gentili F, Capitoni I, Mura G, Marrelli D, Petrioli R, Brunese L, Cappabianca S, Catarci M, Degiuli M, De Manzoni G, De Prizio M, Donini A, Romario UF, Funicelli L, Laghi A, Minetti G, Morgagni P, Petrella E, Pittiani F, Rausei S, Romanini L, Rosati R, Ianora AAS, Tiberio GAM, Volterrani L, Roviello F, Grassi R. Structured and shared CT radiological report of gastric cancer: a consensus proposal by the Italian Research Group for Gastric Cancer (GIRCG) and the Italian Society of Medical and Interventional Radiology (SIRM). Eur Radiol 2022; 32:938-949. [PMID: 34383148 PMCID: PMC8359760 DOI: 10.1007/s00330-021-08205-0] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/15/2021] [Accepted: 07/07/2021] [Indexed: 02/07/2023]
Abstract
OBJECTIVES Written radiological report remains the most important means of communication between radiologist and referring medical/surgical doctor, even though CT reports are frequently just descriptive, unclear, and unstructured. The Italian Society of Medical and Interventional Radiology (SIRM) and the Italian Research Group for Gastric Cancer (GIRCG) promoted a critical shared discussion between 10 skilled radiologists and 10 surgical oncologists, by means of multi-round consensus-building Delphi survey, to develop a structured reporting template for CT of GC patients. METHODS Twenty-four items were organized according to the broad categories of a structured report as suggested by the European Society of Radiology (clinical referral, technique, findings, conclusion, and advice) and grouped into three "CT report sections" depending on the diagnostic phase of the radiological assessment for the oncologic patient (staging, restaging, and follow-up). RESULTS In the final round, 23 out of 24 items obtained agreement ( ≥ 8) and consensus ( ≤ 2) and 19 out 24 items obtained a good stability (p > 0.05). CONCLUSIONS The structured report obtained, shared by surgical and medical oncologists and radiologists, allows an appropriate, clearer, and focused CT report essential to high-quality patient care in GC, avoiding the exclusion of key radiological information useful for multidisciplinary decision-making. KEY POINTS • Imaging represents the cornerstone for tailored treatment in GC patients. • CT-structured radiology report in GC patients is useful for multidisciplinary decision making.
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Affiliation(s)
- Maria Antonietta Mazzei
- Department of Medical, Surgical and Neuro Sciences, University of Siena and Department of Radiological Sciences, Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy ,SIRM, Italian College of Computed Tomography, Italian Society of Medical and Interventional Radiology, Milan, Italy
| | - Giulio Bagnacci
- Department of Medical, Surgical and Neuro Sciences, University of Siena and Department of Radiological Sciences, Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy ,SIRM, Italian College of Computed Tomography, Italian Society of Medical and Interventional Radiology, Milan, Italy
| | - Francesco Gentili
- SIRM, Italian College of Computed Tomography, Italian Society of Medical and Interventional Radiology, Milan, Italy ,Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Iacopo Capitoni
- Department of Medical, Surgical and Neuro Sciences, University of Siena and Department of Radiological Sciences, Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Gianni Mura
- Department of Surgery, Division of General Surgery, Arezzo Hospital, Arezzo, Italy
| | - Daniele Marrelli
- Department of Medicine, Surgery and Neuroscience, Unit of General Surgery and Surgical Oncology, University of Siena, Siena, Italy
| | - Roberto Petrioli
- Department of Oncology, Unit of Medical Oncology, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Luca Brunese
- Department of Medicine and Health Sciences, University of Molise, Campobasso, Italy ,SIRM, Italian College of Oncology, Italian Society of Medical and Interventional Radiology, Milan, Italy
| | - Salvatore Cappabianca
- SIRM, Italian College of Oncology, Italian Society of Medical and Interventional Radiology, Milan, Italy ,Division of Radiology, University of Campania Luigi Vanvitelli, Naples, Italy
| | - Marco Catarci
- FACS; UOC Chirurgia Generale; Ospedale Sandro Pertini – ASL Roma 2, Roma, Italy
| | - Maurizio Degiuli
- Surgical Oncology and Digestive Surgery Unit, Department of Oncology, University of Turin; San Luigi University Hospital, Orbassano, Italy
| | | | - Marco De Prizio
- Department of Surgery, Division of General Surgery, Arezzo Hospital, Arezzo, Italy
| | - Annibale Donini
- Department of Surgery and Biomedical Sciences, University of Perugia, Perugia, Italy
| | | | - Luigi Funicelli
- SIRM, Italian College of Computed Tomography, Italian Society of Medical and Interventional Radiology, Milan, Italy ,SIRM, Italian College of Oncology, Italian Society of Medical and Interventional Radiology, Milan, Italy ,Digestive Surgery, IEO European Institute of Oncology – IRCCS, Milan, Italy
| | - Andrea Laghi
- Department of Surgical and Medical Sciences and Translational Medicine, Sapienza University of Rome – Sant’Andrea University Hospital, Rome, Italy ,SIRM, Italian College of Gastroenterology, Italian Society of Medical and Interventional Radiology, Milan, Italy
| | - Giuseppe Minetti
- SIRM, Italian College of Computed Tomography, Italian Society of Medical and Interventional Radiology, Milan, Italy ,Radiology Department, Ospedale Policlinico San Martino, IRCCS per L’Oncologia e le Neuroscienze, Genoa, Italy
| | - Paolo Morgagni
- General and Oncologic Surgery, Morgagni-Pierantoni Hospital, Forlì, Italy
| | - Enrico Petrella
- Radiology Unit, Morgagni-Pierantoni Hospital, AUSL Romagna, Forlì, Italy
| | - Frida Pittiani
- SIRM, Italian College of Computed Tomography, Italian Society of Medical and Interventional Radiology, Milan, Italy ,Department of Radiology, ASST Spedali Civili Brescia, Brescia, Italy
| | - Stefano Rausei
- Department of Surgery, ASST Valle Olona, Gallarate, Varese, Italy
| | | | - Riccardo Rosati
- Endocrine Unit, Department of Surgery, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Amato Antonio Stabile Ianora
- Interdisciplinary Department of Medicine, Section of Radiology and Radiation Oncology, University of Bari, Bari, Italy
| | - Guido A. M. Tiberio
- Surgical Unit, Department of Experimental and Clinical Sciences, University of Brescia, Brescia, Italy
| | - Luca Volterrani
- Department of Medical, Surgical and Neuro Sciences, University of Siena and Department of Radiological Sciences, Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy ,SIRM, Italian College of Oncology, Italian Society of Medical and Interventional Radiology, Milan, Italy
| | - Franco Roviello
- Department of Medicine, Surgery and Neuroscience, Unit of General Surgery and Surgical Oncology, University of Siena, Siena, Italy
| | - Roberto Grassi
- Division of Radiology, University of Campania Luigi Vanvitelli, Naples, Italy ,SIRM Foundation, Italian Society of Medical and Interventional Radiology, Milan, Italy
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12
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Nardone V, Reginelli A, Grassi R, Boldrini L, Vacca G, D'Ippolito E, Annunziata S, Farchione A, Belfiore MP, Desideri I, Cappabianca S. Delta radiomics: a systematic review. Radiol Med 2021; 126:1571-1583. [PMID: 34865190 DOI: 10.1007/s11547-021-01436-7] [Citation(s) in RCA: 88] [Impact Index Per Article: 29.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Accepted: 11/18/2021] [Indexed: 12/29/2022]
Abstract
BACKGROUND Radiomics can provide quantitative features from medical imaging that can be correlated with various biological features and clinical endpoints. Delta radiomics, on the other hand, consists in the analysis of feature variation at different acquisition time points, usually before and after therapy. The aim of this study was to provide a systematic review of the different delta radiomics approaches. METHODS Eligible articles were searched in Embase, PubMed, and ScienceDirect using a search string that included free text and/or Medical Subject Headings (MeSH) with three key search terms: "radiomics", "texture", and "delta". Studies were analysed using QUADAS-2 and the RQS tool. RESULTS Forty-eight studies were finally included. The studies were divided into preclinical/methodological (five studies, 10.4%); rectal cancer (six studies, 12.5%); lung cancer (twelve studies, 25%); sarcoma (five studies, 10.4%); prostate cancer (three studies, 6.3%), head and neck cancer (six studies, 12.5%); gastrointestinal malignancies excluding rectum (seven studies, 14.6%), and other disease sites (four studies, 8.3%). The median RQS of all studies was 25% (mean 21% ± 12%), with 13 studies (30.2%) achieving a quality score < 10% and 22 studies (51.2%) < 25%. CONCLUSIONS Delta radiomics shows potential benefit for several clinical endpoints in oncology (differential diagnosis, prognosis and prediction of treatment response, and evaluation of side effects). Nevertheless, the studies included in this systematic review suffer from the bias of overall low quality, so that the conclusions are currently heterogeneous, not robust, and not replicable. Further research with prospective and multicentre studies is needed for the clinical validation of delta radiomics approaches.
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Affiliation(s)
- Valerio Nardone
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy.
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Luca Boldrini
- Dipartimento Di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Giovanna Vacca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Emma D'Ippolito
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Salvatore Annunziata
- Dipartimento Di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Alessandra Farchione
- Dipartimento Di Diagnostica Per Immagini, Radioterapia Oncologica Ed Ematologia - Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | - Maria Paola Belfiore
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
| | - Isacco Desideri
- Department of Biomedical, Experimental and Clinical Sciences "M. Serio", University of Florence, Florence, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", 80138, Naples, Italy
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13
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Onoda H, Tanabe M, Higashi M, Kawano Y, Ihara K, Miyoshi K, Ito K. Assessment of gastric wall structure using ultra-high-resolution computed tomography. Eur J Radiol 2021; 146:110067. [PMID: 34847396 DOI: 10.1016/j.ejrad.2021.110067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Revised: 11/14/2021] [Accepted: 11/21/2021] [Indexed: 12/24/2022]
Abstract
PURPOSE To evaluate the image quality of ultra-high-resolution CT (U-HRCT) in the comparison among four different reconstruction methods, focusing on the gastric wall structure, and to compare the conspicuity of a three-layered structure of the gastric wall between conventional HRCT (C-HRCT) and U-HRCT. METHOD Our retrospective study included 48 patients who underwent contrast-enhanced U-HRCT. Quantitative analyses were performed to compare image noise of U-HRCT between deep-learning reconstruction (DLR) and other three methods (filtered back projection: FBP, hybrid iterative reconstruction: Hybrid-IR, and Model-based iterative reconstruction: MBIR). The mean overall image quality scores were also compared between the DLR and other three methods. In addition, the mean conspicuity scores for the three-layered structure of the gastric wall at five regions were compared between C-HRCT and U-HRCT. RESULTS The mean noise of U-HRCT with DLR was significantly lower than that with the other three methods (P < 0.001). The mean overall image quality scores with DLR images were significantly higher than those with the other three methods (P < 0.001). Regarding the comparison between C-HRCT and U-HRCT, the mean conspicuity scores for the three-layered structure of the gastric wall on U-HRCT were significantly better than those on C-HRCT in the fornix (5 [5-5] vs. 3.5 [3-4], P < 0.001), body (4 [3.25-5] vs. 4 [3-4], P = 0.039), angle (5 [4-5] vs. 3 [2-4], P < 0.001), and antral posterior (4 [3.25-5] vs. 2 [2-4], P < 0.001), except for antral anterior (4 [3-5] vs. 3 [3-4], P = 0.230) CONCLUSION: U-HRCT using DLR improved the image noise and overall image quality of the gastric wall as well as the conspicuity of the three-layered structure, suggesting its utility for the evaluation of the anatomical details of the gastric wall structure.
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Affiliation(s)
- Hideko Onoda
- Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
| | - Masahiro Tanabe
- Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan.
| | - Mayumi Higashi
- Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
| | - Yosuke Kawano
- Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
| | - Kenichiro Ihara
- Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
| | - Keisuke Miyoshi
- Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
| | - Katsuyoshi Ito
- Department of Radiology, Yamaguchi University Graduate School of Medicine, 1-1-1 Minami-Kogushi, Ube, Yamaguchi 755-8505, Japan
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Zheng X, Shao J, Zhou L, Wang L, Ge Y, Wang G, Feng F. A Comprehensive Nomogram Combining CT Imaging with Clinical Features for Prediction of Lymph Node Metastasis in Stage I-IIIB Non-small Cell Lung Cancer. Ther Innov Regul Sci 2021; 56:155-167. [PMID: 34699046 DOI: 10.1007/s43441-021-00345-1] [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: 06/03/2021] [Accepted: 10/12/2021] [Indexed: 12/24/2022]
Abstract
OBJECTIVE The status of lymph node metastasis (LNM) is highly correlated with the recurrence and survival outcomes of patients with lung cancer. Thus, a tool that predicts LNM could benefit patient treatment and prognosis. The present study established a new radiomic model by combining computed tomography (CT) radiomic features and clinical parameters to predict the LNM status in patients with non-small cell lung cancer (NSCLC). METHODS Demographic parameters and clinical laboratory values were analyzed in 217 patients with stage I-IIIB NSCLC; 107 of the patients received CT scanning and radiomic characteristics were used for LNM assessment (76 in the training cohort and 31 in the validation cohort). The minimum redundancy maximum relevance (mRMR) and the least absolute shrinkage and selection operator (LASSO) regression model were used to select the most predictive features on the basis of the 76 patients in the training set. The value of the area under the receiver operator characteristic (ROC) curve (AUC) was adopted to determine the correlation between LN status and the radiomics signature in training cohorts and then validated in the 31 patients of validation set. The radiomics nomogram was analyzed using univariate and multivariate logistic regression. Decision curve analysis (DCA) was performed to evaluate the clinical utility of this model. RESULTS This was a retrospective study. Five radiomic characteristics were significantly correlated with LNM in the two cohorts (P < 0.05). The radiomic nomogram that incorporated the above radiomic characteristics, the RDW, and the CT-based LN status had satisfactory discrimination and calibration in the training (AUC, 0.79; 95% CI 0.69-0.89) and validation cohorts (AUC, 0.70; 95% CI 0.50-0.89).The DCA showed that the developed nomogram had promising clinical utility. CONCLUSIONS The developed nomogram, combined with preoperative radiomics evidence, the RDW, and the CT-based LN status, has the potential to preoperatively predict LNM with high accuracy and can facilitate the prediction of LN status for NSCLC patients.
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Affiliation(s)
- Xingxing Zheng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, No. 30 Tongyangbei Road, Tongzhou District, Nantong, 226361, China.,Department of Radiology, Baoji Central Hospital, Baoji, 721000, China
| | - Jingjing Shao
- Key Laboratory of Cancer Research Center Nantong, Affiliated Tumor Hospital of Nantong University, Nantong, 226361, China
| | - Linli Zhou
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, No. 30 Tongyangbei Road, Tongzhou District, Nantong, 226361, China
| | - Li Wang
- Department of Radiology, Baoji Central Hospital, Baoji, 721000, China
| | - Yaqiong Ge
- GE Healthcare China, Shanghai, 210000, China
| | - Gaoren Wang
- Department of Radiotherapy, Affiliated Tumor Hospital of Nantong University, Nantong, 226361, China.
| | - Feng Feng
- Department of Radiology, Affiliated Tumor Hospital of Nantong University, No. 30 Tongyangbei Road, Tongzhou District, Nantong, 226361, China.
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Perioperative Chemotherapy with FLOT Scheme in Resectable Gastric Adenocarcinoma: A Preliminary Correlation between TRG and Radiomics. APPLIED SCIENCES-BASEL 2021. [DOI: 10.3390/app11199211] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Perioperative chemotherapy (p-ChT) with a fluorouracil plus leucovorin, oxaliplatin, and docetaxel (FLOT) scheme is the gold standard of care for locally advanced gastric cancer. We aimed to test CT radiomics performance in early response prediction for p-ChT. Patients with advanced gastric cancer who underwent contrast enhanced CT prior to and post p-ChT were retrospectively enrolled. Histologic evaluation of resected specimens was used as the reference standard, and patients were divided into responders (TRG 1a-1b) and non-responders (TRG 2-3) according to their Becker tumor regression grade (TRG). A volumetric region of interest including the whole tumor tissue was drawn from a CT portal-venous phase before and after p-ChT; 120 radiomic features, both first and second order, were extracted. CT radiomics performances were derived from baseline CT radiomics alone and ΔRadiomics to predict response to p-ChT according to the TRG and tested using a receiver operating characteristic (ROC) curve. The final population comprised 15 patients, 6 (40%) responders and 9 (60%) non-responders. Among pre-treatment CT radiomics parameters, Shape, GLCM, First order, and NGTDM features showed a significant ability to discriminate between responders and non-responders (p < 0.011), with Cluster Shade and Autocorrelation (GLCM features) having AUC = 0.907. ΔRadiomics showed significant differences for Shape, GLRLM, GLSZM, and NGTDM features (p < 0.007). MeshVolume (Shape feature) and LongRunEmphasis (GLRLM feature) had AUC = 0.889. In conclusion, CT radiomics may represent an important supportive approach for the radiologic evaluation of advanced gastric cancer patients.
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Radiomics as a New Frontier of Imaging for Cancer Prognosis: A Narrative Review. Diagnostics (Basel) 2021; 11:diagnostics11101796. [PMID: 34679494 PMCID: PMC8534713 DOI: 10.3390/diagnostics11101796] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 09/15/2021] [Accepted: 09/23/2021] [Indexed: 12/12/2022] Open
Abstract
The evaluation of the efficacy of different therapies is of paramount importance for the patients and the clinicians in oncology, and it is usually possible by performing imaging investigations that are interpreted, taking in consideration different response evaluation criteria. In the last decade, texture analysis (TA) has been developed in order to help the radiologist to quantify and identify parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye, that can be correlated with different endpoints, including cancer prognosis. The aim of this work is to analyze the impact of texture in the prediction of response and in prognosis stratification in oncology, taking into consideration different pathologies (lung cancer, breast cancer, gastric cancer, hepatic cancer, rectal cancer). Key references were derived from a PubMed query. Hand searching and clinicaltrials.gov were also used. This paper contains a narrative report and a critical discussion of radiomics approaches related to cancer prognosis in different fields of diseases.
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Chen Y, Yuan F, Wang L, Li E, Xu Z, Wels M, Yao W, Zhang H. Evaluation of dual-energy CT derived radiomics signatures in predicting outcomes in patients with advanced gastric cancer after neoadjuvant chemotherapy. Eur J Surg Oncol 2021; 48:339-347. [PMID: 34304951 DOI: 10.1016/j.ejso.2021.07.014] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Revised: 03/28/2021] [Accepted: 07/15/2021] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND To investigate the prognostic value of dual-energy CT (DECT) based radiomics to predict disease-free survival (DFS) and overall survival (OS) for patients with advanced gastric cancer (AGC) after neoadjuvant chemotherapy (NAC). METHODS From January 2014 to December 2018, a total of 156 AGC patients were enrolled and randomly allocated into a training cohort and a testing cohort at a ratio of 2:1. Volume of interest of primary tumor was delineated on eight image series. Four feature sets derived from pre-NAC and delta radiomics were generated for each survival arm. Random survival forest was used for generating the optimal radiomics signature (RS). Statistical metrics for model evaluation included Harrell's concordance index (C-index) and the average cumulative/dynamic AUC throughout follow-up. A clinical model and a combined Rad-clinical model were built for comparison. RESULTS The pre-IU (derived from iodine uptake images before NAC) RS performed best for DFS and OS in the testing cohort (C-indices, 0.784 and 0.698; the average cumulative/dynamic AUCs, 0.80 and 0.77). When compared with the clinical model, the radiomics model had significantly higher C-index to predict DFS in the testing cohort (0.784 vs. 0.635, p < 0.001), but no statistical difference was found for OS (0.698 vs. 0.680, p = 0.473). The combined Rad-clinical models showed improved performance in the testing cohort, with C-indices of 0.810 and 0.710 for DFS and OS, respectively. CONCLUSION DECT-derived radiomics serves as a promising non-invasive biomarker to predict survival for AGC patients after NAC, providing an opportunity for transforming proper treatment.
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Affiliation(s)
- Yong Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No 197, Rui Jin 2nd Road, Shanghai, 200025, China
| | - Fei Yuan
- Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No 197, Rui Jin 2nd Road, Shanghai, 200025, China
| | - Lingyun Wang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No 197, Rui Jin 2nd Road, Shanghai, 200025, China
| | - Elsie Li
- Shanghai Engineering Research Center for Broadband Technologies & Applications, No 150, Honggu Road, Shanghai, 200336, China
| | - Zhihan Xu
- Siemens Healthineers Ltd, No. 278, Zhouzhu Road, Shanghai, 201318, China
| | - Michael Wels
- Department of Diagnostic Imaging Computed Tomography Image Analytics, Siemens Healthcare GmbH, Siemensstr, 391301, Forchheim, Germany
| | - Weiwu Yao
- Department of Radiology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 738, Yuyuan Road, Shanghai, 200050, China
| | - Huan Zhang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, No 197, Rui Jin 2nd Road, Shanghai, 200025, China.
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Gentili F, Monteleone I, Mazzei FG, Luzzi L, Del Roscio D, Guerrini S, Volterrani L, Mazzei MA. Advancement in Diagnostic Imaging of Thymic Tumors. Cancers (Basel) 2021; 13:cancers13143599. [PMID: 34298812 PMCID: PMC8303549 DOI: 10.3390/cancers13143599] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 07/13/2021] [Accepted: 07/16/2021] [Indexed: 01/25/2023] Open
Abstract
Simple Summary Diagnostic imaging is pivotal for the diagnosis and staging of thymic tumors. It is important to distinguish thymoma and other tumor histotypes amenable to surgery from lymphoma. Furthermore, in cases of thymoma, it is necessary to differentiate between early and advanced disease before surgery since patients with locally advanced tumors require neoadjuvant chemotherapy for improving survival. This review aims to provide to radiologists a full spectrum of findings of thymic neoplasms using traditional and innovative imaging modalities. Abstract Thymic tumors are rare neoplasms even if they are the most common primary neoplasm of the anterior mediastinum. In the era of advanced imaging modalities, such as functional MRI, dual-energy CT, perfusion CT and radiomics, it is possible to improve characterization of thymic epithelial tumors and other mediastinal tumors, assessment of tumor invasion into adjacent structures and detection of secondary lymph nodes and metastases. This review aims to illustrate the actual state of the art in diagnostic imaging of thymic lesions, describing imaging findings of thymoma and differential diagnosis.
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Affiliation(s)
- Francesco Gentili
- Unit of Diagnostic Imaging, Department of Radiological Sciences, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy; (F.G.M.); (S.G.)
- Correspondence:
| | - Ilaria Monteleone
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy; (I.M.); (D.D.R.); (L.V.); (M.A.M.)
| | - Francesco Giuseppe Mazzei
- Unit of Diagnostic Imaging, Department of Radiological Sciences, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy; (F.G.M.); (S.G.)
| | - Luca Luzzi
- Thoracic Surgery Unit, Department of Medical, Surgical and Neuro Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy;
| | - Davide Del Roscio
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy; (I.M.); (D.D.R.); (L.V.); (M.A.M.)
| | - Susanna Guerrini
- Unit of Diagnostic Imaging, Department of Radiological Sciences, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy; (F.G.M.); (S.G.)
| | - Luca Volterrani
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy; (I.M.); (D.D.R.); (L.V.); (M.A.M.)
| | - Maria Antonietta Mazzei
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, 53100 Siena, Italy; (I.M.); (D.D.R.); (L.V.); (M.A.M.)
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Nardone V, Boldrini L, Grassi R, Franceschini D, Morelli I, Becherini C, Loi M, Greto D, Desideri I. Radiomics in the Setting of Neoadjuvant Radiotherapy: A New Approach for Tailored Treatment. Cancers (Basel) 2021; 13:cancers13143590. [PMID: 34298803 PMCID: PMC8303203 DOI: 10.3390/cancers13143590] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 07/12/2021] [Accepted: 07/14/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary This review based on a literature search aims at showing the impact of Texture Analysis in the prediction of response to neoadjuvant radiotherapy and/or chemoradiotherapy. The manuscript explores radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma and rectal cancer in order to shed a light in the setting of neoadjuvant radiotherapy that can be used to tailor the best subsequent therapeutical strategy. Abstract Introduction: Neoadjuvant radiotherapy is currently used mainly in locally advanced rectal cancer and sarcoma and in a subset of non-small cell lung cancer and esophageal cancer, whereas in other diseases it is under investigation. The evaluation of the efficacy of the induction strategy is made possible by performing imaging investigations before and after the neoadjuvant therapy and is usually challenging. In the last decade, texture analysis (TA) has been developed to help the radiologist to quantify and identify the parameters related to tumor heterogeneity, which cannot be appreciated by the naked eye. The aim of this narrative is to review the impact of TA on the prediction of response to neoadjuvant radiotherapy and or chemoradiotherapy. Materials and Methods: Key references were derived from a PubMed query. Hand searching and ClinicalTrials.gov were also used. Results: This paper contains a narrative report and a critical discussion of radiomics approaches in different fields of neoadjuvant radiotherapy, including esophageal cancer, lung cancer, sarcoma, and rectal cancer. Conclusions: Radiomics can shed a light on the setting of neoadjuvant therapies that can be used to tailor subsequent approaches or even to avoid surgery in the future. At the same, these results need to be validated in prospective and multicenter trials.
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Affiliation(s)
- Valerio Nardone
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (V.N.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Luca Boldrini
- Radiation Oncology Unit, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Rome, Italy;
| | - Roberta Grassi
- Department of Precision Medicine, University of Campania “L. Vanvitelli”, 80138 Naples, Italy; (V.N.); (R.G.)
- Italian Society of Medical and Interventional Radiology (SIRM), SIRM Foundation, 20122 Milan, Italy
| | - Davide Franceschini
- Radiotherapy and Radiosurgery Department, IRCCS Humanitas Research Hospital, via Manzoni 56, 20089 Milan, Italy;
| | - Ilaria Morelli
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy;
- Correspondence: ; Tel.: +39-055-7947719
| | - Carlotta Becherini
- Department of Biomedical, Experimental and Clinical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy;
| | - Mauro Loi
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
| | - Daniela Greto
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
| | - Isacco Desideri
- Radiation Oncology Unit, Azienda Ospedaliero Universitaria Careggi, 50139 Florence, Italy; (M.L.); (D.G.); (I.D.)
- Department of Experimental and Clinical Biomedical Sciences “Mario Serio”, University of Florence, 50134 Florence, Italy
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Mazzei MA, Di Giacomo L, Bagnacci G, Nardone V, Gentili F, Lucii G, Tini P, Marrelli D, Morgagni P, Mura G, Baiocchi GL, Pittiani F, Volterrani L, Roviello F. Delta-radiomics and response to neoadjuvant treatment in locally advanced gastric cancer-a multicenter study of GIRCG (Italian Research Group for Gastric Cancer). Quant Imaging Med Surg 2021; 11:2376-2387. [PMID: 34079708 DOI: 10.21037/qims-20-683] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
Background To predict response to neoadjuvant chemotherapy (NAC) of gastric cancer (GC), prior to surgery, would be pivotal to customize patient treatment. The aim of this study is to investigate the reliability of computed tomography (CT) texture analysis (TA) in predicting the histo-pathological response to NAC in patients with resectable locally advanced gastric cancer (AGC). Methods Seventy (40 male, mean age 63.3 years) patients with resectable locally AGC, treated with NAC and radical surgery, were included in this retrospective study from 5 centers of the Italian Research Group for Gastric Cancer (GIRCG). Population was divided into two groups: 29 patients from one center (internal cohort for model development and internal validation) and 41 from other four centers (external cohort for independent external validation). Gross tumor volume (GTV) was segmented on each pre- and post-NAC multidetector CT (MDCT) image by using a dedicated software (RayStation), and 14 TA parameters were then extrapolated. Correlation between TA parameters and complete pathological response (tumor regression grade, TRG1), was initially investigated for the internal cohort. The univariate significant variables were tested on the external cohort and multivariate logistic analysis was performed. Results In multivariate logistic regression the only significant TA variable was delta gray-level co-occurrence matrix (GLCM) contrast (P=0.001, Nagelkerke R2: 0.546 for the internal cohort and P=0.014, Nagelkerke R2: 0.435 for the external cohort). Receiver operating characteristic (ROC) curves, generated from the logistic regression of all the patients, showed an area under the curve (AUC) of 0.763. Conclusions Post-NAC GLCM contrast and dissimilarity and delta GLCM contrast TA parameters seem to be reliable for identifying patients with locally AGC responder to NAC.
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Affiliation(s)
- Maria Antonietta Mazzei
- Department of Medical, Surgical and Neuro Sciences, University of Siena and Department of Radiological Sciences, Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Letizia Di Giacomo
- Department of Medical, Surgical and Neuro Sciences, University of Siena and Department of Radiological Sciences, Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Giulio Bagnacci
- Department of Medical, Surgical and Neuro Sciences, University of Siena and Department of Radiological Sciences, Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | | | - Francesco Gentili
- Section of Radiology, Unit of Surgical Sciences, University of Parma, Parma, Italy
| | - Gabriele Lucii
- Department of Medical, Surgical and Neuro Sciences, University of Siena and Department of Radiological Sciences, Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Paolo Tini
- Unit of Radiation Oncology, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Daniele Marrelli
- Department of Medical, Surgical and Neuro Sciences, Unit of Surgical Oncology, University of Siena, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Paolo Morgagni
- Department of General Surgery, Morgagni-Pierantoni Hospital, Forlì, Italy
| | - Gianni Mura
- Department of Surgery, San Donato Hospital, Arezzo, Italy
| | - Gian Luca Baiocchi
- Department of Clinical and Experimental Studies, Surgical Clinic, University of Brescia, Brescia, Italy
| | - Frida Pittiani
- Department of Radiology, ASST Spedali Civili Brescia, Brescia, Italy
| | - Luca Volterrani
- Department of Medical, Surgical and Neuro Sciences, University of Siena and Department of Radiological Sciences, Unit of Diagnostic Imaging, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Franco Roviello
- Department of Medical, Surgical and Neuro Sciences, Unit of Surgical Oncology, University of Siena, Azienda Ospedaliera Universitaria Senese, Siena, Italy
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Delta radiomics for rectal cancer response prediction using low field magnetic resonance guided radiotherapy: an external validation. Phys Med 2021; 84:186-191. [PMID: 33901863 DOI: 10.1016/j.ejmp.2021.03.038] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Revised: 03/20/2021] [Accepted: 03/29/2021] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION A recent study performed on 16 locally advanced rectal cancer (LARC) patients treated using magnetic resonance guided radiotherapy (MRgRT) has identified two delta radiomics features as predictors of clinical complete response (cCR) after neoadjuvant radio-chemotherapy (nCRT). This study aims to validate these features (ΔLleast and Δglnu) on an external larger dataset, expanding the analysis also for pathological complete response (pCR) prediction. METHODS A total of 43 LARC patients were enrolled: Gross Tumour Volume (GTV) was delineated on T2/T1* MR images acquired during MRgRT and the two delta features were calculated. Receiver Operating Characteristic (ROC) curve analysis was performed on the 16 cases of the original study and the best cut-off value was identified. The performance of ΔLleast and Δglnu was evaluated at the best cut-off value. RESULTS On the original dataset of 16 patients, ΔLleast reported an AUC of 0.81 for cCR and 0.93 for pCR, while Δglnu 0.72 and 0.54 respectively. The best cut-off values of ΔLleast was 0.73 for both outcomes, while Δglnu reported 0.54 for cCR and 0.93 for pCR. At the external validation, ΔLleast showed an accuracy of 81% for cCR and 79% for pCR, while Δglnu reported 63% for cCR and 40% for pCR. CONCLUSION The accuracy of ΔLleast in predicting cCR and pCR is significantly higher than those obtained considering Δglnu, but inferior if compared with other image-based biomarker, such as the early-regression index. Studies with larger cohorts of patients are recommended to further investigate the role of delta radiomic features in MRgRT.
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Wang R, Liu H, Liang P, Zhao H, Li L, Gao J. Radiomics analysis of CT imaging for differentiating gastric neuroendocrine carcinomas from gastric adenocarcinomas. Eur J Radiol 2021; 138:109662. [PMID: 33774440 DOI: 10.1016/j.ejrad.2021.109662] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Revised: 01/29/2021] [Accepted: 03/16/2021] [Indexed: 02/07/2023]
Abstract
PURPOSE To develop and evaluate a CT-based radiomics nomogram for differentiating gastric neuroendocrine carcinomas (NECs) from gastric adenocarcinomas (ADCs). METHODS CT images of 63 patients with gastric NECs were collected retrospectively, and 63 patients with gastric ADCs were selected as the control group. Univariate analysis was used to identify the significant factors of clinical characteristics and CT findings for differentiating gastric NECs from ADCs. Radiomics analysis was applied to CT images of unenhanced, arterial phase and venous phase, respectively. A radiomics nomogram incorporating the radiomics signature and the subjective CT findings was developed and its diagnostic ability was evaluated. The diagnostic performances of CT findings model, radiomics signature and radiomics nomogram were compared using DeLong test. RESULTS The tumor margin and lymph node (LN) metastasis were independent predictors for differentiating gastric NECs from ADCs. The radiomics signature based on venous phase presented superior AUC of 0.798 [95 % confidence interval (CI), 0.657-0.938] in validation cohort. The nomogram incorporated the radiomics signature, tumor margin and LN metastasis showed AUCs of 0.821 (95 %CI: 0.725-0.895) in the primary cohort and 0.809 (95 %CI: 0.649-0.918) in the validation cohort. Moreover, the radiomics nomogram showed good discrimination and calibration. The diagnostic performance of CT findings model was significantly lower than that of radiomics nomogram (p = 0.001) and radiomics signature (p = 0.025). CONCLUSIONS Radiomics analysis exhibited good performance in differentiating gastric NECs from ADCs, and the radiomics nomogram may have significant clinical implications on preoperative detection of gastric malignant tumors.
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Affiliation(s)
- Rui Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Huan Liu
- Advanced Application Team, GE Healthcare, Shanghai, 201203, China
| | - Pan Liang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Huiping Zhao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Liming Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China
| | - Jianbo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, Henan, China.
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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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Vacca G, Reginelli A, Urraro F, Sangiovanni A, Bruno F, Di Cesare E, Cappabianca S, Vanzulli A. Magnetic resonance severity index assessed by T1-weighted imaging for acute pancreatitis: correlation with clinical outcomes and grading of the revised Atlanta classification-a narrative review. Gland Surg 2021; 9:2312-2320. [PMID: 33447582 DOI: 10.21037/gs-20-554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Acute pancreatitis (AP) is a common disease that may involve pancreas and peripancreatic tissues with a prevalence of up to 50 per 100,000 individuals for year. The Atlanta classification was assessed for the first time in 1992 and modified in 2012 in order to describe morphological features of AP and its complications. AP can be morphologically distinguished in two main types: interstitial edematous pancreatitis (IEP) and necrotizing pancreatitis (NEP). This classification is very important because the presence of necrosis is directly linked to local or systemic complications, hospital stays and death. Magnetic resonance (MR) is very useful to characterize morphological features in AP and its abdominal complications. Particularly we would like to underline the diagnostic, staging and prognostic role of T1-weighted images with fat suppression that could be significant to assess many features of the AP inflammatory process and its complications (detection of the pancreatic contour, pancreatic necrosis, presence of haemorrhage). Signs of inflammatory and edema are instead observed by T1-weighted images. MR cholangiopancreatography (MRCP) is necessary to study the main pancreatic duct and the extrahepatic biliary tract and contrast-enhancement magnetic resonance imaging (MRI) allows to assess the extent of necrosis and vascular injuries.
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Affiliation(s)
- Giovanna Vacca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Fabrizio Urraro
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Angelo Sangiovanni
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Federico Bruno
- Department of Biotechnology and Applied Clinical Sciences, University of L'Aquila, L'Aquila, Italy
| | - Ernesto Di Cesare
- Department of Life, Health and Environmental Sciences, University of L'Aquila, L'Aquila, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania "L. Vanvitelli", Naples, Italy
| | - Angelo Vanzulli
- Department of Radiology, University "La Statale" of Milan, Milan, Italy
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Sun Z, Jin L, Zhang S, Duan S, Xing W, Hu S. Preoperative prediction for lauren type of gastric cancer: A radiomics nomogram analysis based on CT images and clinical features. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2021; 29:675-686. [PMID: 34024809 DOI: 10.3233/xst-210888] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
PURPOSE To investigate feasibility of predicting Lauren type of gastric cancer based on CT radiomics nomogram before operation. MATERIALS AND METHODS The clinical data and pre-treatment CT images of 300 gastric cancer patients with Lauren intestinal or diffuse type confirmed by postoperative pathology were retrospectively analyzed, who were randomly divided into training set and testing set with a ratio of 2:1. Clinical features were compared between the two Lauren types in the training set and testing set, respectively. Gastric tumors on CT images were manually segmented using ITK-SNAP software, and radiomic features of the segmented tumors were extracted, filtered and minimized using the least absolute shrinkage and selection operator (LASSO) regression to select optimal features and develop radiomics signature. A nomogram was constructed with radiomic features and clinical characteristics to predict Lauren type of gastric cancer. Clinical model, radiomics signature model, and the nomogram model were compared using the receiver operating characteristic (ROC) curve analysis with area under the curve (AUC). The calibration curve was used to test the agreement between prediction probability and actual clinical findings, and the decision curve was performed to assess the clinical usage of the nomogram model. RESULTS In clinical features, Lauren type of gastric cancer relate to age and CT-N stage of patients (all p < 0.05). Radiomics signature was developed with the retained 10 radiomic features. The nomogram was constructed with the 2 clinical features and radiomics signature. Among 3 prediction models, performance of the nomogram was the best in predicting Lauren type of gastric cancer, with the respective AUC, accuracy, sensitivity and specificity of 0.864, 78.0%, 90.0%, 70.0%in the testing set. In addition, the calibration curve showed a good agreement between prediction probability and actual clinical findings (p > 0.05). CONCLUSION The nomogram combining radiomics signature and clinical features is a useful tool with the increased value to predict Lauren type of gastric cancer.
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Affiliation(s)
- Zongqiong Sun
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China
| | - Linfang Jin
- Department of Pathology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China
| | - Shuai Zhang
- General Electric Company (GE) Healthcare China, Pudong New Town, Shanghai, China
| | - Shaofeng Duan
- General Electric Company (GE) Healthcare China, Pudong New Town, Shanghai, China
| | - Wei Xing
- Department of Radiology, Third Affiliated Hospital of Soochow University, First people's Hospital of Changzhou City, Jiangsu, China
| | - Shudong Hu
- Department of Radiology, Affiliated Hospital of Jiangnan University, Wuxi City, Jiangsu, China
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Gentili F, Guerrini S, Mazzei FG, Monteleone I, Di Meglio N, Sansotta L, Perrella A, Puglisi S, De Filippo M, Gennaro P, Volterrani L, Castagna MG, Dotta F, Mazzei MA. Dual energy CT in gland tumors: a comprehensive narrative review and differential diagnosis. Gland Surg 2020; 9:2269-2282. [PMID: 33447579 DOI: 10.21037/gs-20-543] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Dual energy CT (DECT)with image acquisition at two different photon X-ray levels allows the characterization of a specific tissue or material/elements, the extrapolation of virtual unenhanced and monoenergetic images, and the quantification of iodine uptake; such special capabilities make the DECT the perfect technique to support oncological imaging for tumor detection and characterization and treatment monitoring, while concurrently reducing the dose of radiation and iodine and improving the metal artifact reduction. Even though its potential in the field of oncology has not been fully explored yet, DECT is already widely used today thanks to the availability of different CT technologies, such as dual-source, single-source rapid-switching, single-source sequential, single-source twin-beam and dual-layer technologies. Moreover DECT technology represents the future of the imaging innovation and it is subject to ongoing development that increase according its clinical potentiality, in particular in the field of oncology. This review points out recent state-of-the-art in DECT applications in gland tumors, with special focus on its potential uses in the field of oncological imaging of endocrine and exocrine glands.
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Affiliation(s)
- Francesco Gentili
- Unit of Diagnostic Imaging, Department of Radiological Sciences, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Susanna Guerrini
- Unit of Diagnostic Imaging, Department of Radiological Sciences, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Francesco Giuseppe Mazzei
- Unit of Diagnostic Imaging, Department of Radiological Sciences, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Ilaria Monteleone
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Nunzia Di Meglio
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Letizia Sansotta
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Armando Perrella
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Sara Puglisi
- Unit of Radiology, Department of Medicine and Surgery, Azienda Ospedaliero-Universitaria di Parma, Parma, Italy
| | - Massimo De Filippo
- Unit of Radiology, Department of Medicine and Surgery, Azienda Ospedaliero-Universitaria di Parma, Parma, Italy
| | - Paolo Gennaro
- Department of Maxillofacial Surgery, University of Siena, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Luca Volterrani
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Maria Grazia Castagna
- Unit of Endocrinology, Department of Medical, Surgical and Neuro Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Francesco Dotta
- Unit of Diabetology, Department of Medical, Surgical and Neuro Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
| | - Maria Antonietta Mazzei
- Unit of Diagnostic Imaging, Department of Medical, Surgical and Neuro Sciences and of Radiological Sciences, University of Siena, Azienda Ospedaliero-Universitaria Senese, Siena, Italy
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Wang L, Gong J, Huang X, Lin G, Zheng B, Chen J, Xie J, Lin R, Duan Q, Lin W. CT-based radiomics nomogram for preoperative prediction of No.10 lymph nodes metastasis in advanced proximal gastric cancer. Eur J Surg Oncol 2020; 47:1458-1465. [PMID: 33261951 DOI: 10.1016/j.ejso.2020.11.132] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 09/28/2020] [Accepted: 11/20/2020] [Indexed: 02/08/2023] Open
Abstract
INTRODUCTION Preoperative diagnosis of No.10 lymph nodes (LNs) metastases in advanced proximal gastric cancer (APGC) patients remains a challenge. The aim of this study was to develop a CT-based radiomics nomogram for identification of No.10 LNs status in APGCs. MATERIALS AND METHODS A total of 515 patients with primary APGCs were retrospectively selected and divided into a training cohort (n = 340) and a validation cohort (n = 175). Total incidence of No.10 LNM was 12.4% (64/515). CT based radiomics nomogram combining with radiomic signature calculated from venous CT imaging features and CT-defined No.10 LNs status evaluated by radiologists was built and tested to predict the No.10 LNs status in APGCs. RESULTS CT based radiomics nomogram yielded classification accuracy with areas under ROC curves, AUC = 0.896 and 0.814 in training and validation cohort, respectively, while radiomic signature and radiologist' diagnosis based on contrast-enhanced CT images yielded lower AUCs ranging in 0.742-0.866 and 0.619-0.685, respectively. In the specificity higher than 80%, the sensitivity of using radiomics nomogram, radiomic signature and radiologists' evaluation to detect No.10 LNs positive cases was 82.8% (53/64), 67.2% (43/64) and 39.1% (25/64), respectively. CONCLUSIONS The CT-based radiomics nomogram provides a promising and more effective method to yield high accuracy in identification of No.10 LNs metastases in APGC patients.
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Affiliation(s)
- Lili Wang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, 270 Dongan Road, Shanghai, 200023, China; Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, China
| | - Xinming Huang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Guifang Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Bin Zheng
- School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, 73019, USA
| | - Jingming Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Jiangao Xie
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Ruolan Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Qing Duan
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Weiwen Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
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Gastrointestinal stromal tumors (GIST): a proposal of a "CT-based predictive model of Miettinen index" in predicting the risk of malignancy. Abdom Radiol (NY) 2020; 45:2989-2996. [PMID: 31506758 DOI: 10.1007/s00261-019-02209-7] [Citation(s) in RCA: 30] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
Abstract
PURPOSE To identify the predictors of malignancy on CT for the evaluation of gastrointestinal stromal tumors (GIST) by correlating CT findings with the mitotic index in order to propose a "CT-based predictive model of Miettinen index." METHODS One radiologist and one resident in radiology with 14- and 4-year experience in oncological field reviewed the CT findings of 42 patients by consensus, with respect to lesion site, size, contour, tumor growth pattern, enhancing pattern, degree of enhancement of tumor, percentage of tumor necrosis, mesenteric fat infiltration, ulceration, calcification, regional lymphadenopathy, direct invasion to adjacent organs, and distant metastasis. All parameters were correlated with the mitotic index evaluated at histopathological analysis following surgery. Normality of variables was evaluated using Shapiro-Wilk test. Pearson's correlation test was used to assess the interaction between variables. The diagnostic accuracy percentage of tumor necrosis was measured by receiver operating characteristic (ROC) analysis for detecting whether the number of mitosis per 50 high-power fields was > 5. RESULTS A significant statistical correlation was found between percentage of tumor necrosis and the mitotic index (p < 0.005), dimension, and location of the tumor. CONCLUSION CT could be an accurate technique in the prediction of malignancy of GIST in a CT risk assessment system, based on the location of the tumor, its size, and the percentage of tumor necrosis.
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Wang X, Sun J, Zhang W, Yang X, Zhu C, Pan B, Zeng Y, Xu J, Chen X, Shen X. Use of radiomics to extract splenic features to predict prognosis of patients with gastric cancer. Eur J Surg Oncol 2020; 46:1932-1940. [PMID: 32694053 DOI: 10.1016/j.ejso.2020.06.021] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 06/04/2020] [Accepted: 06/12/2020] [Indexed: 01/25/2023] Open
Abstract
INTRODUCTION Radiomics allows for mining of imaging data to examine tissue characteristics non-invasively, which can be used to predict the prognosis of a patient. This study explored the use of imaging techniques to evaluate splenic tissue characteristics to predict the prognosis of patients with gastric cancer. MATERIALS AND METHODS Computed tomography images from patients with gastric cancer were collected retrospectively. Splenic image characteristics, extracted with pyradiomics, of patients in the training group were randomly divided. Characteristics with a P value < 0.1 were selected for lasso regression to construct a survival risk model. Models for high-and low-risk groups were established. Patients were divided into the high- and low-risk groups for univariate and multivariate regression analysis of survival-related factors, and a visual prognostic prediction model was established. RESULTS The splenic characteristic prognostic model was consistent in the training and verification groups (p < 0.001 and p = 0.016, respectively). The two groups that displayed different splenic characteristics showed no statistical difference in other basic data except the tumour-node-metastasis (pTNM) stage (p = 0.007). Univariate and multivariate analysis of survival risk factors showed that splenic characteristics (p = 0.042), age (p < 0.001), tumor location (p = 0.002), and pTNM stage (p < 0.001) were independent risk factors for survival. The prognostic prediction model combined with splenic characteristics significantly improved the accuracy of prognosis, predicting one-and three-year survival rates. CONCLUSION Splenic features extracted from imaging technology can accurately predict the long-term survival of patients with gastric cancer. Splenic characteristic grouping can effectively improve the accuracy of survival prediction and gastric cancer prognosis.
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Affiliation(s)
- Xiang Wang
- Department of Gastrointestinal Surgery, The 2nd Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jing Sun
- Department of Gastrointestinal Surgery, The 2nd Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Weiteng Zhang
- Department of Gastrointestinal Surgery, The 2nd Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xinxin Yang
- Department of Gastrointestinal Surgery, The 2nd Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Ce Zhu
- Department of Gastrointestinal Surgery, The 2nd Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Bujian Pan
- Department of Gastrointestinal Surgery, The 2nd Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yunpeng Zeng
- Department of Gastrointestinal Surgery, The 2nd Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Jingxuan Xu
- Department of Gastrointestinal Surgery, The 2nd Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xiaodong Chen
- Department of Gastrointestinal Surgery, The 2nd Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.
| | - Xian Shen
- Department of Gastrointestinal Surgery, The 2nd Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China.
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Sabatino V, Russo U, D'Amuri F, Bevilacqua A, Pagnini F, Milanese G, Gentili F, Nizzoli R, Tiseo M, Pedrazzi G, De Filippo M. Pneumothorax and pulmonary hemorrhage after CT-guided lung biopsy: incidence, clinical significance and correlation. Radiol Med 2020; 126:170-177. [PMID: 32377914 DOI: 10.1007/s11547-020-01211-0] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2019] [Accepted: 04/20/2020] [Indexed: 11/25/2022]
Abstract
OBJECTIVES To evaluate the incidence and clinical significance of pneumothorax (PTX) and pulmonary hemorrhage (PH) after CT-guided lung biopsy (CT-LB). To test correlations of PTX and chest tube insertion (CTI) with PH and other imaging and procedural parameters. METHODS Pre-procedural CT and CT-LB scans of 904 patients were examined. Incidence of PTX and PH and PH location (type-1 along needle track; type-2 perilesional) and severity according to its thickness (low grade < 6 mm; high grade > 6 mm) were recorded. PTX was considered clinically significant if treated with CTI, PH if treated with endoscopic/endovascular procedure. Binary logistic regression analyses were used to determine the effects of different imaging and procedural parameters on the likelihood to develop PTX, CTI and PH and to define their correlation. RESULTS PTX occurred in 306/904 cases (33.8%); CTI was required in 18/306 (5.9%). PH occurred in 296/904 cases (32.7%), and no case required treatment. Nodule-to-pleura distance (ORPTX = 1.052; ORCTI = 1.046; ORPH 1.077), emphysema (ORPTX = 1.287; ORPH = 0.573), procedure time (ORPTX = 1.019; ORCTI = 1.039; ORPH = 1.019), target size (ORPTX = 0.982; ORPH = 0.968) and needle gauge (ORPTX = 0.487; ORCTI = 4.311; ORPH = 2.070) showed statistically significant correlation to PTX, CTI and PH. Type-1 PH showed a protective effect against PTX and CTI (ORPTX = 0.503; ORCTI = 0.416). CONCLUSION PTX and PH have similar incidence after CT-guided lung biopsy. PH along needle track may represent a protective factor against development of PTX and against PTX requiring CTI.
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Affiliation(s)
- Vittorio Sabatino
- Department of Medicine and Surgery, Unit of Radiology, University of Parma, Parma, Italy
| | - Umberto Russo
- Department of Medicine and Surgery, Unit of Radiology, University of Parma, Parma, Italy
| | - Fabiano D'Amuri
- Department of Medicine and Surgery, Unit of Radiology, University of Parma, Parma, Italy
| | - Andrea Bevilacqua
- Department of Medicine and Surgery, Unit of Radiology, University of Parma, Parma, Italy
| | - Francesco Pagnini
- Department of Medicine and Surgery, Unit of Radiology, University of Parma, Parma, Italy
| | - Gianluca Milanese
- Department of Medicine and Surgery, Unit of Radiology, University of Parma, Parma, Italy
| | - Francesco Gentili
- Department of Medicine and Surgery, Unit of Radiology, University of Parma, Parma, Italy.
| | - Rita Nizzoli
- Division of Oncology, University of Parma, Parma, Italy
| | | | - Giuseppe Pedrazzi
- Department of Medicine and Surgery, University of Parma, Parma, Italy
| | - Massimo De Filippo
- Department of Medicine and Surgery, Unit of Radiology, University of Parma, Parma, Italy
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Delta-radiomics increases multicentre reproducibility: a phantom study. Med Oncol 2020; 37:38. [PMID: 32236847 DOI: 10.1007/s12032-020-01359-9] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Accepted: 03/06/2020] [Indexed: 12/19/2022]
Abstract
Texture analysis (TA) can provide quantitative features from medical imaging that can be correlated to clinical endpoints. The challenges relevant to robustness of radiomics features have been analyzed by many researchers, as it seems to be influenced by acquisition and reconstruction protocols. Delta-texture analysis (D-TA), conversely, consist in the analysis of TA feature variations at different acquisition times, usually before and after a therapy. Aim of this study was to investigate the influence of different CT scanners and acquisition parameters in the robustness of TA and D-TA. We scanned a commercial phantom (CIRS model 467, Gammex, Middleton, WI, USA), that is used for the calibration of electron density, two times by varying the disposition of plugs, using three different scanners. After the segmentation, we extracted TA features with LifeX and calculated TA features and D-TA features, defined as the variation of each TA parameters extracted from the same position by varying the plugs with the formula (Y-X)/X. The robustness of TA and D-TA features were then tested with intraclass coefficient correlation (ICC) analysis. The reliability of TA parameters across different scans, with different acquisition parameters and ROI positions has shown poor reliability in 12/37 and moderate reliability in the remaining 25/37, with no parameters showing good reliability. The reliability of D-TA, conversely, showed poor reliability in 10/37 parameters, moderate reliability in 10/37 parameters, and good reliability in 17/37 parameters. The comparison between TA and D-TA ICCs showed a significant difference for the whole group of parameters (p:0.004) and for the subclasses of GLCM parameters (p:0.033), whereas for the other subclasses of matrices (GLRLM, NGLDM, GLZLM, Histogram), the difference was not significant. D-TA features seem to be more robust than TA features. These findings reinforce the potentiality for using D-TA features for early assessment of treatment response and for developing tailored therapies. More work is needed in a clinical setting to confirm the results of the present study.
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Wang Y, Liu W, Yu Y, Han W, Liu JJ, Xue HD, Lei J, Jin ZY, Yu JC. Potential value of CT radiomics in the distinction of intestinal-type gastric adenocarcinomas. Eur Radiol 2020; 30:2934-2944. [PMID: 32020404 DOI: 10.1007/s00330-019-06629-3] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 11/18/2019] [Accepted: 12/13/2019] [Indexed: 12/14/2022]
Abstract
OBJECTIVE The purpose of the study was to investigate the role of CT radiomics for the preoperative distinction of intestinal-type gastric adenocarcinomas. MATERIALS AND METHODS A total of 187 consecutive patients with preoperative contrast CT examination and pathologically proven gastric adenocarcinoma were retrospectively collected. Patients were divided into a training set (n = 150) and a test set (n = 37). Arterial phase (AP), portal phase (PP), and delay phase (DP) images were retrieved for analysis. A dedicated postprocessing software was used to segment the lesions and extract radiomics features. Random forest (RF) algorithm was applied to construct the classifier models. A nomogram was developed by incorporating multiphase radiomics scores. Receiver operating characteristic (ROC) curves were used to evaluate the performance of the radiomics model and nomogram in both sets. RESULTS The radiomics model showed a favorable capability in the distinction of intestinal-type gastric adenocarcinomas. The areas under curves (AUCs) of the AP, PP, and DP radiomics models were 0.754 (95% CI: 0.676, 0.820), 0.815 (95% CI: 0.744, 0.874), and 0.764 (95% CI: 0.688, 0.829) in the training set, respectively, which were confirmed in the test set with AUCs of 0.742 (95% CI: 0.572, 0.872), 0.775 (95% CI: 0.608, 0.895), and 0.857 (95% CI: 0.703, 0.950), respectively. The nomogram yielded excellent performance for distinguishing intestinal-type adenocarcinomas in both sets, with AUCs of 0.928 (95%: 0.875, 0.964) and 0.904 (95% CI: 0.761, 0.976). CONCLUSIONS The multiphase CT radiomics nomogram holds promise for the individual preoperative discrimination of intestinal-type gastric adenocarcinoma. KEY POINTS • CT radiomics has a potential role in the distinction of intestinal-type gastric adenocarcinomas. • Single-phase enhanced CT-based radiomics showed favorable capability in distinguishing intestinal-type tumors. • The nomogram which incorporates the multiphase radiomics scores could facilitate the individual prediction of intestinal-type lesions.
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Affiliation(s)
- Yue Wang
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Bejing, 100730, People's Republic of China
| | - Wei Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Bejing, 100730, People's Republic of China
| | - Yang Yu
- CT Collaboration, Siemens Healthineers Ltd, 59# Beizhan Road, Shenyang, 110013, People's Republic of China
| | - Wei Han
- Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences, School of Basic Medicine, Chinese Academy of Medical Sciences, Peking Union Medical College, No. 5, Dongdansantiao Street, Beijing, 100005, People's Republic of China
| | - Jing-Juan Liu
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Bejing, 100730, People's Republic of China
| | - Hua-Dan Xue
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Bejing, 100730, People's Republic of China
| | - Jing Lei
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Bejing, 100730, People's Republic of China
| | - Zheng-Yu Jin
- Department of Radiology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Bejing, 100730, People's Republic of China.
| | - Jian-Chun Yu
- Department of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Beijing, 100730, People's Republic of China.
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Nardone V, Tini P, Pastina P, Botta C, Reginelli A, Carbone SF, Giannicola R, Calabrese G, Tebala C, Guida C, Giudice A, Barbieri V, Tassone P, Tagliaferri P, Cappabianca S, Capasso R, Luce A, Caraglia M, Mazzei MA, Pirtoli L, Correale P. Radiomics predicts survival of patients with advanced non-small cell lung cancer undergoing PD-1 blockade using Nivolumab. Oncol Lett 2019; 19:1559-1566. [PMID: 31966081 DOI: 10.3892/ol.2019.11220] [Citation(s) in RCA: 35] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Accepted: 08/13/2019] [Indexed: 12/27/2022] Open
Abstract
Immune checkpoint blockade is an emerging anticancer strategy, and Nivolumab is a human mAb to PD-1 that is used in the treatment of a number of different malignancies, including non-small cell lung cancer (NSCLC), kidney cancer, urothelial carcinoma and melanoma. Although the use of Nivolumab prolongs survival in a number of patients, this treatment is hampered by high cost. Therefore, the identification of predictive markers of response to treatment in patients is required. In this context, PD-1/PDL1 blockade antitumor effects occur through the reactivation of a pre-existing immune response, and the efficacy of these effects is strictly associated with the presence of necrosis, hypoxia and inflammation at the tumour sites. It has been indicated that these events can be evaluated by specific assessments using a computed tomography (CT) texture analysis (TA) or radiomics. Therefore, a retrospective study was performed, which aimed to evaluate the potential use of this analysis in the identification of patients with NSCLC who may benefit from Nivolumab treatment. A retrospective analysis was performed of 59 patients with metastatic NSCLC who received Nivolumab treatment between January 2015 and July 2017 at Siena University Hospital (35 patients, training dataset), Catanzaro University Hospital and Reggio Calabria Grand Metropolitan Hospital, Italy (24 patients, validation dataset). Pre- and post-contrast CT sequences were used to contour the gross tumour volume (GTV) of the target lesions prior to Nivolumab treatment. The impact of variations on contouring was analysed using two delineations, which were performed on each patient, and the TA parameters were tested for reliability using the Intraclass Coefficient Correlation method (ICC). All analyses for the current study were performed using LifeX Software©. Imaging, clinical and pathological parameters were correlated with progression free survival and overall survival (OS) using Kaplan Meier analysis. An external validation testing was performed for the TA Score using the validation dataset. A total of 59 patients were included in the analysis of the present study. The reliability ICC analysis of 14 TA parameters indicated a highly reproducibility (ICC >0.70, single measure) in 12 (85%) pre- contrast and 13 (93%) post-contrast exams. A specific cut-off was detected for each of the following parameters: volume (score 1 >36 ml), histogram entropy (score 1 > 1.30), compacity (score 1 <3), gray level co-occurrence matrix (GLCM)-entropy (score 1 >1.80), GLCM-Dissimilarity (score 1 >5) and GLCM-Correlation (score 1<0.54). The global texture score allowed the classification of two subgroups of Low (Score 0-1; 36 patients; 61%) and High Risk patients (Score >1; 23 patients; 39%) that respectively, showed a median OS of 26 (mean +/- SD: 18 +/- 1.98 months; 95% CI 14-21 months) and 5 months (mean +/- SD: 6 +/- 0.99 months; 95% CI: 4-8 months; P=0.002). The current study indicated that TA parameters can identify patients that will benefit from PD-1 blockage by defining the radiological settings that are potentially suggestive of an active immune response. These results require further confirmation in prospective trials.
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Affiliation(s)
- Valerio Nardone
- Unit of Radiation Oncology, Integrated Department of Diagnostic Radiology and Radiotherapy, Ospedale del Mare, I-80147 Naples, Italy
| | - Paolo Tini
- Unit of Radiation Oncology, Oncology Department, University Hospital of Siena, I-53100 Siena, Italy
| | - Pierpaolo Pastina
- Unit of Radiation Oncology, Oncology Department, University Hospital of Siena, I-53100 Siena, Italy
| | - Cirino Botta
- Integrated Area of Medical Oncology, AOU Mater Domini and Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, I-88100 Catanzaro, Italy
| | - Alfonso Reginelli
- Department of Precision Medicine, University of Campania 'L. Vanvitelli', I-80138 Naples, Italy
| | - Salvatore Francesco Carbone
- Unit of Medical Imaging, Emergency Department and Diagnostic Services, University Hospital of Siena, I-53100 Siena, Italy
| | - Rocco Giannicola
- Unit of Medical Oncology, Oncology Department, Grand Metropolitan Hospital 'Bianchi Melacrino Morelli' Reggio Calabria I-89124, Italy
| | - Grazia Calabrese
- Unit of Radiology, Department of Diagnostic Services, Grand Metropolitan Hospital 'Bianchi Melacrino Morelli' Reggio Calabria I-89124, Italy
| | - Carmela Tebala
- Unit of Radiology, Department of Diagnostic Services, Grand Metropolitan Hospital 'Bianchi Melacrino Morelli' Reggio Calabria I-89124, Italy
| | - Cesare Guida
- Unit of Radiation Oncology, Integrated Department of Diagnostic Radiology and Radiotherapy, Ospedale del Mare, I-80147 Naples, Italy
| | - Aldo Giudice
- Epidemiology Unit, IRCCS Istituto Nazionale Tumori 'Fondazione G. Pascale', I-80131 Naples, Italy
| | - Vito Barbieri
- Integrated Area of Medical Oncology, AOU Mater Domini and Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, I-88100 Catanzaro, Italy
| | - Pierfrancesco Tassone
- Integrated Area of Medical Oncology, AOU Mater Domini and Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, I-88100 Catanzaro, Italy
| | - Pierosandro Tagliaferri
- Integrated Area of Medical Oncology, AOU Mater Domini and Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, I-88100 Catanzaro, Italy
| | - Salvatore Cappabianca
- Department of Precision Medicine, University of Campania 'L. Vanvitelli', I-80138 Naples, Italy
| | - Rosanna Capasso
- Department of Precision Medicine, University of Campania 'L. Vanvitelli', I-80138 Naples, Italy
| | - Amalia Luce
- Department of Precision Medicine, University of Campania 'L. Vanvitelli', I-80138 Naples, Italy
| | - Michele Caraglia
- Department of Precision Medicine, University of Campania 'L. Vanvitelli', I-80138 Naples, Italy
| | - Maria Antonietta Mazzei
- Unit of Medical Imaging, Emergency Department and Diagnostic Services, University Hospital of Siena, I-53100 Siena, Italy
| | - Luigi Pirtoli
- Unit of Radiation Oncology, Oncology Department, University Hospital of Siena, I-53100 Siena, Italy
| | - Pierpaolo Correale
- Unit of Medical Oncology, Oncology Department, Grand Metropolitan Hospital 'Bianchi Melacrino Morelli' Reggio Calabria I-89124, Italy
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Gentili F, Pelini V, Lucii G, Luzzi L, Mazzei FG, Fausto A, Volterrani L, Mazzei MA. Update in diagnostic imaging of the thymus and anterior mediastinal masses. Gland Surg 2019; 8:S188-S207. [PMID: 31559186 DOI: 10.21037/gs.2019.05.06] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Anterior mediastinal masses include a wide spectrum of malignant and benign pathologies with a large percentage represented by thymic lesions. Distinguishing these masses on diagnostic imaging is fundamental to guide the proper management for each patient. This review illustrates possibilities and limits of different imaging modalities to diagnose a lesion of the anterior mediastinum with particular attention to thymic disease.
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Affiliation(s)
- Francesco Gentili
- Department of Medicine, Surgery and Neuro Sciences, Diagnostic Imaging, University of Siena, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Veronica Pelini
- Department of Medicine, Surgery and Neuro Sciences, Diagnostic Imaging, University of Siena, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Gabriele Lucii
- Department of Medicine, Surgery and Neuro Sciences, Diagnostic Imaging, University of Siena, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Luca Luzzi
- Thoracic Surgery Unit, Department of Medicine, Surgery and Neuro Sciences, Diagnostic Imaging, University of Siena, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | | | - Alfonso Fausto
- Diagnostic Imaging, University of Siena, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Luca Volterrani
- Department of Medicine, Surgery and Neuro Sciences, Diagnostic Imaging, University of Siena, Azienda Ospedaliera Universitaria Senese, Siena, Italy
| | - Maria Antonietta Mazzei
- Department of Medicine, Surgery and Neuro Sciences, Diagnostic Imaging, University of Siena, Azienda Ospedaliera Universitaria Senese, Siena, Italy
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Park JE, Park SY, Kim HJ, Kim HS. Reproducibility and Generalizability in Radiomics Modeling: Possible Strategies in Radiologic and Statistical Perspectives. Korean J Radiol 2019; 20:1124-1137. [PMID: 31270976 PMCID: PMC6609433 DOI: 10.3348/kjr.2018.0070] [Citation(s) in RCA: 197] [Impact Index Per Article: 39.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2019] [Accepted: 04/07/2019] [Indexed: 02/06/2023] Open
Abstract
Radiomics, which involves the use of high-dimensional quantitative imaging features for predictive purposes, is a powerful tool for developing and testing medical hypotheses. Radiologic and statistical challenges in radiomics include those related to the reproducibility of imaging data, control of overfitting due to high dimensionality, and the generalizability of modeling. The aims of this review article are to clarify the distinctions between radiomics features and other omics and imaging data, to describe the challenges and potential strategies in reproducibility and feature selection, and to reveal the epidemiological background of modeling, thereby facilitating and promoting more reproducible and generalizable radiomics research.
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Affiliation(s)
- Ji Eun Park
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Seo Young Park
- Department of Clinical Epidemiology and Biostatistics, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Hwa Jung Kim
- Department of Clinical Epidemiology and Biostatistics, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea.
| | - Ho Sung Kim
- Department of Radiology and Research Institute of Radiology, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
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Qiu Q, Duan J, Duan Z, Meng X, Ma C, Zhu J, Lu J, Liu T, Yin Y. Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: impact of tumor segmentation variability. Quant Imaging Med Surg 2019; 9:453-464. [PMID: 31032192 DOI: 10.21037/qims.2019.03.02] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Background The reproducibility and non-redundancy of radiomic features are challenges in accelerating the clinical translation of radiomics. In this study, we focused on the robustness and non-redundancy of radiomic features extracted from computed tomography (CT) scans in hepatocellular carcinoma (HCC) patients with respect to different tumor segmentation methods. Methods Arterial enhanced CT images were retrospectively randomly obtained from 106 patients. As a training data set, 26 HCC patients were used to calculate the features' reproducibility and redundancy. Another data set (55 HCC patients and 25 healthy volunteers) was used for classification. The GrowCut and GraphCut semiautomatic segmentation methods were implemented in 3D Slicer software by two independent observers, and manual delineation was performed by five abdominal radiation oncologists to acquire the gross tumor volume (GTV). Seventy-one radiomic features were extracted from GTVs using Imaging Biomarker Explorer (IBEX) software, including 17 tumor intensity statistical features, 16 shape features and 38 textural features. For each radiomic feature, intraclass correlation coefficient (ICC) and hierarchical clustering were used to quantify its reproducibility and redundancy. Features with ICC values greater than 0.75 were considered reproducible. To generate the number of non-redundancy feature subgroups, the R2 statistic method was used. Then, a classification model was built using a support vector machine (SVM) algorithm with 10-fold cross validation, and area under ROC curve (AUC) was used to evaluate the utility of non-redundant feature extraction by hierarchical clustering. Results The percentages of excellent reproducible features in the manual delineation group, GraphCut and GrowCut segmentation group were 69% [49], 73% [52] and 79% [56], respectively. Sixty-five percent [46] of the features showed strong robustness for all segmentation methods. The optimal number of cluster subgroup were 9, 13 and 11 for manual delineation, GraphCut and GrowCut segmentation, respectively. The optimal cluster subgroup number was 6 for all groups when the collectively high reproducibility features were selected for clustering. The receiver operating characteristic (ROC) analysis of radiomics classification model with and without feature reduction for healthy liver and HCC had an AUC value of 0.857 and 0.721 respectively. Conclusions Our study demonstrates that variations exist in the reproducibility of quantitative imaging features extracted from tumor regions segmented using different methods. The reproducibility and non-redundancy of the radiomic features rely greatly on the tumor segmentation in HCC CT images. We recommend that the most reliable and uniform radiomic features should be selected in the clinical use of radiomics. Classification experiments with feature reduction showed that radiomic features were effective in identifying healthy liver and HCC.
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Affiliation(s)
- Qingtao Qiu
- Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan 250117, China
| | - Jinghao Duan
- Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan 250117, China
| | - Zuyun Duan
- Department of Radiology, Second People's Hospital of Dongying City, Dongying 257335, China
| | - Xiangjuan Meng
- Shandong Eye Hospital, Shandong Eye Institute, Shandong Academy of Medical Sciences, Jinan 250021, China
| | - Changsheng Ma
- Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan 250117, China
| | - Jian Zhu
- Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan 250117, China
| | - Jie Lu
- Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan 250117, China
| | - Tonghai Liu
- Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan 250117, China
| | - Yong Yin
- Department of Radiation Oncology, Shandong Cancer Hospital Affiliated to Shandong University, Jinan 250117, China
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Sun ZQ, Hu SD, Li J, Wang T, Duan SF, Wang J. Radiomics study for differentiating gastric cancer from gastric stromal tumor based on contrast-enhanced CT images. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2019; 27:1021-1031. [PMID: 31640109 DOI: 10.3233/xst-190574] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
PURPOSE To test the feasibility of differentiate gastric cancer from gastric stromal tumor using a radiomics study based on contrast-enhanced CT images. MATERIALS AND METHODS The contrast-enhanced CT image data of 60 patients with gastric cancer and 40 patients with gastric stromal tumor confirmed by postoperative pathology were retrospectively analyzed. First, CT images were read by two senior radiologists to acquire subjective CT signs model, including perigastric fatty infiltration, perigastric enlarged lymph nodes, the enhancement and growth modes of gastric tumors. Second, the manual segmentation of gastric tumors from the CT images was performed by the two radiologists to extract radiomics features via ITK-SNAP software, and to construct radiomics signature model. Finally, a diagnostic model integrated with subjective CT signs and radiomics signatures was constructed. The diagnostic efficacy of three models in differentiating gastric cancer from gastric stromal tumor was compared by using receiver operating characteristic curves (ROC). RESULTS There are statistically significant differences between the gastric cancer and gastric stromal tumor in the perigastric enlarged lymph nodes, growth mode and radiomics signature (p < 0.05). The area under ROC curve (AUC), sensitivity and accuracy of subjective CT signs model were the lowest among the three models. While the combined model yields the highest AUC value (0.903), specificity (93.33%) and accuracy (86.00%) among the three models (p = 0.03). CONCLUSION The diagnostic model integrating subjective CT signs and radiomics signature can improve the diagnostic accuracy of gastric tumors.
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Affiliation(s)
- Zong-Qiong Sun
- Department of Radiology, Affiliated Hospital of Jiangnan University, The Fourth People's Hospital of Wuxi City, Jiangsu Province, China
| | - Shu-Dong Hu
- Department of Radiology, Affiliated Hospital of Jiangnan University, The Fourth People's Hospital of Wuxi City, Jiangsu Province, China
| | - Jie Li
- Department of Intervention Affiliated Hospital of Jiangnan University, The Fourth People's Hospital of Wuxi City, Jiangsu Province, China
| | - Teng Wang
- Department of Oncology, Affiliated Hospital of Jiangnan University, The Fourth People's Hospital of Wuxi City, Jiangsu Province, China
| | - Shao-Feng Duan
- General Electric Company (GE) Healthcare China, Pudong New Town, Shanghai, China
| | - Jun Wang
- Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China
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