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Inchingolo R, Maino C, Cannella R, Vernuccio F, Cortese F, Dezio M, Pisani AR, Giandola T, Gatti M, Giannini V, Ippolito D, Faletti R. Radiomics in colorectal cancer patients. World J Gastroenterol 2023; 29:2888-2904. [PMID: 37274803 PMCID: PMC10237092 DOI: 10.3748/wjg.v29.i19.2888] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 04/07/2023] [Accepted: 04/25/2023] [Indexed: 05/16/2023] Open
Abstract
The main therapeutic options for colorectal cancer are surgical resection and adjuvant chemotherapy in non-metastatic disease. However, the evaluation of the overall adjuvant chemotherapy benefit in patients with a high risk of recurrence is challenging. Radiological images can represent a source of data that can be analyzed by using automated computer-based techniques, working on numerical information coded within Digital Imaging and Communications in Medicine files: This image numerical analysis has been named "radiomics". Radiomics allows the extraction of quantitative features from radiological images, mainly invisible to the naked eye, that can be further analyzed by artificial intelligence algorithms. Radiomics is expanding in oncology to either understand tumor biology or for the development of imaging biomarkers for diagnosis, staging, and prognosis, prediction of treatment response and diseases monitoring and surveillance. Several efforts have been made to develop radiomics signatures for colorectal cancer patient using computed tomography (CT) images with different aims: The preoperative prediction of lymph node metastasis, detecting BRAF and RAS gene mutations. Moreover, the use of delta-radiomics allows the analysis of variations of the radiomics parameters extracted from CT scans performed at different timepoints. Most published studies concerning radiomics and magnetic resonance imaging (MRI) mainly focused on the response of advanced tumors that underwent neoadjuvant therapy. Nodes status is the main determinant of adjuvant chemotherapy. Therefore, several radiomics model based on MRI, especially on T2-weighted images and ADC maps, for the preoperative prediction of nodes metastasis in rectal cancer has been developed. Current studies mostly focused on the applications of radiomics in positron emission tomography/CT for the prediction of survival after curative surgical resection and assessment of response following neoadjuvant chemoradiotherapy. Since colorectal liver metastases develop in about 25% of patients with colorectal carcinoma, the main diagnostic tasks of radiomics should be the detection of synchronous and metachronous lesions. Radiomics could be an additional tool in clinical setting, especially in identifying patients with high-risk disease. Nevertheless, radiomics has numerous shortcomings that make daily use extremely difficult. Further studies are needed to assess performance of radiomics in stratifying patients with high-risk disease.
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Affiliation(s)
- Riccardo Inchingolo
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Cesare Maino
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Roberto Cannella
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BiND), University of Palermo, Palermo 90127, Italy
| | - Federica Vernuccio
- Institute of Radiology, University Hospital of Padova, Padova 35128, Italy
| | - Francesco Cortese
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Michele Dezio
- Unit of Interventional Radiology, F. Miulli Hospital, Acquaviva delle Fonti 70021, Italy
| | - Antonio Rosario Pisani
- Interdisciplinary Department of Medicine, Section of Nuclear Medicine, University of Bari “Aldo Moro”, Bari 70121, Italy
| | - Teresa Giandola
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Marco Gatti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Valentina Giannini
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
| | - Davide Ippolito
- Department of Radiology, Fondazione IRCCS San Gerardo dei Tintori, Monza 20900, Italy
| | - Riccardo Faletti
- Department of Surgical Sciences, University of Turin, Turin 10126, Italy
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Panic J, Defeudis A, Mazzetti S, Rosati S, Giannetto G, Micilotta M, Vassallo L, Gatti M, Regge D, Balestra G, Giannini V. A fully automatic deep learning algorithm to segment rectal Cancer on MR images: a multi-center study. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:5066-5069. [PMID: 36086406 DOI: 10.1109/embc48229.2022.9871326] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The aim of the study is to present and tune a fully automatic deep learning algorithm to segment colorectal cancers (CRC) on MR images, based on a U-Net structure. It is a multicenter study, including 3 different Italian institutions, that used 4 different MRI scanners. Two of them were used for training and tuning the systems, while the other two for the validation. The implemented algorithm consists of a pre-processing step to normalize and to highlight the tumoral area, followed by the CRC segmentation using different U-net structures. Automatic masks were compared with manual segmentations performed by three experienced radiologists, one at each center. The two best performing systems (called mdl2 and mdl3), obtained a median Dice Similarity Coefficient of 0.68(mdl2) - 0.69(mdl3), precision of 0.75(md/2) - 0.71(md/3), and recall of 0.69(mdl2) - 0.73(mdl3) on the validation set. Both systems reached high detection rates, 0.98 and 0.95, respectively, on the validation set. These encouraging results, if confirmed on larger dataset, might improve the management of patients with CRC, since it can be used as a fast and precise tool for further radiomics analyses. Clinical Relevance - To provide a reliable tool able to automatically segment CRC tumors that can be used as first step in future radiomics studies aimed at predicting response to chemotherapy and personalizing treatment.
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Filitto G, Coppola F, Curti N, Giampieri E, Dall’Olio D, Merlotti A, Cattabriga A, Cocozza MA, Taninokuchi Tomassoni M, Remondini D, Pierotti L, Strigari L, Cuicchi D, Guido A, Rihawi K, D’Errico A, Di Fabio F, Poggioli G, Morganti AG, Ricciardiello L, Golfieri R, Castellani G. Automated Prediction of the Response to Neoadjuvant Chemoradiotherapy in Patients Affected by Rectal Cancer. Cancers (Basel) 2022; 14:cancers14092231. [PMID: 35565360 PMCID: PMC9100060 DOI: 10.3390/cancers14092231] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 04/22/2022] [Accepted: 04/27/2022] [Indexed: 02/04/2023] Open
Abstract
Simple Summary Colorectal cancer is the second most malignant tumor per number of deaths after lung cancer and the third per number of new cases after breast and lung cancer. The correct and rapid identification (i.e., segmentation of the cancer regions) is a fundamental task for correct patient diagnosis. In this study, we propose a novel automated pipeline for the segmentation of MRI scans of patients with LARC in order to predict the response to nCRT using radiomic features. This study involved the retrospective analysis of T2-weighted MRI scans of 43 patients affected by LARC. The segmentation of tumor areas was on par or better than the state-of-the-art results, but required smaller sample sizes. The analysis of radiomic features allowed us to predict the TRG score, which agreed with the state-of-the-art results. Abstract Background: Rectal cancer is a malignant neoplasm of the large intestine resulting from the uncontrolled proliferation of the rectal tract. Predicting the pathologic response of neoadjuvant chemoradiotherapy at an MRI primary staging scan in patients affected by locally advanced rectal cancer (LARC) could lead to significant improvement in the survival and quality of life of the patients. In this study, the possibility of automatizing this estimation from a primary staging MRI scan, using a fully automated artificial intelligence-based model for the segmentation and consequent characterization of the tumor areas using radiomic features was evaluated. The TRG score was used to evaluate the clinical outcome. Methods: Forty-three patients under treatment in the IRCCS Sant’Orsola-Malpighi Polyclinic were retrospectively selected for the study; a U-Net model was trained for the automated segmentation of the tumor areas; the radiomic features were collected and used to predict the tumor regression grade (TRG) score. Results: The segmentation of tumor areas outperformed the state-of-the-art results in terms of the Dice score coefficient or was comparable to them but with the advantage of considering mucinous cases. Analysis of the radiomic features extracted from the lesion areas allowed us to predict the TRG score, with the results agreeing with the state-of-the-art results. Conclusions: The results obtained regarding TRG prediction using the proposed fully automated pipeline prove its possible usage as a viable decision support system for radiologists in clinical practice.
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Affiliation(s)
- Giuseppe Filitto
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy; (G.F.); (G.C.)
| | - Francesca Coppola
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (F.C.); (A.C.); (M.A.C.); (M.T.T.); (R.G.)
- SIRM Foundation, Italian Society of Medical and Interventional Radiology, 40138 Bologna, Italy
| | - Nico Curti
- eDIMES Lab, Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
- INFN Bologna, 40127 Bologna, Italy;
- Correspondence: (N.C.); (E.G.)
| | - Enrico Giampieri
- eDIMES Lab, Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy
- Correspondence: (N.C.); (E.G.)
| | - Daniele Dall’Olio
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy; (D.D.); (A.M.)
| | - Alessandra Merlotti
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy; (D.D.); (A.M.)
| | - Arrigo Cattabriga
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (F.C.); (A.C.); (M.A.C.); (M.T.T.); (R.G.)
| | - Maria Adriana Cocozza
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (F.C.); (A.C.); (M.A.C.); (M.T.T.); (R.G.)
| | - Makoto Taninokuchi Tomassoni
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (F.C.); (A.C.); (M.A.C.); (M.T.T.); (R.G.)
| | - Daniel Remondini
- INFN Bologna, 40127 Bologna, Italy;
- Department of Physics and Astronomy, University of Bologna, 40127 Bologna, Italy; (D.D.); (A.M.)
| | - Luisa Pierotti
- Sant’Orsola-Malpighi Polyclinic, IRCCS Azienda Ospedaliero-Universitaria di Bologna, 40138 Bologna, Italy;
| | - Lidia Strigari
- Department of Medical Physics, Sant’Orsola-Malpighi Polyclinic, IRCCS Azienda Ospedaliero-Universitaria di Bologn, 40138 Bologna, Italy;
| | - Dajana Cuicchi
- Medical and Surgical Department of Digestive, Hepatic and Endocrine-Metabolic Diseases, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (D.C.); (G.P.)
| | - Alessandra Guido
- Department of Radiation Oncology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (A.G.); (A.G.M.)
| | - Karim Rihawi
- Division of Medical Oncology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (K.R.); (F.D.F.)
| | - Antonietta D’Errico
- Pathology Unit, Department of Specialized, Experimental and Diagnostic Medicine, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy;
| | - Francesca Di Fabio
- Division of Medical Oncology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (K.R.); (F.D.F.)
| | - Gilberto Poggioli
- Medical and Surgical Department of Digestive, Hepatic and Endocrine-Metabolic Diseases, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (D.C.); (G.P.)
| | - Alessio Giuseppe Morganti
- Department of Radiation Oncology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (A.G.); (A.G.M.)
| | - Luigi Ricciardiello
- Department of Medical and Surgical Science, University of Bologna, 40138 Bologna, Italy;
| | - Rita Golfieri
- Department of Radiology, IRCCS Azienda Ospedaliera-Universitaria di Bologna, 40138 Bologna, Italy; (F.C.); (A.C.); (M.A.C.); (M.T.T.); (R.G.)
| | - Gastone Castellani
- Department of Experimental, Diagnostic and Specialty Medicine, University of Bologna, 40138 Bologna, Italy; (G.F.); (G.C.)
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Giannini V, Pusceddu L, Defeudis A, Nicoletti G, Cappello G, Mazzetti S, Sartore-Bianchi A, Siena S, Vanzulli A, Rizzetto F, Fenocchio E, Lazzari L, Bardelli A, Marsoni S, Regge D. Delta-Radiomics Predicts Response to First-Line Oxaliplatin-Based Chemotherapy in Colorectal Cancer Patients with Liver Metastases. Cancers (Basel) 2022; 14:cancers14010241. [PMID: 35008405 PMCID: PMC8750408 DOI: 10.3390/cancers14010241] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Revised: 12/29/2021] [Accepted: 12/30/2021] [Indexed: 12/11/2022] Open
Abstract
Simple Summary Oxaliplatin-based chemotherapy remains the mainstay of first-line therapy in patients with metastatic colorectal cancer (mCRC). Unfortunately, only approximately 60% of treated patients achieve response, and half of responders will experience an early onset of disease progression. Furthermore, some individuals will develop a mixed response due to the emergence of resistant tumor subclones. The ability to predicting which patients will acquire resistance could help them avoid the unnecessary toxicity of oxaliplatin therapies. Furthermore, sorting out lesions that do not respond, in the context of an overall good response, could trigger further investigation into their mutational landscape, providing mechanistic insight towards the planning of a more comprehensive treatment. In this study, we validated a delta-radiomics signature capable of predicting response to oxaliplatin-based first-line treatment of individual liver colorectal cancer metastases. Findings could pave the way to a more personalized treatment of patients with mCRC. Abstract The purpose of this paper is to develop and validate a delta-radiomics score to predict the response of individual colorectal cancer liver metastases (lmCRC) to first-line FOLFOX chemotherapy. Three hundred one lmCRC were manually segmented on both CT performed at baseline and after the first cycle of first-line FOLFOX, and 107 radiomics features were computed by subtracting textural features of CT at baseline from those at timepoint 1 (TP1). LmCRC were classified as nonresponders (R−) if they showed progression of disease (PD), according to RECIST1.1, before 8 months, and as responders (R+), otherwise. After feature selection, we developed a decision tree statistical model trained using all lmCRC coming from one hospital. The final output was a delta-radiomics signature subsequently validated on an external dataset. Sensitivity, specificity, positive (PPV), and negative (NPV) predictive values in correctly classifying individual lesions were assessed on both datasets. Per-lesion sensitivity, specificity, PPV, and NPV were 99%, 94%, 95%, 99%, 85%, 92%, 90%, and 87%, respectively, in the training and validation datasets. The delta-radiomics signature was able to reliably predict R− lmCRC, which were wrongly classified by lesion RECIST as R+ at TP1, (93%, averaging training and validation set, versus 67% of RECIST). The delta-radiomics signature developed in this study can reliably predict the response of individual lmCRC to oxaliplatin-based chemotherapy. Lesions forecasted as poor or nonresponders by the signature could be further investigated, potentially paving the way to lesion-specific therapies.
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Affiliation(s)
- Valentina Giannini
- Department of Surgical Sciences, University of Turin, 10124 Turin, Italy; (A.D.); (G.N.); (S.M.); (D.R.)
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy; (L.P.); (G.C.)
- Correspondence:
| | - Laura Pusceddu
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy; (L.P.); (G.C.)
| | - Arianna Defeudis
- Department of Surgical Sciences, University of Turin, 10124 Turin, Italy; (A.D.); (G.N.); (S.M.); (D.R.)
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy; (L.P.); (G.C.)
| | - Giulia Nicoletti
- Department of Surgical Sciences, University of Turin, 10124 Turin, Italy; (A.D.); (G.N.); (S.M.); (D.R.)
| | - Giovanni Cappello
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy; (L.P.); (G.C.)
| | - Simone Mazzetti
- Department of Surgical Sciences, University of Turin, 10124 Turin, Italy; (A.D.); (G.N.); (S.M.); (D.R.)
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy; (L.P.); (G.C.)
| | - Andrea Sartore-Bianchi
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, 20122 Milan, Italy; (A.S.-B.); (S.S.); (A.V.)
- Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy
| | - Salvatore Siena
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, 20122 Milan, Italy; (A.S.-B.); (S.S.); (A.V.)
- Niguarda Cancer Center, Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy
| | - Angelo Vanzulli
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, 20122 Milan, Italy; (A.S.-B.); (S.S.); (A.V.)
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy;
| | - Francesco Rizzetto
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, 20162 Milan, Italy;
| | - Elisabetta Fenocchio
- Multidisciplinary Outpatient Oncology Clinic, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy;
| | - Luca Lazzari
- Precision Oncology, IFOM-The FIRC Institute of Molecular Oncology, 20139 Milan, Italy; (L.L.); (S.M.)
| | - Alberto Bardelli
- Candiolo Cancer Institute-FPO, IRCCS, 10060 Candiolo, Italy;
- Department of Oncology, University of Torino, 10060 Candiolo, Italy
| | - Silvia Marsoni
- Precision Oncology, IFOM-The FIRC Institute of Molecular Oncology, 20139 Milan, Italy; (L.L.); (S.M.)
| | - Daniele Regge
- Department of Surgical Sciences, University of Turin, 10124 Turin, Italy; (A.D.); (G.N.); (S.M.); (D.R.)
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, 10060 Candiolo, Italy; (L.P.); (G.C.)
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Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:9025470. [PMID: 34754327 PMCID: PMC8572604 DOI: 10.1155/2021/9025470] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 09/30/2021] [Accepted: 10/05/2021] [Indexed: 12/30/2022]
Abstract
Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.
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Cao B, Zhang KC, Wei B, Chen L. Status quo and future prospects of artificial neural network from the perspective of gastroenterologists. World J Gastroenterol 2021; 27:2681-2709. [PMID: 34135549 PMCID: PMC8173384 DOI: 10.3748/wjg.v27.i21.2681] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 03/29/2021] [Accepted: 04/22/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial neural networks (ANNs) are one of the primary types of artificial intelligence and have been rapidly developed and used in many fields. In recent years, there has been a sharp increase in research concerning ANNs in gastrointestinal (GI) diseases. This state-of-the-art technique exhibits excellent performance in diagnosis, prognostic prediction, and treatment. Competitions between ANNs and GI experts suggest that efficiency and accuracy might be compatible in virtue of technique advancements. However, the shortcomings of ANNs are not negligible and may induce alterations in many aspects of medical practice. In this review, we introduce basic knowledge about ANNs and summarize the current achievements of ANNs in GI diseases from the perspective of gastroenterologists. Existing limitations and future directions are also proposed to optimize ANN’s clinical potential. In consideration of barriers to interdisciplinary knowledge, sophisticated concepts are discussed using plain words and metaphors to make this review more easily understood by medical practitioners and the general public.
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Affiliation(s)
- Bo Cao
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Ke-Cheng Zhang
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Bo Wei
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
| | - Lin Chen
- Department of General Surgery & Institute of General Surgery, Chinese People’s Liberation Army General Hospital, Beijing 100853, China
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Radiomics and Magnetic Resonance Imaging of Rectal Cancer: From Engineering to Clinical Practice. Diagnostics (Basel) 2021; 11:diagnostics11050756. [PMID: 33922483 PMCID: PMC8146913 DOI: 10.3390/diagnostics11050756] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/08/2021] [Accepted: 04/21/2021] [Indexed: 12/24/2022] Open
Abstract
While cross-sectional imaging has seen continuous progress and plays an undiscussed pivotal role in the diagnostic management and treatment planning of patients with rectal cancer, a largely unmet need remains for improved staging accuracy, assessment of treatment response and prediction of individual patient outcome. Moreover, the increasing availability of target therapies has called for developing reliable diagnostic tools for identifying potential responders and optimizing overall treatment strategy on a personalized basis. Radiomics has emerged as a promising, still fully evolving research topic, which could harness the power of modern computer technology to generate quantitative information from imaging datasets based on advanced data-driven biomathematical models, potentially providing an added value to conventional imaging for improved patient management. The present study aimed to illustrate the contribution that current radiomics methods applied to magnetic resonance imaging can offer to managing patients with rectal cancer.
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