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Fiz F, Rossi N, Langella S, Conci S, Serenari M, Ardito F, Cucchetti A, Gallo T, Zamboni GA, Mosconi C, Boldrini L, Mirarchi M, Cirillo S, Ruzzenente A, Pecorella I, Russolillo N, Borzi M, Vara G, Mele C, Ercolani G, Giuliante F, Cescon M, Guglielmi A, Ferrero A, Sollini M, Chiti A, Torzilli G, Ieva F, Viganò L. Radiomics of Intrahepatic Cholangiocarcinoma and Peritumoral Tissue Predicts Postoperative Survival: Development of a CT-Based Clinical-Radiomic Model. Ann Surg Oncol 2024:10.1245/s10434-024-15457-9. [PMID: 38797789 DOI: 10.1245/s10434-024-15457-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Accepted: 04/28/2024] [Indexed: 05/29/2024]
Abstract
BACKGROUND For many tumors, radiomics provided a relevant prognostic contribution. This study tested whether the computed tomography (CT)-based textural features of intrahepatic cholangiocarcinoma (ICC) and peritumoral tissue improve the prediction of survival after resection compared with the standard clinical indices. METHODS All consecutive patients affected by ICC who underwent hepatectomy at six high-volume centers (2009-2019) were considered for the study. The arterial and portal phases of CT performed fewer than 60 days before surgery were analyzed. A manual segmentation of the tumor was performed (Tumor-VOI). A 5-mm volume expansion then was applied to identify the peritumoral tissue (Margin-VOI). RESULTS The study enrolled 215 patients. After a median follow-up period of 28 months, the overall survival (OS) rate was 57.0%, and the progression-free survival (PFS) rate was 34.9% at 3 years. The clinical predictive model of OS had a C-index of 0.681. The addition of radiomic features led to a progressive improvement of performances (C-index of 0.71, including the portal Tumor-VOI, C-index of 0.752 including the portal Tumor- and Margin-VOI, C-index of 0.764, including all VOIs of the portal and arterial phases). The latter model combined clinical variables (CA19-9 and tumor pattern), tumor indices (density, homogeneity), margin data (kurtosis, compacity, shape), and GLRLM indices. The model had performance equivalent to that of the postoperative clinical model including the pathology data (C-index of 0.765). The same results were observed for PFS. CONCLUSIONS The radiomics of ICC and peritumoral tissue extracted from preoperative CT improves the prediction of survival. Both the portal and arterial phases should be considered. Radiomic and clinical data are complementary and achieve a preoperative estimation of prognosis equivalent to that achieved in the postoperative setting.
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Affiliation(s)
- Francesco Fiz
- Nuclear Medicine Unit, Department of Diagnostic Imaging, Ente Ospedaliero "Ospedali Galliera", Genoa, Italy
- Department of Nuclear Medicine and Clinical Molecular Imaging, University Hospital, Tübingen, Germany
| | - Noemi Rossi
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Milan, Italy
| | - Serena Langella
- Department of Digestive and Hepatobiliary Surgery, Mauriziano Umberto I Hospital, Turin, Italy
| | - Simone Conci
- Division of General and Hepatobiliary Surgery, Department of Surgical Sciences, Dentistry, Gynaecology and Pediatrics, University Hospital G.B. Rossi, University of Verona, Verona, Italy
| | - Matteo Serenari
- General Surgery and Transplant Unit, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant'Orsola-Malpighi Hospital, Bologna, Italy
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Francesco Ardito
- Hepatobiliary Surgery Unit, A. Gemelli Hospital, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Alessandro Cucchetti
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy
- Department of General Surgery, Morgagni-Pierantoni Hospital, Forlì, Italy
| | - Teresa Gallo
- Department of Radiology, Mauriziano Umberto I Hospital, Turin, Italy
| | - Giulia A Zamboni
- Department of Radiology, University Hospital G.B. Rossi, University of Verona, Verona, Italy
| | - Cristina Mosconi
- Department of Radiology, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant'Orsola-Malpighi Hospital, Bologna, Italy
| | - Luca Boldrini
- Department of Radiology, Radiation Oncology and Hematology, Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy
| | | | - Stefano Cirillo
- Department of Radiology, Mauriziano Umberto I Hospital, Turin, Italy
| | - Andrea Ruzzenente
- Division of General and Hepatobiliary Surgery, Department of Surgical Sciences, Dentistry, Gynaecology and Pediatrics, University Hospital G.B. Rossi, University of Verona, Verona, Italy
| | - Ilaria Pecorella
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
| | - Nadia Russolillo
- Department of Digestive and Hepatobiliary Surgery, Mauriziano Umberto I Hospital, Turin, Italy
| | - Martina Borzi
- Department of Radiology, University Hospital G.B. Rossi, University of Verona, Verona, Italy
| | - Giulio Vara
- Department of Radiology, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant'Orsola-Malpighi Hospital, Bologna, Italy
| | - Caterina Mele
- Hepatobiliary Surgery Unit, A. Gemelli Hospital, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Giorgio Ercolani
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy
- Department of General Surgery, Morgagni-Pierantoni Hospital, Forlì, Italy
| | - Felice Giuliante
- Hepatobiliary Surgery Unit, A. Gemelli Hospital, Università Cattolica del Sacro Cuore, Rome, Italy
| | - Matteo Cescon
- General Surgery and Transplant Unit, IRCCS, Azienda Ospedaliero-Universitaria di Bologna, Sant'Orsola-Malpighi Hospital, Bologna, Italy
- Department of Medical and Surgical Sciences, Alma Mater Studiorum, University of Bologna, Bologna, Italy
| | - Alfredo Guglielmi
- Division of General and Hepatobiliary Surgery, Department of Surgical Sciences, Dentistry, Gynaecology and Pediatrics, University Hospital G.B. Rossi, University of Verona, Verona, Italy
| | - Alessandro Ferrero
- Department of Digestive and Hepatobiliary Surgery, Mauriziano Umberto I Hospital, Turin, Italy
| | - Martina Sollini
- Department of Nuclear Medicine, IRCCS San Raffaele, Milan, Italy
- Faculty of Medicine, Università Vita-Salute San Raffaele, Milan, Italy
| | - Arturo Chiti
- Department of Nuclear Medicine, IRCCS San Raffaele, Milan, Italy
- Faculty of Medicine, Università Vita-Salute San Raffaele, Milan, Italy
| | - Guido Torzilli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy
- Division of Hepatobiliary and General Surgery, Department of Surgery, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy
| | - Francesca Ieva
- MOX Laboratory, Department of Mathematics, Politecnico di Milano, Milan, Italy
- CHDS - Center for Health Data Science, Human Technopole, Milan, Italy
| | - Luca Viganò
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
- Hepatobiliary Unit, Department of Minimally Invasive General and Oncologic Surgery, Humanitas Gavazzeni University Hospital, Bergamo, Italy.
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Li S, Yang X, Lu T, Yuan L, Zhang Y, Zhao J, Deng J, Xue C, Sun Q, Liu X, Zhang W, Zhou J. Extracellular volume fraction can predict the treatment response and survival outcome of colorectal cancer liver metastases. Eur J Radiol 2024; 175:111444. [PMID: 38531223 DOI: 10.1016/j.ejrad.2024.111444] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Revised: 03/09/2024] [Accepted: 03/21/2024] [Indexed: 03/28/2024]
Abstract
OBJECTIVE To assess the prognostic value of pre- and post-therapeutic changes in extracellular volume (ECV) fraction of liver metastases (LMs) for treatment response (TR) and survival outcomes in colorectal cancer liver metastases (CRLM). METHODS 186 LMs were confirmed by pathology or follow-up (Training: 130; Test: 56). We analyzed the changes in ECV fraction of LMs before and after 2 cycles of chemotherapy combined with bevacizumab. After 12 cycles, we evaluated the TR on LMs based on the RECIST v1.1. Relative changes in ECV fraction and Hounsfield Units (HU), defined as ΔECV and ΔHU, were associated with progression-free survival (PFS), overall survival (OS), and TR. We identified TR predictors with multivariate logistic regression and PFS, OS risk factors with COX analysis. RESULTS 186 LMs were classified as TR lesions (TR+: 84) and non-TR lesions (TR-:102). ΔECV, ΔHUA-E, and texture could distinguish the TR of LMs in training and test set (P < 0.05). ΔECV [Odds ratio (OR): 1.03; 95% Confidence interval (CI): 1.02-1.05, P < 0.01] was an independent predictor of TR-. Area under the curve (AUC), sensitivity and specificity of TR model in training and test set were 0.87, 0.84, 90.14%, 90.32%, 72.88%, 64.00%, respectively. High CRD_score indicates that patients have shorter PFS [Hazard ratio (HR): 2.01; 95%CI: 1.02-3.98, P = 0.045)] and OS (HR: 1.89, 95%CI: 1.04-3.42, P = 0.038). CONCLUSION ΔECV can be used as an independent predictor of TR of CRLM chemotherapy combined with bevacizumab.
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Affiliation(s)
- Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
| | - Xinmei Yang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Ting Lu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Long Yuan
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Yuting Zhang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Jun Zhao
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Qiu Sun
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Wenjuan Zhang
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Cuiyingmen No. 82, Chengguan District, Lanzhou 730030, China; Second Clinical School, Lanzhou University, Lanzhou, China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
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Hu H, Chi JC, Zhai B, Guo JH. CT-based radiomics analysis to predict local progression of recurrent colorectal liver metastases after microwave ablation. Medicine (Baltimore) 2023; 102:e36586. [PMID: 38206750 PMCID: PMC10754583 DOI: 10.1097/md.0000000000036586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 11/20/2023] [Indexed: 01/13/2024] Open
Abstract
The objective of this study is to establish and validate a radiomics nomogram for prediction of local tumor progression (LTP) after microwave ablation (MWA) for recurrent colorectal liver metastases (CRLM) after hepatic resection. We included 318 consecutive recurrent CRLM patients (216 of training while 102 of validation cohort) with contrast-enhanced computerized tomography images treated with MWA between January 2014 and October 2018. Support vector machine-generated radiomics signature was incorporated together with clinical information to establish a radiomics nomogram. Our constructed radiomics signature including 15 features (first-order intensity statistics features, shape and size-based features, gray level size zone/dependence matrix features) performed well in assessing LTP for both cohorts. With regard to its predictive performance, its C-index was 0.912, compared to the clinical or radiomics models only (c-statistic 0.89 and 0.75, respectively) in the training cohort. In the validation cohort, the radiomics nomogram had better performance (area under the curve = 0.89) compared to the radiomics and clinical models (0.85 and 0.69). According to decision curve analysis, our as-constructed radiomics nomogram showed high clinical utility. As revealed by survival analysis, LTP showed worse progression-free survival (3-year progression-free survival 42.6% vs 78.4%, P < .01). High-risk patients identified using this radiomics signature exhibited worse LTP compared with low-risk patients (3-year LTP 80.2% vs 48.6%, P < .01). A radiomics-based nomogram of pre-ablation computerized tomography imaging may be the precious biomarker model for predicting LTP and personalized risk stratification for recurrent CRLM after hepatic resection treated by MWA.
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Affiliation(s)
- Hao Hu
- Center of Interventional Radiology & Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China
- Basic Medicine Research and Innovation Center of Ministry of Education, Zhongda Hospital, Southeast University, Nanjing, China
- Department of Interventional Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jia Chang Chi
- Department of Interventional Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Bo Zhai
- Department of Interventional Oncology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Jin He Guo
- Center of Interventional Radiology & Vascular Surgery, Department of Radiology, Zhongda Hospital, Medical School, Southeast University, Nanjing, China
- Basic Medicine Research and Innovation Center of Ministry of Education, Zhongda Hospital, Southeast University, Nanjing, China
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Granata V, Fusco R, De Muzio F, Brunese MC, Setola SV, Ottaiano A, Cardone C, Avallone A, Patrone R, Pradella S, Miele V, Tatangelo F, Cutolo C, Maggialetti N, Caruso D, Izzo F, Petrillo A. Radiomics and machine learning analysis by computed tomography and magnetic resonance imaging in colorectal liver metastases prognostic assessment. LA RADIOLOGIA MEDICA 2023; 128:1310-1332. [PMID: 37697033 DOI: 10.1007/s11547-023-01710-w] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2023] [Accepted: 08/22/2023] [Indexed: 09/13/2023]
Abstract
OBJECTIVE The aim of this study was the evaluation radiomics analysis efficacy performed using computed tomography (CT) and magnetic resonance imaging in the prediction of colorectal liver metastases patterns linked to patient prognosis: tumor growth front; grade; tumor budding; mucinous type. Moreover, the prediction of liver recurrence was also evaluated. METHODS The retrospective study included an internal and validation dataset; the first was composed by 119 liver metastases from 49 patients while the second consisted to 28 patients with single lesion. Radiomic features were extracted using PyRadiomics. Univariate and multivariate approaches including machine learning algorithms were employed. RESULTS The best predictor to identify tumor growth was the Wavelet_HLH_glcm_MaximumProbability with an accuracy of 84% and to detect recurrence the best predictor was wavelet_HLH_ngtdm_Complexity with an accuracy of 90%, both extracted by T1-weigthed arterial phase sequence. The best predictor to detect tumor budding was the wavelet_LLH_glcm_Imc1 with an accuracy of 88% and to identify mucinous type was wavelet_LLH_glcm_JointEntropy with an accuracy of 92%, both calculated on T2-weigthed sequence. An increase statistically significant of accuracy (90%) was obtained using a linear weighted combination of 15 predictors extracted by T2-weigthed images to detect tumor front growth. An increase statistically significant of accuracy at 93% was obtained using a linear weighted combination of 11 predictors by the T1-weigthed arterial phase sequence to classify tumor budding. An increase statistically significant of accuracy at 97% was obtained using a linear weighted combination of 16 predictors extracted on CT to detect recurrence. An increase statistically significant of accuracy was obtained in the tumor budding identification considering a K-nearest neighbors and the 11 significant features extracted T1-weigthed arterial phase sequence. CONCLUSIONS The results confirmed the Radiomics capacity to recognize clinical and histopathological prognostic features that should influence the choice of treatments in colorectal liver metastases patients to obtain a more personalized therapy.
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Affiliation(s)
- Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy.
| | | | - Federica De Muzio
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Maria Chiara Brunese
- Department of Medicine and Health Sciences V. Tiberio, University of Molise, 86100, Campobasso, Italy
| | - Sergio Venanzio Setola
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
| | - Alessandro Ottaiano
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Claudia Cardone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Antonio Avallone
- Clinical Experimental Abdominal Oncology Unit, Istituto Nazionale Tumori, IRCCS Fondazione G. Pascale, 80131, Naples, Italy
| | - Renato Patrone
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Silvia Pradella
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Vittorio Miele
- Department of Radiology, Careggi University Hospital, Largo Brambilla 3, 50134, Florence, Italy
- SIRM Foundation, Italian Society of Medical and Interventional Radiology (SIRM), 20122, Milan, Italy
| | - Fabiana Tatangelo
- Division of Pathological Anatomy and Cytopathology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Carmen Cutolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, 84084, Salerno, Italy
| | - Nicola Maggialetti
- Department of Medical Science, Neuroscience and Sensory Organs (DSMBNOS), University of Bari "Aldo Moro", 70124, Bari, Italy
| | - Damiano Caruso
- Department of Medical Surgical Sciences and Translational Medicine, Radiology Unit-Sant'Andrea University Hospital, Sapienza-University of Rome, 00189, Rome, Italy
| | - Francesco Izzo
- Division of Hepatobiliary Surgical Oncology, Istituto Nazionale Tumori IRCCS Fondazione Pascale-IRCCS di Napoli, 80131, Naples, Italy
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori IRCCS Fondazione Pascale - IRCCS di Napoli, Naples, Italy
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Shimura Y, Komatsu S, Nagatani Y, Funakoshi Y, Sofue K, Kido M, Kuramitsu K, Gon H, Fukushima K, Urade T, So S, Yanagimoto H, Toyama H, Minami H, Fukumoto T. The Usefulness of Total Tumor Volume as a Prognostic Factor and in Selecting the Optimal Treatment Strategy of Chemotherapeutic Intervention in Patients with Colorectal Liver Metastases. Ann Surg Oncol 2023; 30:6603-6610. [PMID: 37386304 DOI: 10.1245/s10434-023-13746-3] [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: 02/09/2023] [Accepted: 05/31/2023] [Indexed: 07/01/2023]
Abstract
BACKGROUND Few reports have discussed the association between total tumor volume (TTV) and prognosis in patients with colorectal liver metastases (CRLM). The present study aimed to evaluate the usefulness of TTV for predicting recurrence-free survival and overall survival (OS) in patients receiving initial hepatic resection or chemotherapy, and to investigate the value of TTV as an indicator for optimal treatment selection for patients with CRLM. PATIENTS AND METHODS This retrospective cohort study included patients with CRLM who underwent hepatic resection (n = 93) or chemotherapy (n = 78) at the Kobe University Hospital. TTV was measured using 3D construction software and computed tomography images. RESULTS A TTV of 100 cm3 has been previously reported as a significant cut-off value for predicting OS of CRLM patients receiving initial hepatic resection. For patients receiving hepatic resection, the OS for those with a TTV ≥ 100 cm3 was significantly reduced compared with those with a TTV < 100 cm3. For patients receiving initial chemotherapy, there were no significant differences between the groups divided according to TTV cut-offs. Regarding OS of patients with TTV ≥ 100 cm3, there was no significant difference between hepatic resection and chemotherapy (p = 0.160). CONCLUSIONS TTV can be a predictive factor of OS for hepatic resection, unlike for initial chemotherapy treatment. The lack of significant difference in OS for CRLM patients with TTV ≥ 100 cm3, regardless of initial treatment, suggests that chemotherapeutic intervention preceding hepatic resection may be indicated for such patients.
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Affiliation(s)
- Yuhi Shimura
- Division of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Shohei Komatsu
- Division of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan.
| | - Yoshiaki Nagatani
- Department of Medical Oncology/Hematology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Yohei Funakoshi
- Department of Medical Oncology/Hematology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Keitaro Sofue
- Department of Radiology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Masahiro Kido
- Division of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Kaori Kuramitsu
- Department of Medical Oncology/Hematology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Hidetoshi Gon
- Division of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Kenji Fukushima
- Division of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Takeshi Urade
- Division of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Shinichi So
- Division of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Hiroaki Yanagimoto
- Division of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Hirochika Toyama
- Division of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Hironobu Minami
- Department of Medical Oncology/Hematology, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
| | - Takumi Fukumoto
- Division of Hepato-Biliary-Pancreatic Surgery, Department of Surgery, Kobe University Graduate School of Medicine, Kobe, Hyogo, Japan
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Marmorino F, Faggioni L, Rossini D, Gabelloni M, Goddi A, Ferrer L, Conca V, Vargas J, Biagiarelli F, Daniel F, Carullo M, Vetere G, Granetto C, Boccaccio C, Cioni D, Antonuzzo L, Bergamo F, Pietrantonio F, Cremolini C, Neri E. The prognostic value of radiomic features in liver-limited metastatic colorectal cancer patients from the TRIBE2 study. Future Oncol 2023; 19:1601-1611. [PMID: 37577810 DOI: 10.2217/fon-2023-0406] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/15/2023] Open
Abstract
Aims: Evaluating the prognostic role of radiomic features in liver-limited metastatic colorectal cancer treated with first-line therapy at baseline and best response among patients undergoing resection. Patients & methods: Among patients enrolled in TRIBE2 (NCT02339116), the association of clinical and radiomic data, extracted by SOPHiA-DDM™ with progression-free and overall survival (OS) in the overall population and with disease-free survival/postresection OS in those undergoing resection was investigated. Results: Among 98 patients, radiomic parameters improved the prediction accuracy of our model for OS (area under the curve: 0.83; sensitivity: 0.85; specificity: 0.73; accuracy: 0.78), but not progression-free survival. Of 46 resected patients, small-distance high gray-level emphasis was associated with shorter disease-free survival and high gray-level zone emphasis/higher kurtosis with shorter postresection OS. Conclusion: Radiomic features should be implemented as tools of outcome prediction for liver-limited metastatic colorectal cancer.
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Affiliation(s)
- Federica Marmorino
- Unit of Oncology, University Hospital of Pisa, Pisa, Italy & Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Lorenzo Faggioni
- Academic Radiology, Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Daniele Rossini
- Unit of Oncology, University Hospital of Pisa, Pisa, Italy & Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Michela Gabelloni
- Academic Radiology, Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Antonio Goddi
- Academic Radiology, Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Loïc Ferrer
- SOPHiA GENETICS, Multimodal Research team, Cité de la Photonique, 11 avenue de Canteranne, 33600, PESSAC, France
| | - Veronica Conca
- Unit of Oncology, University Hospital of Pisa, Pisa, Italy & Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Jennifer Vargas
- SOPHiA GENETICS, Multimodal Research team, Cité de la Photonique, 11 avenue de Canteranne, 33600, PESSAC, France
| | | | - Francesca Daniel
- Oncology Unit 1, Veneto Institute of Oncology IOV - IRCCS, 35128, Padova, Italy
| | - Martina Carullo
- Unit of Oncology, University Hospital of Pisa, Pisa, Italy & Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Guglielmo Vetere
- Unit of Oncology, University Hospital of Pisa, Pisa, Italy & Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Cristina Granetto
- SC Oncologia AO S. Croce & Carle, University Teaching Hospital, Via A. Carle 25, 12100, Cuneo, Italy
| | - Chiara Boccaccio
- Unit of Oncology, University Hospital of Pisa, Pisa, Italy & Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Dania Cioni
- Academic Radiology, Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Lorenzo Antonuzzo
- Clinical Oncology Unit, Careggi University Hospital, Department of Experimental & Clinical Medicine, University of Florence, Viale Pieraccini 6, 50139, Firenze, Italy
| | - Francesca Bergamo
- Oncology Unit 1, Veneto Institute of Oncology IOV - IRCCS, 35128, Padova, Italy
| | - Filippo Pietrantonio
- Medical Oncology Department, Fondazione IRCCS, Istituto Nazionale dei Tumori, Via Giacomo Venezian 1, 20133, Milano, Italy
| | - Chiara Cremolini
- Unit of Oncology, University Hospital of Pisa, Pisa, Italy & Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
| | - Emanuele Neri
- Academic Radiology, Department of Translational Research & New Technologies in Medicine & Surgery, University of Pisa, Via Roma 67, 56126, Pisa, Italy
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7
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Li S, Yuan L, Yue M, Xu Y, Liu S, Wang F, Liu X, Wang F, Deng J, Sun Q, Liu X, Xue C, Lu T, Zhang W, Zhou J. Early evaluation of liver metastasis using spectral CT to predict outcome in patients with colorectal cancer treated with FOLFOXIRI and bevacizumab. Cancer Imaging 2023; 23:30. [PMID: 36964617 PMCID: PMC10039512 DOI: 10.1186/s40644-023-00547-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 03/15/2023] [Indexed: 03/26/2023] Open
Abstract
PURPOSE Early evaluation of the efficacy of first-line chemotherapy combined with bevacizumab in patients with colorectal cancer liver metastasis (CRLM) remains challenging. This study used 2-month post-chemotherapy spectral computed tomography (CT) to predict the overall survival (OS) and response of CRLM patients with bevacizumab-containing therapy. METHOD This retrospective analysis was performed in 104 patients with pathologically confirmed CRLM between April 2017 and October 2021. Patients were treated with 5-fluorouracil, leucovorin, oxaliplatin or irinotecan with bevacizumab. Portal venous phase spectral CT was performed on the target liver lesion within 2 months of commencing chemotherapy to demonstrate the iodine concentration (IoD) of the target liver lesion. The patients were classified as responders (R +) or non-responders (R -) according to the Response Evaluation Criteria in Solid Tumors (RECIST) v1.1 at 6 months. Multivariate analysis was performed to determine the relationships of the spectral CT parameters, tumor markers, morphology of target lesions with OS and response. The differences in portal venous phase spectral CT parameters between the R + and R - groups were analyzed. Receiver operating characteristic (ROC) curves were used to evaluate the predictive power of spectral CT parameters. RESULTS Of the 104 patients (mean age ± standard deviation: 57.73 years ± 12.56; 60 men) evaluated, 28 (26.9%) were classified as R + . Cox multivariate analysis identified the iodine concentration (hazard ratio [HR]: 1.238; 95% confidence interval [95% CI]: 1.089-1.408; P < 0.001), baseline tumor longest diameter (BLD) (HR: 1.022; 95% CI: 1.005-1.038, P = 0.010), higher baseline CEA (HR: 1.670; 95% CI: 1.016-2.745, P = 0.043), K-RAS mutation (HR: 2.027; 95% CI: 1.192-3.449; P = 0.009), and metachronous liver metastasis (HR: 1.877; 95% CI: 1.179-2.988; P = 0.008) as independent risk factors for patient OS. Logistic multivariate analysis identified the IoD (Odds Ratio [OR]: 2.243; 95% CI: 1.405-4.098; P = 0.002) and clinical N stage of the primary tumor (OR: 4.998; 95% CI: 1.210-25.345; P = 0.035) as independent predictor of R + . Using IoD cutoff values of 4.75 (100ug/cm3) the area under the ROC curve was 0.916, sensitivity and specificity were 80.3% and 96.4%, respectively. CONCLUSIONS Spectral CT IoD can predict the OS and response of patients with CRLM after 2 months of treatment with bevacizumab-containing therapy.
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Affiliation(s)
- Shenglin Li
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Long Yuan
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Mengying Yue
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, China
| | - Yuan Xu
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Suwei Liu
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Feng Wang
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, China
| | - Xiaoqin Liu
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, China
| | - Fengyan Wang
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, China
| | - Juan Deng
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Qiu Sun
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Xianwang Liu
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Caiqiang Xue
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Ting Lu
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Wenjuan Zhang
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, China
- Second Clinical School, Lanzhou University, Lanzhou, China
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China
| | - Junlin Zhou
- Department of Radiology, Lanzhou University Second Hospital, Chengguan District, Cuiyingmen No.82, Lanzhou, 730030, China.
- Second Clinical School, Lanzhou University, Lanzhou, China.
- Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, China.
- Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, China.
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8
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Su X, Zhang H, Wang Y. A predictive model for early therapeutic efficacy of colorectal liver metastases using multimodal MRI data. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2023; 31:357-372. [PMID: 36591694 DOI: 10.3233/xst-221317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
BACKGROUND Liver metastases is a pivotal factor of death in patients with colorectal cancer. The longitudinal data of colorectal liver metastases (CRLM) during treatment can monitor and reflect treatment efficacy and outcomes. OBJECTIVE The objective of this study is to establish a radiomic model based on longitudinal magnetic resonance imaging (MRI) to predict chemotherapy response in patients with CRLM. METHODS This study retrospectively enrolled longitudinal MRI data of five modalities on 100 patients. According to Response Evaluation Criteria in Solid Tumors (RECIST 1.1), 42 and 58 patients were identified as responders and non-responders, respectively. First, radiomic features were computed from different modalities of image data acquired pre-treatment and early-treatment, as well as their differences (Δ). Next, the features were screened by a two-sample t-test, max-relevance and min-redundancy (mRMR), and least absolute shrinkage and selection operator (LASSO). Then, several ensemble radiomic models that integrate support vector machine (SVM), k-nearest neighbor (KNN), gradient boost decision tree (GBDT) and multi-layer perceptron (MLP) were established based on voting method to predict chemotherapy response. Data samples were divided into training and verification queues using a ratio of 8:2. Finally, we used the area under ROC curve (AUC) to evaluate model performance. RESULTS Using the ensemble model developed using featue differences (Δ) computed from the longitudinal apparent diffusion coefficient (ADC) images, AUC is 0.9007±0.0436 for the training cohort. Applying to the testing cohort, AUC is 0.8958 and overall accuracy is 0.9. CONCLUSIONS Study results demonstrate advantages and high performance of the ensemble radiomic model based on the radiomics feature difference of the longitudinal ADC images in predicting chemotherapy response, which has potential to assist treatment decision-making and improve clinical outcome.
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Affiliation(s)
- Xuan Su
- Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Huan Zhang
- Department of Radiology, Fudan University Shanghai Cancer Center, Shanghai, China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China
| | - Yuanjun Wang
- Institute of Medical Imaging Engineering, University of Shanghai for Science and Technology, Shanghai, China
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9
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A Review of Radiomics in Predicting Therapeutic Response in Colorectal Liver Metastases: From Traditional to Artificial Intelligence Techniques. Healthcare (Basel) 2022; 10:healthcare10102075. [DOI: 10.3390/healthcare10102075] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/12/2022] [Accepted: 10/13/2022] [Indexed: 11/17/2022] Open
Abstract
An early evaluation of colorectal cancer liver metastasis (CRCLM) is crucial in determining treatment options that ultimately affect patient survival rates and outcomes. Radiomics (quantitative imaging features) have recently gained popularity in diagnostic and therapeutic strategies. Despite this, radiomics faces many challenges and limitations. This study sheds light on these limitations by reviewing the studies that used radiomics to predict therapeutic response in CRCLM. Despite radiomics’ potential to enhance clinical decision-making, it lacks standardization. According to the results of this study, the instability of radiomics quantification is caused by changes in CT scan parameters used to obtain CT scans, lesion segmentation methods used for contouring liver metastases, feature extraction methods, and dataset size used for experimentation and validation. Accordingly, the study recommends combining radiomics with deep learning to improve prediction accuracy.
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10
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Predicting survival for hepatic arterial infusion chemotherapy of unresectable colorectal liver metastases: Radiomics analysis of pretreatment computed tomography. J Transl Int Med 2022; 10:56-64. [PMID: 35702189 PMCID: PMC8997799 DOI: 10.2478/jtim-2022-0004] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022] Open
Abstract
Objective Hepatic arterial infusion chemotherapy (HAIC) is an effective treatment for advanced unresectable colorectal cancer liver metastases (CRLM). This study was conducted to predict the efficacy of HAIC in patients with unresectable CRLM by radiomics methods based on pretreatment computed tomography (CT) examinations and clinical data. Materials and Methods A total of 63 patients were included in this study (41 in the training group and 22 in the validation group). All these patients underwent CT examination before HAIC. During the follow-up period, CT scans and laboratory examinations were performed regularly. Eighty-five radiological features were extracted from the regions of interest (ROIs) of CT images using the PyRadiomics program. The t-test and correlation were applied to select features. These features were analyzed using LASSO-Cox regression, and a linear model was developed to predict overall survival (OS). Results After reducing features by t-test and correlation test, seven features remained. After LASSO-Cox cross-validation, four features remained at λ = 0.232. They were gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), neighborhood gray tone difference matrix (NGTDM), and the location of the primary tumor. The C-index was 0.758 in the training group and 0.743 in the test group. Nomograms predicting 1-, 2-, and 3-year survival were established. Conclusion Our study demonstrates that a radiomics approach based on pretreatment CT texture analysis has the ability to predict early the outcome of HAIC in patients with advanced unresectable colorectal cancer with a high degree of accuracy and feasibility.
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11
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Fiz F, Masci C, Costa G, Sollini M, Chiti A, Ieva F, Torzilli G, Viganò L. PET/CT-based radiomics of mass-forming intrahepatic cholangiocarcinoma improves prediction of pathology data and survival. Eur J Nucl Med Mol Imaging 2022; 49:3387-3400. [DOI: 10.1007/s00259-022-05765-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2022] [Accepted: 03/11/2022] [Indexed: 12/18/2022]
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12
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Viganò L, Jayakody Arachchige VS, Fiz F. Is precision medicine for colorectal liver metastases still a utopia? New perspectives by modern biomarkers, radiomics, and artificial intelligence. World J Gastroenterol 2022; 28:608-623. [PMID: 35317421 PMCID: PMC8900542 DOI: 10.3748/wjg.v28.i6.608] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 07/29/2021] [Accepted: 01/20/2022] [Indexed: 02/06/2023] Open
Abstract
The management of patients with liver metastases from colorectal cancer is still debated. Several therapeutic options and treatment strategies are available for an extremely heterogeneous clinical scenario. Adequate prediction of patients’ outcomes and of the effectiveness of chemotherapy and loco-regional treatments are crucial to reach a precision medicine approach. This has been an unmet need for a long time, but recent studies have opened new perspectives. New morphological biomarkers have been identified. The dynamic evaluation of the metastases across a time interval, with or without chemotherapy, provided a reliable assessment of the tumor biology. Genetics have been explored and, thanks to their strong association with prognosis, have the potential to drive treatment planning. The liver-tumor interface has been identified as one of the main determinants of tumor progression, and its components, in particular the immune infiltrate, are the focus of major research. Image mining and analyses provided new insights on tumor biology and are expected to have a relevant impact on clinical practice. Artificial intelligence is a further step forward. The present paper depicts the evolution of clinical decision-making for patients affected by colorectal liver metastases, facing modern biomarkers and innovative opportunities that will characterize the evolution of clinical research and practice in the next few years.
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Affiliation(s)
- Luca Viganò
- Department of Hepatobiliary and General Surgery, IRCCS Humanitas Research Hospital, Rozzano 20089, MI, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele 20072, MI, Italy
| | - Visala S Jayakody Arachchige
- Department of Hepatobiliary and General Surgery, IRCCS Humanitas Research Hospital, Rozzano 20089, MI, Italy
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele 20072, MI, Italy
| | - Francesco Fiz
- Nuclear Medicine, IRCCS Humanitas Research Hospital, Rozzano 20089, MI, Italy
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13
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Rompianesi G, Pegoraro F, Ceresa CDL, Montalti R, Troisi RI. Artificial intelligence in the diagnosis and management of colorectal cancer liver metastases. World J Gastroenterol 2022; 28:108-122. [PMID: 35125822 PMCID: PMC8793013 DOI: 10.3748/wjg.v28.i1.108] [Citation(s) in RCA: 35] [Impact Index Per Article: 17.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 10/12/2021] [Accepted: 12/25/2021] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer (CRC) is the third most common malignancy worldwide, with approximately 50% of patients developing colorectal cancer liver metastasis (CRLM) during the follow-up period. Management of CRLM is best achieved via a multidisciplinary approach and the diagnostic and therapeutic decision-making process is complex. In order to optimize patients' survival and quality of life, there are several unsolved challenges which must be overcome. These primarily include a timely diagnosis and the identification of reliable prognostic factors. Furthermore, to allow optimal treatment options, a precision-medicine, personalized approach is required. The widespread digitalization of healthcare generates a vast amount of data and together with accessible high-performance computing, artificial intelligence (AI) technologies can be applied. By increasing diagnostic accuracy, reducing timings and costs, the application of AI could help mitigate the current shortcomings in CRLM management. In this review we explore the available evidence of the possible role of AI in all phases of the CRLM natural history. Radiomics analysis and convolutional neural networks (CNN) which combine computed tomography (CT) images with clinical data have been developed to predict CRLM development in CRC patients. AI models have also proven themselves to perform similarly or better than expert radiologists in detecting CRLM on CT and magnetic resonance scans or identifying them from the noninvasive analysis of patients' exhaled air. The application of AI and machine learning (ML) in diagnosing CRLM has also been extended to histopathological examination in order to rapidly and accurately identify CRLM tissue and its different histopathological growth patterns. ML and CNN have shown good accuracy in predicting response to chemotherapy, early local tumor progression after ablation treatment, and patient survival after surgical treatment or chemotherapy. Despite the initial enthusiasm and the accumulating evidence, AI technologies' role in healthcare and CRLM management is not yet fully established. Its limitations mainly concern safety and the lack of regulation and ethical considerations. AI is unlikely to fully replace any human role but could be actively integrated to facilitate physicians in their everyday practice. Moving towards a personalized and evidence-based patient approach and management, further larger, prospective and rigorous studies evaluating AI technologies in patients at risk or affected by CRLM are needed.
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Affiliation(s)
- Gianluca Rompianesi
- Division of Hepato-Bilio-Pancreatic, Minimally Invasive and Robotic Surgery, Department of Clinical Medicine and Surgery, Federico II University Hospital, Naples 80125, Italy
| | - Francesca Pegoraro
- Division of Hepato-Bilio-Pancreatic, Minimally Invasive and Robotic Surgery, Department of Clinical Medicine and Surgery, Federico II University Hospital, Naples 80125, Italy
| | - Carlo DL Ceresa
- Department of Hepato-Pancreato-Biliary Surgery, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9ES, United Kingdom
| | - Roberto Montalti
- Division of Hepato-Bilio-Pancreatic, Minimally Invasive and Robotic Surgery, Department of Public Health, Federico II University Hospital, Naples 80125, Italy
| | - Roberto Ivan Troisi
- Division of Hepato-Bilio-Pancreatic, Minimally Invasive and Robotic Surgery, Department of Clinical Medicine and Surgery, Federico II University Hospital, Naples 80125, Italy
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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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15
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Cheng S, Jin Z, Xue H. Assessment of Response to Chemotherapy in Pancreatic Cancer with Liver Metastasis: CT Texture as a Predictive Biomarker. Diagnostics (Basel) 2021; 11:diagnostics11122252. [PMID: 34943489 PMCID: PMC8700536 DOI: 10.3390/diagnostics11122252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 11/21/2021] [Accepted: 11/26/2021] [Indexed: 11/16/2022] Open
Abstract
In this paper, we assess changes in CT texture of metastatic liver lesions after treatment with chemotherapy in patients with pancreatic cancer and determine if texture parameters correlate with measured time to progression (TTP). This retrospective study included 110 patients with pancreatic cancer with liver metastasis, and mean, entropy, kurtosis, skewness, mean of positive pixels, and standard deviation (SD) values were extracted during texture analysis. Response assessment was also obtained by using RECIST 1.1, Choi and modified Choi criteria, respectively. The correlation of texture parameters and existing assessment criteria with TTP were evaluated using Kaplan-Meier and Cox regression analyses in the training cohort. Kaplan-Meier curves of the proportion of patients without disease progression were significantly different for several texture parameters, and were better than those for RECIST 1.1-, Choi-, and modified Choi-defined response (p < 0.05 vs. p = 0.398, p = 0.142, and p = 0.536, respectively). Cox regression analysis showed that percentage change in SD was an independent predictor of TTP (p = 0.016) and confirmed in the validation cohort (p = 0.019). In conclusion, CT texture parameters have the potential to become predictive imaging biomarkers for response evaluation in pancreatic cancer with liver metastasis.
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CT Texture Analysis of Pulmonary Neuroendocrine Tumors-Associations with Tumor Grading and Proliferation. J Clin Med 2021; 10:jcm10235571. [PMID: 34884272 PMCID: PMC8658090 DOI: 10.3390/jcm10235571] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 11/22/2021] [Accepted: 11/23/2021] [Indexed: 11/16/2022] Open
Abstract
Texture analysis derived from computed tomography (CT) might be able to provide clinically relevant imaging biomarkers and might be associated with histopathological features in tumors. The present study sought to elucidate the possible associations between texture features derived from CT images with proliferation index Ki-67 and grading in pulmonary neuroendocrine tumors. Overall, 38 patients (n = 22 females, 58%) with a mean age of 60.8 ± 15.2 years were included into this retrospective study. The texture analysis was performed using the free available Mazda software. All tumors were histopathologically confirmed. In discrimination analysis, "S(1,1)SumEntrp" was significantly different between typical and atypical carcinoids (mean 1.74 ± 0.11 versus 1.79 ± 0.14, p = 0.007). The correlation analysis revealed a moderate positive association between Ki-67 index with the first order parameter kurtosis (r = 0.66, p = 0.001). Several other texture features were associated with the Ki-67 index, the highest correlation coefficient showed "S(4,4)InvDfMom" (r = 0.59, p = 0.004). Several texture features derived from CT were associated with the proliferation index Ki-67 and might therefore be a valuable novel biomarker in pulmonary neuroendocrine tumors. "Sumentrp" might be a promising parameter to aid in the discrimination between typical and atypical carcinoids.
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Defeudis A, Cefaloni L, Giannetto G, Cappello G, Rizzetto F, Panic J, Barra D, Nicoletti G, Mazzetti S, Vanzulli A, Regge D, Giannini V. Comparison of radiomics approaches to predict resistance to 1st line chemotherapy in liver metastatic colorectal cancer. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:3305-3308. [PMID: 34891947 DOI: 10.1109/embc46164.2021.9630316] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Colorectal cancer (CRC) has the second-highest tumor incidence and is a leading cause of death by cancer. Nearly 20% of patients with CRC will have metastases (mts) at the time of diagnosis, and more than 50% of patients with CRC develop metastases during their disease. Unfortunately, only 45% of patients after a chemotherapy will respond to treatment. The aim of this study is to develop and validate a machine learning algorithm to predict response of individual liver mts, using CT scans. Understanding which mts will respond or not will help clinicians in providing a more efficient per-lesion treatment based on patient specific response and not only following a standard treatment. A group of 92 patients was enrolled from two Italian institutions. CT scans were collected, and the portal venous phase was manually segmented by an expert radiologist. Then, 75 radiomics features were extracted both from 7x7 ROIs that moved across the image and from the whole 3D mts. Feature selection was performed using a genetic algorithm. Results are presented as a comparison of the two different approaches of features extraction and different classification algorithms. Accuracy (ACC), sensitivity (SE), specificity (SP), negative and positive predictive values (NPV and PPV) were evaluated for all lesions (per-lesion analysis) and patients (per-patient analysis) in the construction and validation sets. Best results were obtained in the per-lesion analysis from the 3D approach using a Support Vector Machine as classifier. We reached on the training set an ACC of 81%, while on test set, we obtained SE of 76%, SP of 67%, PPV of 69% and NPV of 75%. On the validation set a SE of 61%, SP of 60%, PPV of 57% and NPV of 64% were reached. The promising results obtained in the validation dataset should be extended to a larger cohort of patient to further validate our method.Clinical Relevance- to develop a radiomics signatures predicting single liver mts response to therapy. A personalized mts approach is important to avoid unnecessary toxicity offering more suitable treatments and a better quality of life to oncological 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: 22] [Impact Index Per Article: 7.3] [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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Nakanishi R, Oki E, Hasuda H, Sano E, Miyashita Y, Sakai A, Koga N, Kuriyama N, Nonaka K, Fujimoto Y, Jogo T, Hokonohara K, Hu Q, Hisamatsu Y, Ando K, Kimura Y, Yoshizumi T, Mori M. ASO Author Reflection: Radiomics-Based Prediction for the Responder to First-Line Oxaliplatin-Based Chemotherapy in Patients with Colorectal Liver Metastasis. Ann Surg Oncol 2021; 28:2986-2987. [PMID: 33725205 DOI: 10.1245/s10434-020-09584-2] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 12/18/2020] [Indexed: 11/18/2022]
Affiliation(s)
- Ryota Nakanishi
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan.
| | - Eiji Oki
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Hirofumi Hasuda
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Eiki Sano
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Yu Miyashita
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Akihiro Sakai
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Naomichi Koga
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Naotaka Kuriyama
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Kentaro Nonaka
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Yoshiaki Fujimoto
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Tomoko Jogo
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Kentaro Hokonohara
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Qingjiang Hu
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Yuichi Hisamatsu
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Koji Ando
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Yasue Kimura
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Tomoharu Yoshizumi
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan
| | - Masaki Mori
- Department of Surgery and Science, Graduate School of Medical Sciences, Kyushu University, Maidashi 3-1-1, Higashi-ku, Fukuoka, 812-8582, Japan
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Hewitt DB, Pawlik TM, Cloyd JM. Who Will Benefit? Using Radiomics to Predict Response to Oxaliplatin-Based Chemotherapy in Patients with Colorectal Liver Metastases. Ann Surg Oncol 2021; 28:2931-2933. [PMID: 33475881 DOI: 10.1245/s10434-020-09586-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/29/2020] [Accepted: 12/30/2020] [Indexed: 11/18/2022]
Affiliation(s)
- Daniel Brock Hewitt
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Timothy M Pawlik
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, Columbus, OH, USA
| | - Jordan M Cloyd
- Department of Surgery, Division of Surgical Oncology, The Ohio State University Wexner Medical Center, Columbus, OH, USA.
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