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Cheng H, Xu H, Peng B, Huang X, Hu Y, Zheng C, Zhang Z. Illuminating the future of precision cancer surgery with fluorescence imaging and artificial intelligence convergence. NPJ Precis Oncol 2024; 8:196. [PMID: 39251820 PMCID: PMC11385925 DOI: 10.1038/s41698-024-00699-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Accepted: 08/29/2024] [Indexed: 09/11/2024] Open
Abstract
Real-time and accurate guidance for tumor resection has long been anticipated by surgeons. In the past decade, the flourishing material science has made impressive progress in near-infrared fluorophores that may fulfill this purpose. Fluorescence imaging-guided surgery shows great promise for clinical application and has undergone widespread evaluations, though it still requires continuous improvements to transition this technique from bench to bedside. Concurrently, the rapid progress of artificial intelligence (AI) has revolutionized medicine, aiding in the screening, diagnosis, and treatment of human doctors. Incorporating AI helps enhance fluorescence imaging and is poised to bring major innovations to surgical guidance, thereby realizing precision cancer surgery. This review provides an overview of the principles and clinical evaluations of fluorescence-guided surgery. Furthermore, recent endeavors to synergize AI with fluorescence imaging were presented, and the benefits of this interdisciplinary convergence were discussed. Finally, several implementation strategies to overcome technical hurdles were proposed to encourage and inspire future research to expedite the clinical application of these revolutionary technologies.
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Affiliation(s)
- Han Cheng
- Department of Oral and Maxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, P. R. China
- College of Stomatology, Shanghai Jiao Tong University & National Center for Stomatology, Shanghai, 200011, P. R. China
- National Clinical Research Center for Oral Diseases & Shanghai Key Laboratory of Stomatology, Shanghai, 200011, P. R. China
- Shanghai Research Institute of Stomatology, Shanghai, 200011, P. R. China
- Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, 200011, P. R. China
| | - Hongtao Xu
- Department of Oral and Maxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, P. R. China
- College of Stomatology, Shanghai Jiao Tong University & National Center for Stomatology, Shanghai, 200011, P. R. China
- National Clinical Research Center for Oral Diseases & Shanghai Key Laboratory of Stomatology, Shanghai, 200011, P. R. China
- Shanghai Research Institute of Stomatology, Shanghai, 200011, P. R. China
- Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, 200011, P. R. China
| | - Boyang Peng
- School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
| | - Xiaojuan Huang
- Department of Oral and Maxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, P. R. China
- College of Stomatology, Shanghai Jiao Tong University & National Center for Stomatology, Shanghai, 200011, P. R. China
- National Clinical Research Center for Oral Diseases & Shanghai Key Laboratory of Stomatology, Shanghai, 200011, P. R. China
- Shanghai Research Institute of Stomatology, Shanghai, 200011, P. R. China
- Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, 200011, P. R. China
| | - Yongjie Hu
- Department of Oral and Maxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, P. R. China
- College of Stomatology, Shanghai Jiao Tong University & National Center for Stomatology, Shanghai, 200011, P. R. China
- National Clinical Research Center for Oral Diseases & Shanghai Key Laboratory of Stomatology, Shanghai, 200011, P. R. China
- Shanghai Research Institute of Stomatology, Shanghai, 200011, P. R. China
- Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, 200011, P. R. China
| | - Chongyang Zheng
- Department of Oral and Maxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, P. R. China.
- College of Stomatology, Shanghai Jiao Tong University & National Center for Stomatology, Shanghai, 200011, P. R. China.
- National Clinical Research Center for Oral Diseases & Shanghai Key Laboratory of Stomatology, Shanghai, 200011, P. R. China.
- Shanghai Research Institute of Stomatology, Shanghai, 200011, P. R. China.
- Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, 200011, P. R. China.
| | - Zhiyuan Zhang
- Department of Oral and Maxillofacial-Head & Neck Oncology, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, P. R. China.
- College of Stomatology, Shanghai Jiao Tong University & National Center for Stomatology, Shanghai, 200011, P. R. China.
- National Clinical Research Center for Oral Diseases & Shanghai Key Laboratory of Stomatology, Shanghai, 200011, P. R. China.
- Shanghai Research Institute of Stomatology, Shanghai, 200011, P. R. China.
- Research Unit of Oral and Maxillofacial Regenerative Medicine, Chinese Academy of Medical Sciences, Shanghai, 200011, P. R. China.
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Huang Y, Wang X, Cao Y, Li M, Li L, Chen H, Tang S, Lan X, Jiang F, Zhang J. Multiparametric MRI model to predict molecular subtypes of breast cancer using Shapley additive explanations interpretability analysis. Diagn Interv Imaging 2024; 105:191-205. [PMID: 38272773 DOI: 10.1016/j.diii.2024.01.004] [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: 11/10/2023] [Revised: 01/13/2024] [Accepted: 01/16/2024] [Indexed: 01/27/2024]
Abstract
PURPOSE The purpose of this study was to assess the predictive performance of multiparametric magnetic resonance imaging (MRI) for molecular subtypes and interpret features using SHapley Additive exPlanations (SHAP) analysis. MATERIAL AND METHODS Patients with breast cancer who underwent pre-treatment MRI (including ultrafast dynamic contrast-enhanced MRI, magnetic resonance spectroscopy, diffusion kurtosis imaging and intravoxel incoherent motion) were recruited between February 2019 and January 2022. Thirteen semantic and thirteen multiparametric features were collected and the key features were selected to develop machine-learning models for predicting molecular subtypes of breast cancers (luminal A, luminal B, triple-negative and HER2-enriched) by using stepwise logistic regression. Semantic model and multiparametric model were built and compared based on five machine-learning classifiers. Model decision-making was interpreted using SHAP analysis. RESULTS A total of 188 women (mean age, 53 ± 11 [standard deviation] years; age range: 25-75 years) were enrolled and further divided into training cohort (131 women) and validation cohort (57 women). XGBoost demonstrated good predictive performance among five machine-learning classifiers. Within the validation cohort, the areas under the receiver operating characteristic curves (AUCs) for the semantic models ranged from 0.693 (95% confidence interval [CI]: 0.478-0.839) for HER2-enriched subtype to 0.764 (95% CI: 0.681-0.908) for luminal A subtype, inferior to multiparametric models that yielded AUCs ranging from 0.771 (95% CI: 0.630-0.888) for HER2-enriched subtype to 0.857 (95% CI: 0.717-0.957) for triple-negative subtype. The AUCs between the semantic and the multiparametric models did not show significant differences (P range: 0.217-0.640). SHAP analysis revealed that lower iAUC, higher kurtosis, lower D*, and lower kurtosis were distinctive features for luminal A, luminal B, triple-negative breast cancer, and HER2-enriched subtypes, respectively. CONCLUSION Multiparametric MRI is superior to semantic models to effectively predict the molecular subtypes of breast cancer.
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Affiliation(s)
- Yao Huang
- School of Medicine, Chongqing University, Chongqing, 400030, China; Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Xiaoxia Wang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Ying Cao
- School of Medicine, Chongqing University, Chongqing, 400030, China; Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Mengfei Li
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Lan Li
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Huifang Chen
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Sun Tang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Xiaosong Lan
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Fujie Jiang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China
| | - Jiuquan Zhang
- Department of Radiology, Chongqing University Cancer Hospital, Chongqing Key Laboratory for Intelligent Oncology in Breast Cancer (iCQBC), 400030, Chongqing, China.
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Kokkinakis S, Ziogas IA, Llaque Salazar JD, Moris DP, Tsoulfas G. Clinical Prediction Models for Prognosis of Colorectal Liver Metastases: A Comprehensive Review of Regression-Based and Machine Learning Models. Cancers (Basel) 2024; 16:1645. [PMID: 38730597 PMCID: PMC11083016 DOI: 10.3390/cancers16091645] [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: 04/07/2024] [Revised: 04/22/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024] Open
Abstract
Colorectal liver metastasis (CRLM) is a disease entity that warrants special attention due to its high frequency and potential curability. Identification of "high-risk" patients is increasingly popular for risk stratification and personalization of the management pathway. Traditional regression-based methods have been used to derive prediction models for these patients, and lately, focus has shifted to artificial intelligence-based models, with employment of variable supervised and unsupervised techniques. Multiple endpoints, like overall survival (OS), disease-free survival (DFS) and development or recurrence of postoperative complications have all been used as outcomes in these studies. This review provides an extensive overview of available clinical prediction models focusing on the prognosis of CRLM and highlights the different predictor types incorporated in each model. An overview of the modelling strategies and the outcomes chosen is provided. Specific patient and treatment characteristics included in the models are discussed in detail. Model development and validation methods are presented and critically appraised, and model performance is assessed within a proposed framework.
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Affiliation(s)
- Stamatios Kokkinakis
- Department of General Surgery, School of Medicine, University Hospital of Heraklion, University of Crete, 71500 Heraklion, Greece;
| | - Ioannis A. Ziogas
- Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (I.A.Z.); (J.D.L.S.)
| | - Jose D. Llaque Salazar
- Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA; (I.A.Z.); (J.D.L.S.)
| | - Dimitrios P. Moris
- Department of Surgery, Duke University Medical Center, Durham, NC 27710, USA;
| | - Georgios Tsoulfas
- Department of Transplantation Surgery, Centre for Research and Innovation in Solid Organ Transplantation, Aristotle University School of Medicine, 54124 Thessaloniki, Greece
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Wang J, Botvinov J, Bhatt AJ, Beyer K, Kreis ME, Adam M, Alseidi A, Margonis GA. Somatic Mutations in Surgically Treated Colorectal Liver Metastases: An Overview. Cells 2024; 13:679. [PMID: 38667294 PMCID: PMC11049420 DOI: 10.3390/cells13080679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 03/31/2024] [Accepted: 04/08/2024] [Indexed: 04/28/2024] Open
Abstract
Colorectal cancer is the second most common cause of cancer death in the United States, and up to half of patients develop colorectal liver metastases (CRLMs). Notably, somatic genetic mutations, such as mutations in RAS, BRAF, mismatch repair (MMR) genes, TP53, and SMAD4, have been shown to play a prognostic role in patients with CRLM. This review summarizes and appraises the current literature regarding the most relevant somatic mutations in surgically treated CRLM by not only reviewing representative studies, but also providing recommendations for areas of future research. In addition, advancements in genetic testing and an increasing emphasis on precision medicine have led to a more nuanced understanding of these mutations; thus, more granular data for each mutation are reviewed when available. Importantly, such knowledge can pave the way for precision medicine with the ultimate goal of improving patient outcomes.
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Affiliation(s)
- Jane Wang
- Department of Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (M.A.); (A.A.)
| | - Julia Botvinov
- Hackensack Meridian School of Medicine, Nutley, NJ 07110, USA;
| | - Aarshvi Jahnvi Bhatt
- University of Toledo College of Medicine and Life Sciences, Toledo, OH 43614, USA;
| | - Katharina Beyer
- Department of General and Visceral Surgery, Charité Campus Benjamin Franklin, 12203 Berlin, Germany; (K.B.); (M.E.K.)
| | - Martin E. Kreis
- Department of General and Visceral Surgery, Charité Campus Benjamin Franklin, 12203 Berlin, Germany; (K.B.); (M.E.K.)
| | - Mohamed Adam
- Department of Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (M.A.); (A.A.)
| | - Adnan Alseidi
- Department of Surgery, University of California San Francisco, San Francisco, CA 94143, USA; (M.A.); (A.A.)
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Achterberg FB, Bijlstra OD, Slooter MD, Sibinga Mulder BG, Boonstra MC, Bouwense SA, Bosscha K, Coolsen MME, Derksen WJM, Gerhards MF, Gobardhan PD, Hagendoorn J, Lips D, Marsman HA, Zonderhuis BM, Wullaert L, Putter H, Burggraaf J, Mieog JSD, Vahrmeijer AL, Swijnenburg RJ. ICG-Fluorescence Imaging for Margin Assessment During Minimally Invasive Colorectal Liver Metastasis Resection. JAMA Netw Open 2024; 7:e246548. [PMID: 38639939 PMCID: PMC11031680 DOI: 10.1001/jamanetworkopen.2024.6548] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 01/31/2024] [Indexed: 04/20/2024] Open
Abstract
Importance Unintended tumor-positive resection margins occur frequently during minimally invasive surgery for colorectal liver metastases and potentially negatively influence oncologic outcomes. Objective To assess whether indocyanine green (ICG)-fluorescence-guided surgery is associated with achieving a higher radical resection rate in minimally invasive colorectal liver metastasis surgery and to assess the accuracy of ICG fluorescence for predicting the resection margin status. Design, Setting, and Participants The MIMIC (Minimally Invasive, Indocyanine-Guided Metastasectomy in Patients With Colorectal Liver Metastases) trial was designed as a prospective single-arm multicenter cohort study in 8 Dutch liver surgery centers. Patients were scheduled to undergo minimally invasive (laparoscopic or robot-assisted) resections of colorectal liver metastases between September 1, 2018, and June 30, 2021. Exposures All patients received a single intravenous bolus of 10 mg of ICG 24 hours prior to surgery. During surgery, ICG-fluorescence imaging was used as an adjunct to ultrasonography and regular laparoscopy to guide and assess the resection margin in real time. The ICG-fluorescence imaging was performed during and after liver parenchymal transection to enable real-time assessment of the tumor margin. Absence of ICG fluorescence was favorable both during transection and in the tumor bed directly after resection. Main Outcomes and Measures The primary outcome measure was the radical (R0) resection rate, defined by the percentage of colorectal liver metastases resected with at least a 1 mm distance between the tumor and resection plane. Secondary outcomes were the accuracy of ICG fluorescence in detecting margin-positive (R1; <1 mm margin) resections and the change in surgical management. Results In total, 225 patients were enrolled, of whom 201 (116 [57.7%] male; median age, 65 [IQR, 57-72] years) with 316 histologically proven colorectal liver metastases were included in the final analysis. The overall R0 resection rate was 92.4%. Re-resection of ICG-fluorescent tissue in the resection cavity was associated with a 5.0% increase in the R0 percentage (from 87.4% to 92.4%; P < .001). The sensitivity and specificity for real-time resection margin assessment were 60% and 90%, respectively (area under the receiver operating characteristic curve, 0.751; 95% CI, 0.668-0.833), with a positive predictive value of 54% and a negative predictive value of 92%. After training and proctoring of the first procedures, participating centers that were new to the technique had a comparable false-positive rate for predicting R1 resections during the first 10 procedures (odds ratio, 1.36; 95% CI, 0.44-4.24). The ICG-fluorescence imaging was associated with changes in intraoperative surgical management in 56 (27.9%) of the patients. Conclusions and Relevance In this multicenter prospective cohort study, ICG-fluorescence imaging was associated with an increased rate of tumor margin-negative resection and changes in surgical management in more than one-quarter of the patients. The absence of ICG fluorescence during liver parenchymal transection predicted an R0 resection with 92% accuracy. These results suggest that use of ICG fluorescence may provide real-time feedback of the tumor margin and a higher rate of complete oncologic resection.
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Affiliation(s)
- Friso B. Achterberg
- Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands
- Department of Surgery, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands
- Department of Surgery, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Okker D. Bijlstra
- Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands
- Department of Surgery, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands
- Department of Surgery, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
| | - Maxime D. Slooter
- Department of Surgery, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands
| | | | - Mark C. Boonstra
- Department of Surgery, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Stefan A. Bouwense
- NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Koop Bosscha
- Department of Surgery, Jeroen Bosch Ziekenhuis, Den Bosch, the Netherlands
| | - Mariëlle M. E. Coolsen
- Department of Surgery, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Wouter J. M. Derksen
- Department of Surgery, St. Antonius Hospital, Nieuwegein/Regionaal Academisch Kankercentrum Utrecht, Utrecht, the Netherlands
| | - Michael F. Gerhards
- Department of Surgery, Onze Lieve Vrouwe Gasthuis, Amsterdam, the Netherlands
| | | | - Jeroen Hagendoorn
- Department of Surgery, University Medical Center Utrecht/Regionaal Academisch Kankercentrum Utrecht, Utrecht, the Netherlands
| | - Daan Lips
- Department of Surgery, Medisch Spectrum Twente, Enschede, the Netherlands
| | - Hendrik A. Marsman
- Department of Surgery, Onze Lieve Vrouwe Gasthuis, Amsterdam, the Netherlands
| | - Babs M. Zonderhuis
- Department of Surgery, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Lissa Wullaert
- Department of Surgery, Amphia Ziekenhuis, Breda, the Netherlands
- Department of Surgical Oncology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Hein Putter
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, the Netherlands
| | - Jacobus Burggraaf
- Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands
- Centre for Human Drug Research, Leiden, the Netherlands
| | - J. Sven D. Mieog
- Department of Surgery, Leiden University Medical Center, Leiden, the Netherlands
| | | | - Rutger-Jan Swijnenburg
- Department of Surgery, Amsterdam University Medical Center, University of Amsterdam, Amsterdam, the Netherlands
- Department of Surgery, Amsterdam University Medical Center, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Cancer Center Amsterdam, Amsterdam, the Netherlands
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Endo Y, Alaimo L, Moazzam Z, Woldesenbet S, Lima HA, Yang J, Munir MM, Shaikh CF, Azap L, Katayama E, Rueda BO, Guglielmi A, Ruzzenente A, Aldrighetti L, Alexandrescu S, Kitago M, Poultsides G, Sasaki K, Aucejo F, Pawlik TM. Optimal policy tree to assist in adjuvant therapy decision-making after resection of colorectal liver metastases. Surgery 2024; 175:645-653. [PMID: 37778970 DOI: 10.1016/j.surg.2023.06.045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/03/2023] [Accepted: 06/18/2023] [Indexed: 10/03/2023]
Abstract
BACKGROUND Although systemic postoperative therapy after surgery for colorectal liver metastases is generally recommended, the benefit of adjuvant chemotherapy has been debated. We used machine learning to develop a decision tree and define which patients may benefit from adjuvant chemotherapy after hepatectomy for colorectal liver metastases. METHODS Patients who underwent curative-intent resection for colorectal liver metastases between 2000 and 2020 were identified from an international multi-institutional database. An optimal policy tree analysis was used to determine the optimal assignment of the adjuvant chemotherapy to subgroups of patients for overall survival and recurrence-free survival. RESULTS Among 1,358 patients who underwent curative-intent resection of colorectal liver metastases, 1,032 (76.0%) received adjuvant chemotherapy. After a median follow-up of 28.7 months (interquartile range 13.7-52.0), 5-year overall survival was 67.5%, and 3-year recurrence-free survival was 52.6%, respectively. Adjuvant chemotherapy was associated with better recurrence-free survival (3-year recurrence-free survival: adjuvant chemotherapy, 54.4% vs no adjuvant chemotherapy, 46.8%; P < .001) but no overall survival significant improvement (5-year overall survival: adjuvant chemotherapy, 68.1% vs no adjuvant chemotherapy, 65.7%; P = .15). Patients were randomly allocated into 2 cohorts (training data set, n = 679, testing data set, n = 679). The random forest model demonstrated good performance in predicting counterfactual probabilities of death and recurrence relative to receipt of adjuvant chemotherapy. According to the optimal policy tree, patient demographics, secondary tumor characteristics, and primary tumor characteristics defined the subpopulation that would benefit from adjuvant chemotherapy. CONCLUSION A novel artificial intelligence methodology based on patient, primary tumor, and treatment characteristics may help clinicians tailor adjuvant chemotherapy recommendations after colorectal liver metastases resection.
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Affiliation(s)
- Yutaka Endo
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
| | - Laura Alaimo
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH; Department of Surgery, University of Verona, Italy
| | - Zorays Moazzam
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
| | - Selamawit Woldesenbet
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
| | - Henrique A Lima
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
| | - Jason Yang
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
| | - Muhammad Musaab Munir
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
| | - Chanza F Shaikh
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
| | - Lovette Azap
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
| | - Erryk Katayama
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
| | - Belisario Ortiz Rueda
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH
| | | | | | | | | | - Minoru Kitago
- Department of Surgery, Keio University, Tokyo, Japan
| | | | | | - Federico Aucejo
- Department of General Surgery, Cleveland Clinic Foundation, OH
| | - Timothy M Pawlik
- Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH.
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Zhang W, Liu Y, Wu Q, Wei X, Liu B, Jiao Q, Zhang R, Hu B, Li Y, Ying T. Pitfalls and strategies of Sonazoid enhanced ultrasonography in differentiating metastatic and benign hepatic lesions. Clin Hemorheol Microcirc 2024; 86:467-479. [PMID: 38043009 DOI: 10.3233/ch-231995] [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] [Indexed: 12/04/2023]
Abstract
OBJECTIVE This article aims to clarify pitfalls and find strategies for the detecting and diagnosing hyperechoic liver metastases (LMs) using Sonazoid-contrast enhanced ultrasonography (Sonazoid-CEUS). METHODS This study was a prospective self-controlled study. Patients with hepatic lesions suspected as LMs or benign lesions were included in the study. Baseline ultrasonography (BUS) and Sonazoid-CEUS were performed on every patient. Characteristics of LMs and benign nodules were compared by chi-square test and fisher test. Factors influenced the CEUS were demonstrated by univariate analysis and multivariate logistic regression analysis. RESULTS 54 patients were included in this study. CEUS found additional 75 LMs from 19 patients in Kupffer phase. We found hyperechoic focal liver lesions and deep seated in liver are main confounding factors in CEUS diagnosis. Sensitivity would be improved from 16.67% to 78.57%, negative predictive value (NPV) would be improved from 28.57% to 76.92% and accuracy would be improved from 37.5% to 87.50% when using rapid "wash-in" and "wash-out" as main diagnostic criteria. CONCLUSIONS Hyperechoic LMs especially deeply seated ones are usually not shown typical "black hole" sign in Kupffer phase. Quickly "wash-in and wash out" shows high accuracy in diagnosing malignant nodules. We highly recommend CEUS as a routing exam to detect and diagnose LMs.
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Affiliation(s)
- Wei Zhang
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yilun Liu
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiong Wu
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xiaoer Wei
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Beibei Liu
- Institute of Diagnostic and Interventional Radiology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Qiong Jiao
- Department of Pathology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Rui Zhang
- Department of Obstetrics and Gynecology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Bing Hu
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Ultrasound in Medicine, Shanghai, China
| | - Yi Li
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Tao Ying
- Department of Ultrasound in Medicine, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Shanghai Institute of Ultrasound in Medicine, Shanghai, China
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Zhang R, Hong M, Cai H, Liang Y, Chen X, Liu Z, Wu M, Zhou C, Bao C, Wang H, Yang S, Hu Q. Predicting the pathological invasiveness in patients with a solitary pulmonary nodule via Shapley additive explanations interpretation of a tree-based machine learning radiomics model: a multicenter study. Quant Imaging Med Surg 2023; 13:7828-7841. [PMID: 38106261 PMCID: PMC10722047 DOI: 10.21037/qims-23-615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/08/2023] [Indexed: 12/19/2023]
Abstract
Background Radiomics models could help assess the benign and malignant invasiveness and prognosis of pulmonary nodules. However, the lack of interpretability limits application of these models. We thus aimed to construct and validate an interpretable and generalized computed tomography (CT) radiomics model to evaluate the pathological invasiveness in patients with a solitary pulmonary nodule in order to improve the management of these patients. Methods We retrospectively enrolled 248 patients with CT-diagnosed solitary pulmonary nodules. Radiomic features were extracted from nodular region and perinodular regions of 3 and 5 mm. After coarse-to-fine feature selection, the radiomics score (radscore) was calculated using the least absolute shrinkage and selection operator logistic method. Univariate and multivariate logistic regression analyses were performed to determine the invasiveness-related clinicoradiological factors. The clinical-radiomics model was then constructed using the logistic and extreme gradient boosting (XGBoost) algorithms. The Shapley additive explanations (SHAP) method was then used to explain the contributions of the features. After removing batch effects with the ComBat algorithm, we assessed the generalization of the explainable clinical-radiomics model in two independent external validation cohorts (n=147 and n=149). Results The clinical-radiomic XGBoost model integrating the radscore, CT value, nodule length, and crescent sign demonstrated better predictive performance than did the clinical-radiomics logistic model in assessing pulmonary nodule invasiveness, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.889 [95% confidence interval (CI), 0.848-0.927] in the training cohort. The SHAP algorithm illustrates the contribution of each feature in the final model. The specific model decision process was visualized using a tree-based decision heatmap. Satisfactory generalization performance was shown with AUCs of 0.889 (95% CI, 0.823-0.942) and 0.915 (95% CI, 0.851-0.963) in the two external validation cohorts. Conclusions An interpretable and generalized clinical-radiomics model for predicting pulmonary nodule invasibility was constructed to help clinicians determine the invasiveness of pulmonary nodules and devise assessment strategies in an easily understandable manner.
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Affiliation(s)
- Rong Zhang
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, China
| | - Minping Hong
- Department of Radiology, Jiaxing TCM Hospital Affiliated to Zhejiang Chinese Medical University, Jiaxing, China
| | - Hongjie Cai
- The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China
| | - Yanting Liang
- Department of Radiology, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, China
| | - Xinjie Chen
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, China
| | - Ziwei Liu
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, China
| | - Meilian Wu
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, China
| | - Cuiru Zhou
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, China
| | - Chenzhengren Bao
- Department of Radiology, The Affiliated Chencun Hospital of Shunde Hospital, Southern Medical University (The Affiliated Chencun Hospital of The First People’s Hospital of Shunde), Foshan, China
| | - Huafeng Wang
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, China
| | - Shaomin Yang
- Department of Radiology, Lecong Hospital of Shunde, Foshan, China
| | - Qiugen Hu
- Department of Radiology, Shunde Hospital, Southern Medical University (The First People’s Hospital of Shunde), Foshan, China
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Zhang C, Xu J, Tang R, Yang J, Wang W, Yu X, Shi S. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. J Hematol Oncol 2023; 16:114. [PMID: 38012673 PMCID: PMC10680201 DOI: 10.1186/s13045-023-01514-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2023] [Accepted: 11/20/2023] [Indexed: 11/29/2023] Open
Abstract
Research into the potential benefits of artificial intelligence for comprehending the intricate biology of cancer has grown as a result of the widespread use of deep learning and machine learning in the healthcare sector and the availability of highly specialized cancer datasets. Here, we review new artificial intelligence approaches and how they are being used in oncology. We describe how artificial intelligence might be used in the detection, prognosis, and administration of cancer treatments and introduce the use of the latest large language models such as ChatGPT in oncology clinics. We highlight artificial intelligence applications for omics data types, and we offer perspectives on how the various data types might be combined to create decision-support tools. We also evaluate the present constraints and challenges to applying artificial intelligence in precision oncology. Finally, we discuss how current challenges may be surmounted to make artificial intelligence useful in clinical settings in the future.
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Affiliation(s)
- Chaoyi Zhang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jin Xu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Rong Tang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Jianhui Yang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Wei Wang
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China
| | - Xianjun Yu
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
| | - Si Shi
- Department of Pancreatic Surgery, Fudan University Shanghai Cancer Center, No. 270 Dong'An Road, Shanghai, 200032, People's Republic of China.
- Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People's Republic of China.
- Shanghai Pancreatic Cancer Institute, No. 399 Lingling Road, Shanghai, 200032, People's Republic of China.
- Pancreatic Cancer Institute, Fudan University, Shanghai, 200032, People's Republic of China.
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Wang YY, Xin ZC, Wang K. Impact of Molecular Status on Metastasectomy of Colorectal Cancer Liver Metastases. Clin Colon Rectal Surg 2023; 36:423-429. [PMID: 37795466 PMCID: PMC10547543 DOI: 10.1055/s-0043-1767700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/06/2023]
Abstract
Although surgical resection could provide better survival for patients with colorectal cancer liver metastases (CRLM), the recurrence rate after resection of CRLM remains high. The progress of genome sequencing technologies has greatly improved the molecular understanding of colorectal cancer. In the era of genomics and targeted therapy, genetic mutation analysis is of great significance to guide systemic treatment and identify patients who can benefit from resection of CRLM. RAS and BRAF mutations and microsatellite instability/deficient deoxyribonucleic acid (DNA) mismatch repair status have been incorporated into current clinical practice. Other promising molecular biomarkers such as coexisting gene mutations and circulating tumor DNA are under active investigation. This study aimed to review the prognostic significance of molecular biomarkers in patients with CRLM undergoing metastasectomy based on the current evidence.
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Affiliation(s)
- Yan-Yan Wang
- Hepatopancreatobiliary Surgery Department I, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University School of Oncology, Beijing Cancer Hospital and Institute, Beijing, China
| | - Ze-Chang Xin
- Hepatopancreatobiliary Surgery Department I, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University School of Oncology, Beijing Cancer Hospital and Institute, Beijing, China
| | - Kun Wang
- Hepatopancreatobiliary Surgery Department I, Key Laboratory of Carcinogenesis and Translational Research, Ministry of Education, Peking University School of Oncology, Beijing Cancer Hospital and Institute, Beijing, China
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11
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Wickersham M, Bartelo N, Kulm S, Liu Y, Zhang Y, Elemento O. USING MACHINE LEARNING METHODS TO ASSESS THE RISK OF ALCOHOL MISUSE IN OLDER ADULTS. RESEARCH SQUARE 2023:rs.3.rs-3154584. [PMID: 37886491 PMCID: PMC10602059 DOI: 10.21203/rs.3.rs-3154584/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
The population of older adults, defined in this study as those 50 years of age or older, continues to increase every year. Substance misuse, particularly alcohol misuse, is often neglected in these individuals. To better identify older adults who might not be properly assessed for alcohol misuse, we have derived a risk assessment tool using patients from the United Kingdom Biobank (UKB), which was validated on patients in the Weill Cornell Medicine (WCM) electronic health record (EHR). The model and tooling created stratifies the risk of alcohol misuse in older adults using 10 features that are commonly found in most EHR systems. We found that the area under the receiver operating curve (AUROC) to correctly predict alcohol misuse in older adults for the UKB and WCM models were 0.84 and 0.78, respectively. We further show that of those who self-identified as having ongoing alcohol misuse in the UKB cohort, only 12.5% of these patients had any alcohol-related F.10 ICD-10 code. Extending this to the WCM cohort, we forecast that 7,838 out of 12,360 older adults with no F.10 ICD-10 code (63.4%) may be missed as having alcohol misuse in the EHR. Overall, this study importantly prioritizes the health of older adults by being able to predict alcohol misuse in an understudied population.
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Affiliation(s)
- Matthew Wickersham
- Weill-Cornell/Rockefeller/Sloan-Kettering Tri-Institutional MD-PhD Program, New York, New York, United States
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, United States
| | - Nicholas Bartelo
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, United States
| | - Scott Kulm
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, United States
| | - Yifan Liu
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States
| | - Yiye Zhang
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, United States
- Department of Emergency Medicine, Weill Cornell Medicine, New York, New York, United States
| | - Olivier Elemento
- Department of Physiology and Biophysics, Weill Cornell Medicine, New York, New York, United States
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, New York, United States
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12
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Bertsimas D, Margonis GA, Tang S, Koulouras A, Antonescu CR, Brennan MF, Martin-Broto J, Rutkowski P, Stasinos G, Wang J, Pikoulis E, Bylina E, Sobczuk P, Gutierrez A, Jadeja B, Tap WD, Chi P, Singer S. An interpretable AI model for recurrence prediction after surgery in gastrointestinal stromal tumour: an observational cohort study. EClinicalMedicine 2023; 64:102200. [PMID: 37731933 PMCID: PMC10507206 DOI: 10.1016/j.eclinm.2023.102200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Revised: 08/16/2023] [Accepted: 08/21/2023] [Indexed: 09/22/2023] Open
Abstract
Background There are several models that predict the risk of recurrence following resection of localised, primary gastrointestinal stromal tumour (GIST). However, assessment of calibration is not always feasible and when performed, calibration of current GIST models appears to be suboptimal. We aimed to develop a prognostic model to predict the recurrence of GIST after surgery with both good discrimination and calibration by uncovering and harnessing the non-linear relationships among variables that predict recurrence. Methods In this observational cohort study, the data of 395 adult patients who underwent complete resection (R0 or R1) of a localised, primary GIST in the pre-imatinib era at Memorial Sloan Kettering Cancer Center (NY, USA) (recruited 1982-2001) and a European consortium (Spanish Group for Research in Sarcomas, 80 sites) (recruited 1987-2011) were used to train an interpretable Artificial Intelligence (AI)-based model called Optimal Classification Trees (OCT). The OCT predicted the probability of recurrence after surgery by capturing non-linear relationships among predictors of recurrence. The data of an additional 596 patients from another European consortium (Polish Clinical GIST Registry, 7 sites) (recruited 1981-2013) who were also treated in the pre-imatinib era were used to externally validate the OCT predictions with regard to discrimination (Harrell's C-index and Brier score) and calibration (calibration curve, Brier score, and Hosmer-Lemeshow test). The calibration of the Memorial Sloan Kettering (MSK) GIST nomogram was used as a comparative gold standard. We also evaluated the clinical utility of the OCT and the MSK nomogram by performing a Decision Curve Analysis (DCA). Findings The internal cohort included 395 patients (median [IQR] age, 63 [54-71] years; 214 men [54.2%]) and the external cohort included 556 patients (median [IQR] age, 60 [52-68] years; 308 men [55.4%]). The Harrell's C-index of the OCT in the external validation cohort was greater than that of the MSK nomogram (0.805 (95% CI: 0.803-0.808) vs 0.788 (95% CI: 0.786-0.791), respectively). In the external validation cohort, the slope and intercept of the calibration curve of the main OCT were 1.041 and 0.038, respectively. In comparison, the slope and intercept of the calibration curve for the MSK nomogram was 0.681 and 0.032, respectively. The MSK nomogram overestimated the recurrence risk throughout the entire calibration curve. Of note, the Brier score was lower for the OCT compared to the MSK nomogram (0.147 vs 0.564, respectively), and the Hosmer-Lemeshow test was insignificant (P = 0.087) for the OCT model but significant (P < 0.001) for the MSK nomogram. Both results confirmed the superior discrimination and calibration of the OCT over the MSK nomogram. A decision curve analysis showed that the AI-based OCT model allowed for superior decision making compared to the MSK nomogram for both patients with 25-50% recurrence risk as well as those with >50% risk of recurrence. Interpretation We present the first prognostic models of recurrence risk in GIST that demonstrate excellent discrimination, calibration, and clinical utility on external validation. Additional studies for further validation are warranted. With further validation, these tools could potentially improve patient counseling and selection for adjuvant therapy. Funding The NCI SPORE in Soft Tissue Sarcoma and NCI Cancer Center Support Grants.
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Affiliation(s)
- Dimitris Bertsimas
- Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Seehanah Tang
- Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Angelos Koulouras
- Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Cristina R. Antonescu
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Murray F. Brennan
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Javier Martin-Broto
- Medical Oncology Department, Fundación Jimenez Diaz University Hospital, Madrid, Spain
- Hospital General de Villalba, Madrid, Spain
- Instituto de Investigacion Sanitaria Fundacion Jimenez Diaz (IIS/FJD; UAM), Madrid, Spain
| | - Piotr Rutkowski
- Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | | | - Jane Wang
- Department of Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Emmanouil Pikoulis
- Third Department of Surgery, Attikon University Hospital, Athens, Greece
| | - Elzbieta Bylina
- Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Pawel Sobczuk
- Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, Poland
| | - Antonio Gutierrez
- Medical Oncology Department, Fundación Jimenez Diaz University Hospital, Madrid, Spain
- Hospital General de Villalba, Madrid, Spain
- Instituto de Investigacion Sanitaria Fundacion Jimenez Diaz (IIS/FJD; UAM), Madrid, Spain
| | - Bhumika Jadeja
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - William D. Tap
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ping Chi
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Human Oncology and Pathogenesis Program (HOPP), Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Department of Medicine, Weill Cornell Medical College, New York, NY, USA
| | - Samuel Singer
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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13
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Alaimo L, Moazzam Z, Endo Y, Lima HA, Butey SP, Ruzzenente A, Guglielmi A, Aldrighetti L, Weiss M, Bauer TW, Alexandrescu S, Poultsides GA, Maithel SK, Marques HP, Martel G, Pulitano C, Shen F, Cauchy F, Koerkamp BG, Endo I, Kitago M, Kim A, Ejaz A, Beane J, Cloyd J, Pawlik TM. The Application of Artificial Intelligence to Investigate Long-Term Outcomes and Assess Optimal Margin Width in Hepatectomy for Intrahepatic Cholangiocarcinoma. Ann Surg Oncol 2023; 30:4292-4301. [PMID: 36952150 DOI: 10.1245/s10434-023-13349-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 01/29/2023] [Indexed: 03/24/2023]
Abstract
BACKGROUND Intrahepatic cholangiocarcinoma (ICC) is associated with poor long-term outcomes, and limited evidence exists on optimal resection margin width. This study used artificial intelligence to investigate long-term outcomes and optimal margin width in hepatectomy for ICC. METHODS The study enrolled patients who underwent curative-intent resection for ICC between 1990 and 2020. The optimal survival tree (OST) was used to investigate overall (OS) and recurrence-free survival (RFS). An optimal policy tree (OPT) assigned treatment recommendations based on random forest (RF) counterfactual survival probabilities associated with each possible margin width between 0 and 20 mm. RESULTS Among 600 patients, the median resection margin was 4 mm (interquartile range [IQR], 2-10). Overall, 379 (63.2 %) patients experienced recurrence with a 5-year RFS of 28.3 % and a 5-year OS of 38.7 %. The OST identified five subgroups of patients with different OS rates based on tumor size, a carbohydrate antigen 19-9 [CA19-9] level higher than 200 U/mL, nodal status, margin width, and age (area under the curve [AUC]: training, 0.81; testing, 0.69). The patients with tumors smaller than 4.8 cm and a margin width of 2.5 mm or greater had a relative increase in 5-year OS of 37 % compared with the entire cohort. The OST for RFS estimated a 46 % improvement in the 5-year RFS for the patients younger than 60 years who had small (<4.8 cm) well- or moderately differentiated tumors without microvascular invasion. The OPT suggested five optimal margin widths to maximize the 5-year OS for the subgroups of patients based on age, tumor size, extent of hepatectomy, and CA19-9 levels. CONCLUSIONS Artificial intelligence OST identified subgroups within ICC relative to long-term outcomes. Although tumor biology dictated prognosis, the OPT suggested that different margin widths based on patient and disease characteristics may optimize ICC long-term survival.
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Affiliation(s)
- Laura Alaimo
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
- Department of Surgery, University of Verona, Verona, Italy
| | - Zorays Moazzam
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
| | - Yutaka Endo
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
| | - Henrique A Lima
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
| | - Swatika P Butey
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
| | | | | | | | - Matthew Weiss
- Department of Surgery, Johns Hopkins Hospital, Baltimore, MD, USA
| | - Todd W Bauer
- Department of Surgery, University of Virginia, Charlottesville, VA, USA
| | | | | | | | - Hugo P Marques
- Department of Surgery, Curry Cabral Hospital, Lisbon, Portugal
| | | | - Carlo Pulitano
- Department of Surgery, Royal Prince Alfred Hospital, University of Sydney, Sydney, NSW, Australia
| | - Feng Shen
- Department of Surgery, Eastern Hepatobiliary Surgery Hospital, Shanghai, China
| | - François Cauchy
- Department of Hepatobiliopancreatic Surgery and Liver Transplantation, AP-HP, Beaujon Hospital, Clichy, France
| | - Bas Groot Koerkamp
- Department of Surgery, Erasmus University Medical Centre, Rotterdam, The Netherlands
| | - Itaru Endo
- Department of Gastroenterological Surgery, Yokohama City University School of Medicine, Yokohama, Japan
| | - Minoru Kitago
- Department of Surgery, Keio University, Tokyo, Japan
| | - Alex Kim
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
| | - Aslam Ejaz
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
| | - Joal Beane
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
| | - Jordan Cloyd
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA
| | - Timothy M Pawlik
- Division of Surgical Oncology, Wexner Medical Center, James Comprehensive Cancer Center, Department of Surgery, The Ohio State University, 395 West 12th Avenue, Suite 670, Columbus, OH, USA.
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14
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Alaimo L, Moazzam Z, Pawlik TM. ASO Author Reflections: Long-Term Outcomes and Optimal Margin Width Among Patients Undergoing Hepatectomy for Intrahepatic Cholangiocarcinoma. Ann Surg Oncol 2023; 30:4302-4303. [PMID: 36964330 DOI: 10.1245/s10434-023-13351-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 02/15/2023] [Indexed: 03/26/2023]
Affiliation(s)
- Laura Alaimo
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
- Department of Surgery, University of Verona, Verona, Italy
| | - Zorays Moazzam
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA
| | - Timothy M Pawlik
- Division of Surgical Oncology, Department of Surgery, The Ohio State University Wexner Medical Center and James Comprehensive Cancer Center, Columbus, OH, USA.
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15
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Bakrania A, Joshi N, Zhao X, Zheng G, Bhat M. Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases. Pharmacol Res 2023; 189:106706. [PMID: 36813095 DOI: 10.1016/j.phrs.2023.106706] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/14/2023] [Revised: 02/17/2023] [Accepted: 02/19/2023] [Indexed: 02/22/2023]
Abstract
Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.
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Affiliation(s)
- Anita Bakrania
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.
| | | | - Xun Zhao
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada
| | - Gang Zheng
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Mamatha Bhat
- Toronto General Hospital Research Institute, Toronto, ON, Canada; Ajmera Transplant Program, University Health Network, Toronto, ON, Canada; Division of Gastroenterology, Department of Medicine, University Health Network and University of Toronto, Toronto, ON, Canada; Department of Medical Sciences, Toronto, ON, Canada.
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16
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Bertsimas D, Margonis GA. Explainable vs. interpretable artificial intelligence frameworks in oncology. Transl Cancer Res 2023; 12:217-220. [PMID: 36915595 PMCID: PMC10007880 DOI: 10.21037/tcr-22-2427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 12/31/2022] [Indexed: 01/30/2023]
Affiliation(s)
- Dimitris Bertsimas
- Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Georgios Antonios Margonis
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY, USA.,Department of General and Visceral Surgery, Charité Campus Benjamin Franklin, Berlin, Germany
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Kawaguchi Y, Jain AJ, Chun YS, Vauthey JN. Artificial Intelligence or Tumor Biology to Predict Survival After Resection of Colorectal Liver Metastases? Ann Surg Oncol 2023; 30:3161-3162. [PMID: 36809607 DOI: 10.1245/s10434-023-13223-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 01/29/2023] [Indexed: 02/23/2023]
Affiliation(s)
- Yoshikuni Kawaguchi
- Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Anish J Jain
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yun Shin Chun
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jean-Nicolas Vauthey
- Department of Surgical Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Assessing Metabolic Markers in Glioblastoma Using Machine Learning: A Systematic Review. Metabolites 2023; 13:metabo13020161. [PMID: 36837779 PMCID: PMC9958885 DOI: 10.3390/metabo13020161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 01/14/2023] [Accepted: 01/18/2023] [Indexed: 01/24/2023] Open
Abstract
Glioblastoma (GBM) is a common and deadly brain tumor with late diagnoses and poor prognoses. Machine learning (ML) is an emerging tool that can create highly accurate diagnostic and prognostic prediction models. This paper aimed to systematically search the literature on ML for GBM metabolism and assess recent advancements. A literature search was performed using predetermined search terms. Articles describing the use of an ML algorithm for GBM metabolism were included. Ten studies met the inclusion criteria for analysis: diagnostic (n = 3, 30%), prognostic (n = 6, 60%), or both (n = 1, 10%). Most studies analyzed data from multiple databases, while 50% (n = 5) included additional original samples. At least 2536 data samples were run through an ML algorithm. Twenty-seven ML algorithms were recorded with a mean of 2.8 algorithms per study. Algorithms were supervised (n = 24, 89%), unsupervised (n = 3, 11%), continuous (n = 19, 70%), or categorical (n = 8, 30%). The mean reported accuracy and AUC of ROC were 95.63% and 0.779, respectively. One hundred six metabolic markers were identified, but only EMP3 was reported in multiple studies. Many studies have identified potential biomarkers for GBM diagnosis and prognostication. These algorithms show promise; however, a consensus on even a handful of biomarkers has not yet been made.
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Hewitt DB, Brown ZJ, Pawlik TM. The Role of Biomarkers in the Management of Colorectal Liver Metastases. Cancers (Basel) 2022; 14:cancers14194602. [PMID: 36230522 PMCID: PMC9559307 DOI: 10.3390/cancers14194602] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Accepted: 09/17/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Colorectal cancer remains one of the most significant sources of cancer-related morbidity and mortality worldwide. The liver is the most common site of metastatic spread. Multiple modalities exist to manage and potentially cure patients with metastatic colorectal cancer. However, reliable biomarkers to assist with clinical decision-making are limited. Recent advances in genomic sequencing technology have greatly expanded our knowledge of colorectal cancer carcinogenesis and significantly reduced the cost and timing of the investigation. In this article, we discuss the current utility of biomarkers in the management of colorectal cancer liver metastases. Abstract Surgical management combined with improved systemic therapies have extended 5-year overall survival beyond 50% among patients with colorectal liver metastases (CRLM). Furthermore, a multitude of liver-directed therapies has improved local disease control for patients with unresectable CRLM. Unfortunately, a significant portion of patients treated with curative-intent hepatectomy develops disease recurrence. Traditional markers fail to risk-stratify and prognosticate patients with CRLM appropriately. Over the last few decades, advances in molecular sequencing technology have greatly expanded our knowledge of the pathophysiology and tumor microenvironment characteristics of CRLM. These investigations have revealed biomarkers with the potential to better inform management decisions in patients with CRLM. Actionable biomarkers such as RAS and BRAF mutations, microsatellite instability/mismatch repair status, and tumor mutational burden have been incorporated into national and societal guidelines. Other biomarkers, including circulating tumor DNA and radiomic features, are under active investigation to evaluate their clinical utility. Given the plethora of therapeutic modalities and lack of evidence on timing and sequence, reliable biomarkers are needed to assist clinicians with the development of patient-tailored management plans. In this review, we discuss the current evidence regarding biomarkers for patients with CRLM.
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Margonis GA, Vauthey J. Precision surgery for colorectal liver metastases: Current knowledge and future perspectives. Ann Gastroenterol Surg 2022; 6:606-615. [PMID: 36091304 PMCID: PMC9444843 DOI: 10.1002/ags3.12591] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Accepted: 06/05/2022] [Indexed: 11/08/2022] Open
Abstract
Precision surgery for colorectal liver metastases (CRLM) includes optimal selection of both the patient and surgery. Initial attempts of using clinical risk scores to identify patients for whom technically feasible surgery is oncologically futile failed. Since then, patient selection for single-stage hepatectomy followed three distinct approaches, all of which incorporated biomarkers. The BRAF V600E mutation, the G12V KRAS variant, and the triple mutation of RAS, TP53, and SMAD4 appear to be the most promising, but none can be used in isolation to deny surgery in otherwise resectable cases. Combining biomarkers with clinicopathologic factors that predict poor prognosis may be used to select patients for surgery, but external validation and matched analyses with medically treated counterparts are needed. Patient selection for special surgical procedures (two-stage hepatectomy [TSH], Associating Liver Partition and Portal vein Ligation for staged hepatectomy [ALPPS], and liver transplant [LT]) has been recently refined. Specifically, BRAF mutations and right-sided laterality have been proposed as separate contraindications to LT. A similar association of right-sided laterality, particularly when combined with RAS mutations, with very poor outcomes has been observed for ALPPS and has been suggested as a biologic contraindication. Data are scarce for TSH but RAS mutations may portend very poor survival following TSH completion. The selection of the best single-stage hepatectomy (optimal margin and type of resection) based on biomarkers remains debated, although there is some evidence that RAS may play a significant role. Lastly, although there are currently no criteria to select among the three special techniques based on their efficacy or appropriateness in different settings, RAS mutational status may be used to select patients for TSH, while right-sided tumor in conjunction with a RAS mutation may be a contraindication to LT and ALPPS.
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Affiliation(s)
- Georgios Antonios Margonis
- Department of SurgeryMemorial Sloan Kettering Cancer CenterNew YorkNew YorkUSA
- Department of General and Visceral SurgeryCharité Campus Benjamin FranklinBerlinGermany
| | - Jean‐Nicolas Vauthey
- Department of Surgical OncologyThe University of Texas MD Anderson Cancer CenterHoustonTexasUSA
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Wagner D, Margonis GA. Gene Alterations, Mediators, and Artificial Intelligence in Colorectal Liver Metastases. Cells 2022; 11:cells11142205. [PMID: 35883648 PMCID: PMC9316659 DOI: 10.3390/cells11142205] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Accepted: 07/12/2022] [Indexed: 02/04/2023] Open
Affiliation(s)
- Doris Wagner
- Department of General Surgery, Medical University of Graz, 8036 Graz, Austria
- Correspondence: (D.W.); (G.A.M.)
| | - Georgios Antonios Margonis
- Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
- Department of General and Visceral Surgery, Charité Campus Benjamin Franklin, 10117 Berlin, Germany
- Correspondence: (D.W.); (G.A.M.)
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