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Rizk PA, Gonzalez MR, Galoaa BM, Girgis AG, Van Der Linden L, Chang CY, Lozano-Calderon SA. Machine Learning-Assisted Decision Making in Orthopaedic Oncology. JBJS Rev 2024; 12:01874474-202407000-00005. [PMID: 38991098 DOI: 10.2106/jbjs.rvw.24.00057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/13/2024]
Abstract
» Artificial intelligence is an umbrella term for computational calculations that are designed to mimic human intelligence and problem-solving capabilities, although in the future, this may become an incomplete definition. Machine learning (ML) encompasses the development of algorithms or predictive models that generate outputs without explicit instructions, assisting in clinical predictions based on large data sets. Deep learning is a subset of ML that utilizes layers of networks that use various inter-relational connections to define and generalize data.» ML algorithms can enhance radiomics techniques for improved image evaluation and diagnosis. While ML shows promise with the advent of radiomics, there are still obstacles to overcome.» Several calculators leveraging ML algorithms have been developed to predict survival in primary sarcomas and metastatic bone disease utilizing patient-specific data. While these models often report exceptionally accurate performance, it is crucial to evaluate their robustness using standardized guidelines.» While increased computing power suggests continuous improvement of ML algorithms, these advancements must be balanced against challenges such as diversifying data, addressing ethical concerns, and enhancing model interpretability.
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Affiliation(s)
- Paul A Rizk
- Division of Orthopaedic Oncology, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Marcos R Gonzalez
- Division of Orthopaedic Oncology, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Bishoy M Galoaa
- Interdisciplinary Science & Engineering Complex (ISEC), Northeastern University, Boston, Massachusetts
| | - Andrew G Girgis
- Boston University Chobanian & Avedisian School of Medicine, Boston, Massachusetts
| | - Lotte Van Der Linden
- Division of Orthopaedic Oncology, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - Connie Y Chang
- Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts
| | - Santiago A Lozano-Calderon
- Division of Orthopaedic Oncology, Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
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Kamalapathy PN, Gonzalez MR, de Groot TM, Ramkumar D, Raskin KA, Ashkani-Esfahani S, Lozano-Calderón SA. Prediction of 5-year survival in soft tissue leiomyosarcoma using a machine learning model algorithm. J Surg Oncol 2024; 129:531-536. [PMID: 37974529 DOI: 10.1002/jso.27514] [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: 08/23/2023] [Revised: 10/16/2023] [Accepted: 10/28/2023] [Indexed: 11/19/2023]
Abstract
BACKGROUND AND OBJECTIVES Leiomyosarcoma (LMS) is associated with one of the poorest overall survivals among soft tissue sarcomas. We sought to develop and externally validate a model for 5-year survival prediction in patients with appendicular or truncal LMS using machine learning algorithms. METHODS The Surveillance, Epidemiology, and End Results (SEER) database was used for development and internal validation of the models; external validation was assessed using our institutional database. Five machine learning algorithms were developed and then tested on our institutional database. Area under the receiver operating characteristic curve (AUC) and Brier score were used to assess model performance. RESULTS A total of 2209 patients from the SEER database and 81 patients from our tertiary institution were included. All models had excellent calibration with AUC 0.84-0.85 and Brier score 0.15-0.16. After assessing the performance indicators according to the TRIPOD model, we found that the Elastic-Net Penalized Logistic Regression outperformed other models. The AUCs of the institutional data were 0.83 (imputed) and 0.85 (complete-case analysis) with a Brier score of 0.16. CONCLUSION Our study successfully developed five machine learning algorithms to assess 5-year survival in patients with LMS. The Elastic-Net Penalized Logistic Regression retained performance upon external validation with an AUC of 0.85 and Brier score of 0.15.
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Affiliation(s)
- Pramod N Kamalapathy
- Department of Orthopaedic Surgery, Division of Orthopaedic Oncology, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Marcos R Gonzalez
- Department of Orthopaedic Surgery, Division of Orthopaedic Oncology, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Tom M de Groot
- Department of Orthopaedic Surgery, Division of Orthopaedic Oncology, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Dipak Ramkumar
- Department of Orthopaedic Surgery, Beth Israel Lahey Health, Burlington, Massachusetts, USA
| | - Kevin A Raskin
- Department of Orthopaedic Surgery, Division of Orthopaedic Oncology, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Soheil Ashkani-Esfahani
- Department of Orthopaedic Surgery, Foot & Ankle Research and Innovation Lab (FARIL), Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Santiago A Lozano-Calderón
- Department of Orthopaedic Surgery, Division of Orthopaedic Oncology, Harvard Medical School, Massachusetts General Hospital, Boston, Massachusetts, USA
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Lee L, Yi T, Fice M, Achar RK, Jones C, Klein E, Buac N, Lopez-Hisijos N, Colman MW, Gitelis S, Blank AT. Development and external validation of a machine learning model for prediction of survival in undifferentiated pleomorphic sarcoma. Musculoskelet Surg 2024; 108:77-86. [PMID: 37658174 DOI: 10.1007/s12306-023-00795-w] [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: 07/01/2022] [Accepted: 08/20/2023] [Indexed: 09/03/2023]
Abstract
PURPOSE Machine learning (ML) algorithms to predict cancer survival have recently been reported for a number of sarcoma subtypes, but none have investigated undifferentiated pleomorphic sarcoma (UPS). ML is a powerful tool that has the potential to better prognosticate UPS. METHODS The Surveillance, Epidemiology, and End Results (SEER) database was queried for cases of histologically confirmed undifferentiated pleomorphic sarcoma (UPS) (n = 665). Patient, tumor, and treatment characteristics were recorded, and ML models were developed to predict 1-, 3-, and 5-year survival. The best performing ML model was externally validated using an institutional cohort of UPS patients (n = 151). RESULTS All ML models performed best at the 1-year time point and worst at the 5-year time point. On internal validation within the SEER cohort, the best models had c-statistics of 0.67-0.69 at the 5-year time point. The Multi-Layer Perceptron Neural Network (MLP) model was the best performing model and used for external validation. Similarly, the MLP model performed best at 1-year and worst at 5-year on external validation with c-statistics of 0.85 and 0.81, respectively. The MLP model was well calibrated on external validation. The MLP model has been made publicly available at https://rachar.shinyapps.io/ups_app/ . CONCLUSION Machine learning models perform well for survival prediction in UPS, though this sarcoma subtype may be more difficult to prognosticate than other subtypes. Future studies are needed to further validate the machine learning approach for UPS prognostication.
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Affiliation(s)
- L Lee
- Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA.
| | - T Yi
- Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA
| | - M Fice
- Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA
| | - R K Achar
- Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA
| | - C Jones
- Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA
| | - E Klein
- Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA
| | - N Buac
- Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA
| | - N Lopez-Hisijos
- Department of Pathology, Rush University Medical Center, Chicago, IL, USA
| | - M W Colman
- Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA
| | - S Gitelis
- Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA
| | - A T Blank
- Department of Orthopedic Surgery, Section of Orthopedic Oncology, Rush University Medical Center, 1611 W. Harrison St., Suite 300, Chicago, IL, USA
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Li S, Yi H, Leng Q, Wu Y, Mao Y. New perspectives on cancer clinical research in the era of big data and machine learning. Surg Oncol 2024; 52:102009. [PMID: 38215544 DOI: 10.1016/j.suronc.2023.102009] [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: 08/29/2023] [Accepted: 10/16/2023] [Indexed: 01/14/2024]
Abstract
In the 21st century, the development of medical science has entered the era of big data, and machine learning has become an essential tool for mining medical big data. The establishment of the SEER database has provided a wealth of epidemiological data for cancer clinical research, and the number of studies based on SEER and machine learning has been growing in recent years. This article reviews recent research based on SEER and machine learning and finds that the current focus of such studies is primarily on the development and validation of models using machine learning algorithms, with the main directions being lymph node metastasis prediction, distant metastasis prediction, and prognosis-related research. Compared to traditional models, machine learning algorithms have the advantage of stronger adaptability, but also suffer from disadvantages such as overfitting and poor interpretability, which need to be weighed in practical applications. At present, machine learning algorithms, as the foundation of artificial intelligence, have just begun to emerge in the field of cancer clinical research. The future development of oncology will enter a more precise era of cancer research, characterized by larger data, higher dimensions, and more frequent information exchange. Machine learning is bound to shine brightly in this field.
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Affiliation(s)
- Shujun Li
- Department of Hematology, Xiangya Hospital, Central South University, Changsha, 410008, China; National Clinical Research Center for Geriatric Diseases (Xiangya Hospital), China; Hunan Hematology Oncology Clinical Medical Research Center, China
| | - Hang Yi
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Qihao Leng
- Xiangya School of Medicine, Central South University, Changsha, 410013, Hunan Province, China
| | - You Wu
- Institute for Hospital Management, School of Medicine, Tsinghua University, 30 Shuangqing Rd, Haidian District, Beijing, China; Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA.
| | - Yousheng Mao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
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Kunze KN, Williams RJ, Ranawat AS, Pearle AD, Kelly BT, Karlsson J, Martin RK, Pareek A. Artificial intelligence (AI) and large data registries: Understanding the advantages and limitations of contemporary data sets for use in AI research. Knee Surg Sports Traumatol Arthrosc 2024; 32:13-18. [PMID: 38226678 DOI: 10.1002/ksa.12018] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 11/27/2023] [Indexed: 01/17/2024]
Affiliation(s)
- Kyle N Kunze
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Riley J Williams
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Anil S Ranawat
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Andrew D Pearle
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Bryan T Kelly
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
| | - Jon Karlsson
- Department of Orthopaedics, Sahlgrenska University Hospital, Sahlgrenska Academy, Gothenburg University, Gothenburg, Sweden
| | - R Kyle Martin
- Department of Orthopedic Surgery, University of Minnesota, Minneapolis, Minnesota, USA
| | - Ayoosh Pareek
- Department of Orthopaedic Surgery, Hospital for Special Surgery, New York, New York, USA
- Sports Medicine and Shoulder Service, Hospital for Special Surgery, New York, New York, USA
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Karnuta JM, Murphy MP, Luu BC, Ryan MJ, Haeberle HS, Brown NM, Iorio R, Chen AF, Ramkumar PN. Artificial Intelligence for Automated Implant Identification in Total Hip Arthroplasty: A Multicenter External Validation Study Exceeding Two Million Plain Radiographs. J Arthroplasty 2023; 38:1998-2003.e1. [PMID: 35271974 DOI: 10.1016/j.arth.2022.03.002] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Revised: 02/23/2022] [Accepted: 03/01/2022] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND The surgical management of complications after total hip arthroplasty (THA) necessitates accurate identification of the femoral implant manufacturer and model. Automated image processing using deep learning has been previously developed and internally validated; however, external validation is necessary prior to responsible application of artificial intelligence (AI)-based technologies. METHODS We trained, validated, and externally tested a deep learning system to classify femoral-sided THA implants as one of the 8 models from 2 manufacturers derived from 2,954 original, deidentified, retrospectively collected anteroposterior plain radiographs across 3 academic referral centers and 13 surgeons. From these radiographs, 2,117 were used for training, 249 for validation, and 588 for external testing. Augmentation was applied to the training set (n = 2,117,000) to increase model robustness. Performance was evaluated by area under the receiver operating characteristic curve, sensitivity, specificity, and accuracy. Implant identification processing speed was calculated. RESULTS The training and testing sets were drawn from statistically different populations of implants (P < .001). After 1,000 training epochs by the deep learning system, the system discriminated 8 implant models with a mean area under the receiver operating characteristic curve of 0.991, accuracy of 97.9%, sensitivity of 88.6%, and specificity of 98.9% in the external testing dataset of 588 anteroposterior radiographs. The software classified implants at a mean speed of 0.02 seconds per image. CONCLUSION An AI-based software demonstrated excellent internal and external validation. Although continued surveillance is necessary with implant library expansion, this software represents responsible and meaningful clinical application of AI with immediate potential to globally scale and assist in preoperative planning prior to revision THA.
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Affiliation(s)
- Jaret M Karnuta
- Orthopaedic Machine Learning Laboratory, Orthopaedic Intelligence LLC, Cleveland Heights, OH; Department of Orthopaedic Surgery, University of Pennsylvania, Philadelphia, PA
| | - Michael P Murphy
- Department of Orthopaedic Surgery & Rehabilitation, Loyola University Medical Center, Chicago, IL
| | - Bryan C Luu
- Orthopaedic Machine Learning Laboratory, Orthopaedic Intelligence LLC, Cleveland Heights, OH; Department of Orthopaedic Surgery, Baylor College of Medicine, Houston, TX
| | - Michael J Ryan
- Orthopaedic Machine Learning Laboratory, Orthopaedic Intelligence LLC, Cleveland Heights, OH
| | - Heather S Haeberle
- Orthopaedic Machine Learning Laboratory, Orthopaedic Intelligence LLC, Cleveland Heights, OH; Sports Medicine Institute, Hospital for Special Surgery, New York, NY
| | - Nicholas M Brown
- Department of Orthopaedic Surgery & Rehabilitation, Loyola University Medical Center, Chicago, IL
| | - Richard Iorio
- Department of Orthopaedic Surgery, Brigham & Women's Hospital, Boston, MA
| | - Antonia F Chen
- Department of Orthopaedic Surgery, Brigham & Women's Hospital, Boston, MA
| | - Prem N Ramkumar
- Orthopaedic Machine Learning Laboratory, Orthopaedic Intelligence LLC, Cleveland Heights, OH; Sports Medicine Institute, Hospital for Special Surgery, New York, NY; Department of Orthopaedic Surgery, Brigham & Women's Hospital, Boston, MA
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Yeramosu T, Ahmad W, Bashir A, Wait J, Bassett J, Domson G. Predicting five-year mortality in soft-tissue sarcoma patients. Bone Joint J 2023; 105-B:702-710. [PMID: 37257862 DOI: 10.1302/0301-620x.105b6.bjj-2022-0998.r1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Aims The aim of this study was to identify factors associated with five-year cancer-related mortality in patients with limb and trunk soft-tissue sarcoma (STS) and develop and validate machine learning algorithms in order to predict five-year cancer-related mortality in these patients. Methods Demographic, clinicopathological, and treatment variables of limb and trunk STS patients in the Surveillance, Epidemiology, and End Results Program (SEER) database from 2004 to 2017 were analyzed. Multivariable logistic regression was used to determine factors significantly associated with five-year cancer-related mortality. Various machine learning models were developed and compared using area under the curve (AUC), calibration, and decision curve analysis. The model that performed best on the SEER testing data was further assessed to determine the variables most important in its predictive capacity. This model was externally validated using our institutional dataset. Results A total of 13,646 patients with STS from the SEER database were included, of whom 35.9% experienced five-year cancer-related mortality. The random forest model performed the best overall and identified tumour size as the most important variable when predicting mortality in patients with STS, followed by M stage, histological subtype, age, and surgical excision. Each variable was significant in logistic regression. External validation yielded an AUC of 0.752. Conclusion This study identified clinically important variables associated with five-year cancer-related mortality in patients with limb and trunk STS, and developed a predictive model that demonstrated good accuracy and predictability. Orthopaedic oncologists may use these findings to further risk-stratify their patients and recommend an optimal course of treatment.
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Affiliation(s)
- Teja Yeramosu
- School of Medicine, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Waleed Ahmad
- School of Medicine, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Azhar Bashir
- Department of Orthopaedic Surgery, School of Medicine, Virginia Commonwealth University, Richmond, Virginia, USA
| | - Jacob Wait
- School of Medicine, Virginia Commonwealth University, Richmond, Virginia, USA
| | - James Bassett
- College of Medicine and Life Sciences, University of Toledo, Toledo, Ohio, USA
| | - Gregory Domson
- Department of Orthopaedic Surgery, School of Medicine, Virginia Commonwealth University, Richmond, Virginia, USA
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Abstract
OPINION STATEMENT Myxoid liposarcoma (MLPS) is a rare soft tissue sarcoma, with propensity to metastasize to locations such as soft tissue and bone. Thus, whole-body MRI should be a consideration as part of staging for patients with a new diagnosis of MLPS since PET and CT may not identify extrapulmonary disease. Surveillance imaging should be tailored, with consideration of more frequent and longer duration of monitoring for large tumors or tumors with round cell component. This review focuses on studies evaluating imaging in MLPS as well as recent publications on survival and prognostic tools in MLPS.
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Affiliation(s)
- Thanh P Ho
- Division of Medical Oncology, Mayo Clinic, 200 First Street SW, Rochester, MN, 55905, USA.
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Li MD, Ahmed SR, Choy E, Lozano-Calderon SA, Kalpathy-Cramer J, Chang CY. Artificial intelligence applied to musculoskeletal oncology: a systematic review. Skeletal Radiol 2022; 51:245-256. [PMID: 34013447 DOI: 10.1007/s00256-021-03820-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 05/13/2021] [Accepted: 05/13/2021] [Indexed: 02/02/2023]
Abstract
Developments in artificial intelligence have the potential to improve the care of patients with musculoskeletal tumors. We performed a systematic review of the published scientific literature to identify the current state of the art of artificial intelligence applied to musculoskeletal oncology, including both primary and metastatic tumors, and across the radiology, nuclear medicine, pathology, clinical research, and molecular biology literature. Through this search, we identified 252 primary research articles, of which 58 used deep learning and 194 used other machine learning techniques. Articles involving deep learning have mostly involved bone scintigraphy, histopathology, and radiologic imaging. Articles involving other machine learning techniques have mostly involved transcriptomic analyses, radiomics, and clinical outcome prediction models using medical records. These articles predominantly present proof-of-concept work, other than the automated bone scan index for bone metastasis quantification, which has translated to clinical workflows in some regions. We systematically review and discuss this literature, highlight opportunities for multidisciplinary collaboration, and identify potentially clinically useful topics with a relative paucity of research attention. Musculoskeletal oncology is an inherently multidisciplinary field, and future research will need to integrate and synthesize noisy siloed data from across clinical, imaging, and molecular datasets. Building the data infrastructure for collaboration will help to accelerate progress towards making artificial intelligence truly useful in musculoskeletal oncology.
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Affiliation(s)
- Matthew D Li
- Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. .,Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Syed Rakin Ahmed
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.,Harvard Medical School, Harvard Graduate Program in Biophysics, Harvard University, Cambridge, MA, USA.,Geisel School of Medicine At Dartmouth, Dartmouth College, Hanover, NH, USA
| | - Edwin Choy
- Division of Hematology Oncology, Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Santiago A Lozano-Calderon
- Department of Orthopedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Connie Y Chang
- Division of Musculoskeletal Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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