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Lilleby W, Kishan A, Geinitz H. Acute and long-term toxicity in primary hypofractionated external photon radiation therapy in patients with localized prostate cancer. World J Urol 2024; 42:41. [PMID: 38244053 PMCID: PMC10799812 DOI: 10.1007/s00345-023-04714-3] [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: 05/26/2023] [Accepted: 11/05/2023] [Indexed: 01/22/2024] Open
Abstract
BACKGROUND Compelling evidence exists for the iso-effectiveness and safety of moderate hypofractionated radiotherapy (Hypo-RT) schedules [1, 2]. However, international guidelines are not congruent regarding recommendation of ultrahypofractionated radiotherapy (UHF-RT) to all risk groups. METHODS The current review gives an overview of clinically relevant toxicity extracted from major randomized controlled trials (RCT) trials comparing conventional to hypofractionated regimes in the primary setting of external photon radiation. Functional impairments are reported by using physician-rated and patient-reported scores using validated questionnaires. RESULTS The uncertain radiobiology of the urethra/bladder when applying extreme hypofractionation may have contributed to worse acute urinary toxicity score in the Scandinavian UHF-RT and worse subacute toxicity in PACE-B. The observed trend of increased acute GI toxicity in several moderate Hypo-RT trials and one UHF-RT trial, the Scandinavian Hypo-RT PC trial, could be associated to the different planning margins and radiation dose schedules. CONCLUSION Nevertheless, Hypo-RT has gained ground for patients with localized PCa and further improvements may be achieved by inclusion of genetically assessed radiation sensitivity. Several RCTs in Hypo-RT have shown non-inferior outcome and well-tolerated treatment toxicity by physician-rated scores. In the future, we suggest that toxicity should be measured by patient-reported outcome (PRO) using comparable questionnaires.
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Affiliation(s)
| | - Amar Kishan
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Hans Geinitz
- Department of Radiation Oncology, Hospital of the Barmherzigen Schwestern, Ordensklinikum, Linz, Austria
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Bazarkin A, Morozov A, Androsov A, Fajkovic H, Rivas JG, Singla N, Koroleva S, Teoh JYC, Zvyagin AV, Shariat SF, Somani B, Enikeev D. Assessment of Prostate and Bladder Cancer Genomic Biomarkers Using Artificial Intelligence: a Systematic Review. Curr Urol Rep 2024; 25:19-35. [PMID: 38099997 DOI: 10.1007/s11934-023-01193-2] [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] [Accepted: 12/01/2023] [Indexed: 01/14/2024]
Abstract
PURPOSE OF REVIEW The aim of the systematic review is to assess AI's capabilities in the genetics of prostate cancer (PCa) and bladder cancer (BCa) to evaluate target groups for such analysis as well as to assess its prospects in daily practice. RECENT FINDINGS In total, our analysis included 27 articles: 10 articles have reported on PCa and 17 on BCa, respectively. The AI algorithms added clinical value and demonstrated promising results in several fields, including cancer detection, assessment of cancer development risk, risk stratification in terms of survival and relapse, and prediction of response to a specific therapy. Besides clinical applications, genetic analysis aided by the AI shed light on the basic urologic cancer biology. We believe, our results of the AI application to the analysis of PCa, BCa data sets will help to identify new targets for urological cancer therapy. The integration of AI in genomic research for screening and clinical applications will evolve with time to help personalizing chemotherapy, prediction of survival and relapse, aid treatment strategies such as reducing frequency of diagnostic cystoscopies, and clinical decision support, e.g., by predicting immunotherapy response. These factors will ultimately lead to personalized and precision medicine thereby improving patient outcomes.
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Affiliation(s)
- Andrey Bazarkin
- Institute for Urology and Reproductive Health, Sechenov University, Moscow, Russia
| | - Andrey Morozov
- Institute for Urology and Reproductive Health, Sechenov University, Moscow, Russia
| | - Alexander Androsov
- Department of Pediatric Surgery, Division of Pediatric Urology and Andrology, Sechenov University, Moscow, Russia
| | - Harun Fajkovic
- Department of Urology and Comprehensive Cancer Center, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Karl Landsteiner Institute of Urology and Andrology, Vienna, Austria
| | - Juan Gomez Rivas
- Department of Urology, Clinico San Carlos University Hospital, Madrid, Spain
| | - Nirmish Singla
- School of Medicine, Brady Urological Institute, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Svetlana Koroleva
- Clinical Institute for Children Health Named After N.F. Filatov, Sechenov University, Moscow, Russia
| | - Jeremy Yuen-Chun Teoh
- Department of Surgery, S.H. Ho Urology Centre, The Chinese University of Hong Kong, Hong Kong, China
| | - Andrei V Zvyagin
- Institute of Molecular Theranostics, Sechenov University, Moscow, Russia
- Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, 117997, Moscow, Russia
| | - Shahrokh François Shariat
- Department of Urology and Comprehensive Cancer Center, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria
- Karl Landsteiner Institute of Urology and Andrology, Vienna, Austria
- Department of Urology, Weill Cornell Medical College, New York, NY, USA
- Department of Urology, University of Texas Southwestern, Dallas, TX, USA
- Department of Urology, Second Faculty of Medicine, Charles University, Prague, Czech Republic
- Division of Urology, Department of Special Surgery, Jordan University Hospital, The University of Jordan, Amman, Jordan
| | - Bhaskar Somani
- Department of Urology, University Hospital Southampton, Southampton, United Kingdom
| | - Dmitry Enikeev
- Institute for Urology and Reproductive Health, Sechenov University, Moscow, Russia.
- Department of Urology and Comprehensive Cancer Center, Medical University of Vienna, Währinger Gürtel 18-20, 1090, Vienna, Austria.
- Karl Landsteiner Institute of Urology and Andrology, Vienna, Austria.
- Division of Urology, Rabin Medical Center, Petah Tikva, Israel.
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Salama V, Geng Y, Rigert J, Fuller CD, Shete S, Moreno AC. Systematic Review of Genetic Polymorphisms Associated with Acute Pain Induced by Radiotherapy for Head and Neck Cancers. Clin Transl Radiat Oncol 2023; 43:100669. [PMID: 37954025 PMCID: PMC10634655 DOI: 10.1016/j.ctro.2023.100669] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/10/2023] [Accepted: 08/13/2023] [Indexed: 11/14/2023] Open
Abstract
Background/objective Pain is the most common acute symptom following radiation therapy (RT) for head and neck cancer (HNC). The multifactorial origin of RT-induced pain makes it highly challenging to manage. Multiple studies were conducted to identify genetic variants associated with cancer pain, however few of them focused on RT-induced acute pain. In this review, we summarize the potential mechanisms of acute pain after RT in HNC and identify genetic variants associated with RT-induced acute pain and relevant acute toxicities. Methods A comprehensive search of Ovid Medline, EMBASE and Web of Science databases using terms including "Variants", "Polymorphisms", "Radiotherapy", "Acute pain", "Acute toxicity" published up to February 28, 2022, was performed by two reviewers. Review articles and citations were reviewed manually. The identified SNPs associated with RT-induced acute pain and toxicities were reported, and the molecular functions of the associated genes were described based on genetic annotation using The Human Gene Database; GeneCards. Results A total of 386 articles were identified electronically and 8 more articles were included after manual search. 21 articles were finally included. 32 variants in 27 genes, of which 25% in inflammatory/immune response, 20% had function in DNA damage response and repair, 20% in cell death or cell cycle, were associated with RT-inflammatory pain and acute oral mucositis or dermatitis. 4 variants in 4 genes were associated with neuropathy and neuropathic pain. 5 variants in 4 genes were associated with RT-induced mixed types of post-RT-throat/neck pain. Conclusion Different types of pain develop after RT in HNC, including inflammatory pain; neuropathic pain; nociceptive pain; and mixed oral pain. Genetic variants involved in DNA damage response and repair, cell death, inflammation and neuropathic pathways may affect pain presentation post-RT. These variants could be used for personalized pain management in HNC patients receiving RT.
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Affiliation(s)
- Vivian Salama
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yimin Geng
- Research Medical Library, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Jillian Rigert
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Sanjay Shete
- Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Amy C. Moreno
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Froń A, Semianiuk A, Lazuk U, Ptaszkowski K, Siennicka A, Lemiński A, Krajewski W, Szydełko T, Małkiewicz B. Artificial Intelligence in Urooncology: What We Have and What We Expect. Cancers (Basel) 2023; 15:4282. [PMID: 37686558 PMCID: PMC10486651 DOI: 10.3390/cancers15174282] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2023] [Revised: 08/15/2023] [Accepted: 08/24/2023] [Indexed: 09/10/2023] Open
Abstract
INTRODUCTION Artificial intelligence is transforming healthcare by driving innovation, automation, and optimization across various fields of medicine. The aim of this study was to determine whether artificial intelligence (AI) techniques can be used in the diagnosis, treatment planning, and monitoring of urological cancers. METHODOLOGY We conducted a thorough search for original and review articles published until 31 May 2022 in the PUBMED/Scopus database. Our search included several terms related to AI and urooncology. Articles were selected with the consensus of all authors. RESULTS Several types of AI can be used in the medical field. The most common forms of AI are machine learning (ML), deep learning (DL), neural networks (NNs), natural language processing (NLP) systems, and computer vision. AI can improve various domains related to the management of urologic cancers, such as imaging, grading, and nodal staging. AI can also help identify appropriate diagnoses, treatment options, and even biomarkers. In the majority of these instances, AI is as accurate as or sometimes even superior to medical doctors. CONCLUSIONS AI techniques have the potential to revolutionize the diagnosis, treatment, and monitoring of urologic cancers. The use of AI in urooncology care is expected to increase in the future, leading to improved patient outcomes and better overall management of these tumors.
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Affiliation(s)
- Anita Froń
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Alina Semianiuk
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Uladzimir Lazuk
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Kuba Ptaszkowski
- Department of Physiotherapy, Wroclaw Medical University, 50-368 Wroclaw, Poland;
| | - Agnieszka Siennicka
- Department of Physiology and Pathophysiology, Wroclaw Medical University, 50-556 Wroclaw, Poland;
| | - Artur Lemiński
- Department of Urology and Urological Oncology, Pomeranian Medical University, 70-111 Szczecin, Poland;
| | - Wojciech Krajewski
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Tomasz Szydełko
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
| | - Bartosz Małkiewicz
- Department of Minimally Invasive and Robotic Urology, University Center of Excellence in Urology, Wroclaw Medical University, 50-556 Wroclaw, Poland; (A.S.); (U.L.); (W.K.); (T.S.)
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Susmitha P, Kumar P, Yadav P, Sahoo S, Kaur G, Pandey MK, Singh V, Tseng TM, Gangurde SS. Genome-wide association study as a powerful tool for dissecting competitive traits in legumes. FRONTIERS IN PLANT SCIENCE 2023; 14:1123631. [PMID: 37645459 PMCID: PMC10461012 DOI: 10.3389/fpls.2023.1123631] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 06/08/2023] [Indexed: 08/31/2023]
Abstract
Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.
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Affiliation(s)
- Pusarla Susmitha
- Regional Agricultural Research Station, Acharya N.G. Ranga Agricultural University, Andhra Pradesh, India
| | - Pawan Kumar
- Department of Genetics and Plant Breeding, College of Agriculture, Chaudhary Charan Singh (CCS) Haryana Agricultural University, Hisar, India
| | - Pankaj Yadav
- Department of Bioscience and Bioengineering, Indian Institute of Technology, Rajasthan, India
| | - Smrutishree Sahoo
- Department of Genetics and Plant Breeding, School of Agriculture, Gandhi Institute of Engineering and Technology (GIET) University, Odisha, India
| | - Gurleen Kaur
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Manish K. Pandey
- Department of Genomics, Prebreeding and Bioinformatics, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India
| | - Varsha Singh
- Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, United States
| | - Te Ming Tseng
- Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, United States
| | - Sunil S. Gangurde
- Department of Plant Pathology, University of Georgia, Tifton, GA, United States
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Oh JH, Lee S, Thor M, Rosenstein BS, Tannenbaum A, Kerns S, Deasy JO. Predicting the germline dependence of hematuria risk in prostate cancer radiotherapy patients. Radiother Oncol 2023; 185:109723. [PMID: 37244355 PMCID: PMC10524941 DOI: 10.1016/j.radonc.2023.109723] [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: 12/24/2022] [Revised: 05/09/2023] [Accepted: 05/17/2023] [Indexed: 05/29/2023]
Abstract
BACKGROUND AND PURPOSE Late radiation-induced hematuria can develop in prostate cancer patients undergoing radiotherapy and can negatively impact the quality-of-life of survivors. If a genetic component of risk could be modeled, this could potentially be the basis for modifying treatment for high-risk patients. We therefore investigated whether a previously developed machine learning-based modeling method using genome-wide common single nucleotide polymorphisms (SNPs) can stratify patients in terms of the risk of radiation-induced hematuria. MATERIALS AND METHODS We applied a two-step machine learning algorithm that we previously developed for genome-wide association studies called pre-conditioned random forest regression (PRFR). PRFR includes a pre-conditioning step, producing adjusted outcomes, followed by random forest regression modeling. Data was from germline genome-wide SNPs for 668 prostate cancer patients treated with radiotherapy. The cohort was stratified only once, at the outset of the modeling process, into two groups: a training set (2/3 of samples) for modeling and a validation set (1/3 of samples). Post-modeling bioinformatics analysis was conducted to identify biological correlates plausibly associated with the risk of hematuria. RESULTS The PRFR method achieved significantly better predictive performance compared to other alternative methods (all p < 0.05). The odds ratio between the high and low risk groups, each of which consisted of 1/3 of samples in the validation set, was 2.87 (p = 0.029), implying a clinically useful level of discrimination. Bioinformatics analysis identified six key proteins encoded by CTNND2, GSK3B, KCNQ2, NEDD4L, PRKAA1, and TXNL1 genes as well as four statistically significant biological process networks previously shown to be associated with the bladder and urinary tract. CONCLUSION The risk of hematuria is significantly dependent on common genetic variants. The PRFR algorithm resulted in a stratification of prostate cancer patients at differential risk levels of post-radiotherapy hematuria. Bioinformatics analysis identified important biological processes involved in radiation-induced hematuria.
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Affiliation(s)
- Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States.
| | - Sangkyu Lee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Maria Thor
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | - Barry S Rosenstein
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Allen Tannenbaum
- Departments of Computer Science and Applied Mathematics & Statistics, Stony Brook University, Stony Brook, NY, United States
| | - Sarah Kerns
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, United States
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States
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Kerns SL, Hall WA, Marples B, West CML. Normal Tissue Toxicity Prediction: Clinical Translation on the Horizon. Semin Radiat Oncol 2023; 33:307-316. [PMID: 37331785 DOI: 10.1016/j.semradonc.2023.03.010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
Improvements in radiotherapy delivery have enabled higher therapeutic doses and improved efficacy, contributing to the growing number of long-term cancer survivors. These survivors are at risk of developing late toxicity from radiotherapy, and the inability to predict who is most susceptible results in substantial impact on quality of life and limits further curative dose escalation. A predictive assay or algorithm for normal tissue radiosensitivity would allow more personalized treatment planning, reducing the burden of late toxicity, and improving the therapeutic index. Progress over the last 10 years has shown that the etiology of late clinical radiotoxicity is multifactorial and informs development of predictive models that combine information on treatment (eg, dose, adjuvant treatment), demographic and health behaviors (eg, smoking, age), co-morbidities (eg, diabetes, collagen vascular disease), and biology (eg, genetics, ex vivo functional assays). AI has emerged as a useful tool and is facilitating extraction of signal from large datasets and development of high-level multivariable models. Some models are progressing to evaluation in clinical trials, and we anticipate adoption of these into the clinical workflow in the coming years. Information on predicted risk of toxicity could prompt modification of radiotherapy delivery (eg, use of protons, altered dose and/or fractionation, reduced volume) or, in rare instances of very high predicted risk, avoidance of radiotherapy. Risk information can also be used to assist treatment decision-making for cancers where efficacy of radiotherapy is equivalent to other treatments (eg, low-risk prostate cancer) and can be used to guide follow-up screening in instances where radiotherapy is still the best choice to maximize tumor control probability. Here, we review promising predictive assays for clinical radiotoxicity and highlight studies that are progressing to develop an evidence base for clinical utility.
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Affiliation(s)
- Sarah L Kerns
- Department of Radiation Oncology, the Medical College of Wisconsin, Milwaukee, WI.
| | - William A Hall
- Department of Radiation Oncology, the Medical College of Wisconsin, Milwaukee, WI
| | - Brian Marples
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY
| | - Catharine M L West
- Division of Cancer Sciences, the University of Manchester, Manchester Academic Health Science Centre, Christie Hospital, Manchester, UK
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Núñez-Benjumea FJ, González-García S, Moreno-Conde A, Riquelme-Santos JC, López-Guerra JL. Benchmarking machine learning approaches to predict radiation-induced toxicities in lung cancer patients. Clin Transl Radiat Oncol 2023; 41:100640. [PMID: 37251617 PMCID: PMC10213176 DOI: 10.1016/j.ctro.2023.100640] [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: 04/22/2023] [Accepted: 05/09/2023] [Indexed: 05/31/2023] Open
Abstract
Background and purpose Radiation-induced toxicities are common adverse events in lung cancer (LC) patients undergoing radiotherapy (RT). An accurate prediction of these adverse events might facilitate an informed and shared decision-making process between patient and radiation oncologist with a clearer view of life-balance implications in treatment choices. This work provides a benchmark of machine learning (ML) approaches to predict radiation-induced toxicities in LC patients built upon a real-world health dataset based on a generalizable methodology for their implementation and external validation. Materials and Methods Ten feature selection (FS) methods were combined with five ML-based classifiers to predict six RT-induced toxicities (acute esophagitis, acute cough, acute dyspnea, acute pneumonitis, chronic dyspnea, and chronic pneumonitis). A real-world health dataset (RWHD) built from 875 consecutive LC patients was used to train and validate the resulting 300 predictive models. Internal and external accuracy was calculated in terms of AUC per clinical endpoint, FS method, and ML-based classifier under analysis. Results Best performing predictive models obtained per clinical endpoint achieved comparable performances to methods from state-of-the-art at internal validation (AUC ≥ 0.81 in all cases) and at external validation (AUC ≥ 0.73 in 5 out of 6 cases). Conclusion A benchmark of 300 different ML-based approaches has been tested against a RWHD achieving satisfactory results following a generalizable methodology. The outcomes suggest potential relationships between underrecognized clinical factors and the onset of acute esophagitis or chronic dyspnea, thus demonstrating the potential that ML-based approaches have to generate novel data-driven hypotheses in the field.
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Affiliation(s)
- Francisco J. Núñez-Benjumea
- Innovation & Data Analysis Unit, Institute of Biomedicine of Seville, IBiS/Virgen Macarena University Hospital/CSIC/University of Seville, Seville, Spain
| | - Sara González-García
- Institute of Biomedicine of Seville, IBIS/Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, Spain
| | - Alberto Moreno-Conde
- Innovation & Data Analysis Unit, Institute of Biomedicine of Seville, IBiS/Virgen Macarena University Hospital/CSIC/University of Seville, Seville, Spain
| | | | - José L. López-Guerra
- Radiation Oncology Department, Institute of Biomedicine of Seville, IBIS/Virgen del Rocío University Hospital/CSIC/University of Seville, Seville, Spain
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Zhang Z, Wei X. Artificial intelligence-assisted selection and efficacy prediction of antineoplastic strategies for precision cancer therapy. Semin Cancer Biol 2023; 90:57-72. [PMID: 36796530 DOI: 10.1016/j.semcancer.2023.02.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 01/12/2023] [Accepted: 02/13/2023] [Indexed: 02/16/2023]
Abstract
The rapid development of artificial intelligence (AI) technologies in the context of the vast amount of collectable data obtained from high-throughput sequencing has led to an unprecedented understanding of cancer and accelerated the advent of a new era of clinical oncology with a tone of precision treatment and personalized medicine. However, the gains achieved by a variety of AI models in clinical oncology practice are far from what one would expect, and in particular, there are still many uncertainties in the selection of clinical treatment options that pose significant challenges to the application of AI in clinical oncology. In this review, we summarize emerging approaches, relevant datasets and open-source software of AI and show how to integrate them to address problems from clinical oncology and cancer research. We focus on the principles and procedures for identifying different antitumor strategies with the assistance of AI, including targeted cancer therapy, conventional cancer therapy, and cancer immunotherapy. In addition, we also highlight the current challenges and directions of AI in clinical oncology translation. Overall, we hope this article will provide researchers and clinicians with a deeper understanding of the role and implications of AI in precision cancer therapy, and help AI move more quickly into accepted cancer guidelines.
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Affiliation(s)
- Zhe Zhang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China; State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, and Collaborative Innovation Center for Biotherapy, Chengdu 610041, PR China
| | - Xiawei Wei
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu 610041, PR China.
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Voon NS, Manan HA, Yahya N. Remote assessment of cognition and quality of life following radiotherapy for nasopharyngeal carcinoma: deep-learning-based predictive models and MRI correlates. J Cancer Surviv 2023:10.1007/s11764-023-01371-8. [PMID: 37010777 PMCID: PMC10069366 DOI: 10.1007/s11764-023-01371-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Accepted: 03/22/2023] [Indexed: 04/04/2023]
Abstract
PURPOSE Irradiation of the brain regions from nasopharyngeal carcinoma (NPC) radiotherapy (RT) is frequently unavoidable, which may result in radiation-induced cognitive deficit. Using deep learning (DL), the study aims to develop prediction models in predicting compromised cognition in patients following NPC RT using remote assessments and determine their relation to the quality of life (QoL) and MRI changes. METHODS Seventy patients (20-76 aged) with MRI imaging (pre- and post-RT (6 months-1 year)) and complete cognitive assessments were recruited. Hippocampus, temporal lobes (TLs), and cerebellum were delineated and dosimetry parameters were extracted. Assessments were given post-RT via telephone (Telephone Interview Cognitive Status (TICS), Telephone Montreal Cognitive Assessment (T-MoCA), Telephone Mini Addenbrooke's Cognitive Examination (Tele-MACE), and QLQ-H&N 43). Regression and deep neural network (DNN) models were used to predict post-RT cognition using anatomical and treatment dose features. RESULTS Remote cognitive assessments were inter-correlated (r > 0.9). TLs showed significance in pre- and post-RT volume differences and cognitive deficits, that are correlated with RT-associated volume atrophy and dose distribution. Good classification accuracy based on DNN area under receiver operating curve (AUROC) for cognitive prediction (T-MoCA AUROC = 0.878, TICS AUROC = 0.89, Tele-MACE AUROC = 0.919). CONCLUSION DL-based prediction models assessed using remote assessments can assist in predicting cognitive deficit following NPC RT. Comparable results of remote assessments in assessing cognition suggest its possibility in replacing standard assessments. IMPLICATIONS FOR CANCER SURVIVORS Application of prediction models in individual patient enables tailored interventions to be provided in managing cognitive changes following NPC RT.
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Affiliation(s)
- Noor Shatirah Voon
- Diagnostic Imaging and Radiotherapy, Centre for Diagnostic, Therapeutic and Investigative Sciences (CODTIS), Faculty of Health Sciences, National University of Malaysia, Jalan Raja Muda Aziz, 50300, Kuala Lumpur, Malaysia
- National Cancer Institute, Ministry of Health, Jalan P7, Presint 7, 62250, Putrajaya, Malaysia
| | - Hanani Abdul Manan
- Functional Image Processing Laboratory, Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Cheras, 56000, Kuala Lumpur, Malaysia
| | - Noorazrul Yahya
- Diagnostic Imaging and Radiotherapy, Centre for Diagnostic, Therapeutic and Investigative Sciences (CODTIS), Faculty of Health Sciences, National University of Malaysia, Jalan Raja Muda Aziz, 50300, Kuala Lumpur, Malaysia.
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11
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Kerns SL, Williams JP, Marples B. Modeling normal bladder injury after radiation therapy. Int J Radiat Biol 2023; 99:1046-1054. [PMID: 36854008 PMCID: PMC10330568 DOI: 10.1080/09553002.2023.2182000] [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: 12/19/2022] [Revised: 02/03/2023] [Accepted: 02/06/2023] [Indexed: 03/02/2023]
Abstract
PURPOSE For decades, Dr. John Moulder has been a leading radiation biologist and one of the few who consistently supported the study of normal tissue responses to radiation. His meticulous modeling and collaborations across the field have offered a prime example of how research can be taken from the bench to the bedside and back, with the ultimate goal of providing benefit to patients. Much of the focus of John's work was on mitigating damage to the kidney, whether as the result of accidental or deliberate clinical exposures. Following in his footsteps, we offer here a brief overview of work conducted in the field of radiation-induced bladder injury. We then describe our own preclinical experimental studies which originated as a response to reports from a clinical genome-wide association study (GWAS) investigating genomic biomarkers of normal tissue toxicity in prostate cancer patients treated with radiotherapy. In particular, we discuss the use of Renin-Angiotensin System (RAS) inhibitors as modulators of injury, agents championed by the Moulder group, and how RAS inhibitors are associated with a reduction in some measures of toxicity. Using a murine model, along with precise CT-image guided irradiation of the bladder using single and fractionated dosing regimens, we have been able to demonstrate radiation-induced functional injury to the bladder and mitigation of this functional damage by an inhibitor of angiotensin-converting enzyme targeting the RAS, an experimental approach akin to that used by the Moulder group. We consider our scientific trajectory as a bedside-to-bench approach because the observation was made clinically and investigated in a preclinical model; this experimental approach aligns with the exemplary career of Dr. John Moulder. CONCLUSIONS Despite the differences in functional endpoints, recent findings indicate a commonality between bladder late effects and the work in kidney pioneered by Dr. John Moulder. We offer evidence that targeting the RAS pathway may provide a targetable pathway to reducing late bladder toxicity.
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Affiliation(s)
- Sarah L. Kerns
- Department of Department of Radiation Oncology, the Medical College of Wisconsin, Milwaukee, WI, USA
| | - Jacqueline P. Williams
- Departments of Environmental Medicine, University of Rochester Medical Center, Rochester, NY, USA
- Departments of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, USA
| | - Brian Marples
- Departments of Radiation Oncology, University of Rochester Medical Center, Rochester, NY, USA
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12
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Kishan AU, Marco N, Ma TM, Steinberg ML, Sachdeva A, Cao M, Ballas LK, Rietdorf E, Telesca D, Weidhaas JB. Application of a genetic signature of late GU toxicity in SCIMITAR, a Post-op SBRT trial. Clin Transl Radiat Oncol 2023; 39:100594. [PMID: 36880064 PMCID: PMC9984404 DOI: 10.1016/j.ctro.2023.100594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/02/2023] [Accepted: 02/05/2023] [Indexed: 02/10/2023] Open
Abstract
Predictors of genitourinary toxicity after post-prostatectomy radiotherapy remain elusive. A previously defined germline DNA signature (PROSTOX) has shown predictive ability for late grade ≥ 2 GU toxicity after intact prostate stereotactic body radiotherapy. We explore whether PROSTOX would predict toxicity among patients receiving post-prostatectomy SBRT on a phase II clinical trial.
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Affiliation(s)
- Amar U. Kishan
- University of California, Department of Radiation Oncology, Los Angeles, CA 90025, USA
| | - Nicholas Marco
- University of California, Department of Statistics, Los Angeles, CA 90025, USA
| | - Ting Martin Ma
- University of California, Department of Radiation Oncology, Los Angeles, CA 90025, USA
| | - Michael L. Steinberg
- University of California, Department of Radiation Oncology, Los Angeles, CA 90025, USA
| | - Ankush Sachdeva
- University of California, Institute of Urologic Oncology, Los Angeles, CA 90025, USA
| | - Minsong Cao
- University of California, Department of Radiation Oncology, Los Angeles, CA 90025, USA
| | - Leslie K. Ballas
- Cedars Sinai, Department of Radiation Oncology, Los Angeles, CA 90048, USA
| | - Emily Rietdorf
- University of California, Department of Radiation Oncology, Los Angeles, CA 90025, USA
| | - Donatello Telesca
- University of California, Department of Statistics, Los Angeles, CA 90025, USA
| | - Joanne B. Weidhaas
- University of California, Department of Radiation Oncology, Los Angeles, CA 90025, USA
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13
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Wei C, Xiang X, Zhou X, Ren S, Zhou Q, Dong W, Lin H, Wang S, Zhang Y, Lin H, He Q, Lu Y, Jiang X, Shuai J, Jin X, Xie C. Development and validation of an interpretable radiomic nomogram for severe radiation proctitis prediction in postoperative cervical cancer patients. Front Microbiol 2023; 13:1090770. [PMID: 36713206 PMCID: PMC9877536 DOI: 10.3389/fmicb.2022.1090770] [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: 11/06/2022] [Accepted: 12/28/2022] [Indexed: 01/13/2023] Open
Abstract
Background Radiation proctitis is a common complication after radiotherapy for cervical cancer. Unlike simple radiation damage to other organs, radiation proctitis is a complex disease closely related to the microbiota. However, analysis of the gut microbiota is time-consuming and expensive. This study aims to mine rectal information using radiomics and incorporate it into a nomogram model for cheap and fast prediction of severe radiation proctitis prediction in postoperative cervical cancer patients. Methods The severity of the patient's radiation proctitis was graded according to the RTOG/EORTC criteria. The toxicity grade of radiation proctitis over or equal to grade 2 was set as the model's target. A total of 178 patients with cervical cancer were divided into a training set (n = 124) and a validation set (n = 54). Multivariate logistic regression was used to build the radiomic and non-raidomic models. Results The radiomics model [AUC=0.6855(0.5174-0.8535)] showed better performance and more net benefit in the validation set than the non-radiomic model [AUC=0.6641(0.4904-0.8378)]. In particular, we applied SHapley Additive exPlanation (SHAP) method for the first time to a radiomics-based logistic regression model to further interpret the radiomic features from case-based and feature-based perspectives. The integrated radiomic model enables the first accurate quantitative assessment of the probability of radiation proctitis in postoperative cervical cancer patients, addressing the limitations of the current qualitative assessment of the plan through dose-volume parameters only. Conclusion We successfully developed and validated an integrated radiomic model containing rectal information. SHAP analysis of the model suggests that radiomic features have a supporting role in the quantitative assessment of the probability of radiation proctitis in postoperative cervical cancer patients.
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Affiliation(s)
- Chaoyi Wei
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Xinli Xiang
- The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Xiaobo Zhou
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Siyan Ren
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Qingyu Zhou
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Wenjun Dong
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Haizhen Lin
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Saijun Wang
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Yuyue Zhang
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China
| | - Hai Lin
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Qingzu He
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Yuer Lu
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Xiaoming Jiang
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Jianwei Shuai
- Wenzhou Key Laboratory of Biophysics, Wenzhou Institute, University of Chinese Academy of Sciences, Wenzhou, Zhejiang Province, China
| | - Xiance Jin
- Radiotherapy Center, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China,School of Basic Medical Sciences, Wenzhou Medical University, Wenzhou, Zhejiang Province, China,*Correspondence: Xiance Jin, ✉
| | - Congying Xie
- Medical and Radiation Oncology, The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang Province, China,Congying Xie, ✉
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14
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Bladder Cancer Radiation Oncology of the Future: Prognostic Modelling, Radiomics, and Treatment Planning With Artificial Intelligence. Semin Radiat Oncol 2023; 33:70-75. [PMID: 36517196 DOI: 10.1016/j.semradonc.2022.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Machine learning (ML) and artificial intelligence (AI) have demonstrated potential to improve the care of radiation oncology patients. Here we review recent advances applicable to the care of bladder cancer, with an eye towards studies that may suggest next steps in clinical implementation. Algorithms have been applied to clinical records, pathology, and radiology data to generate accurate predictive models for prognosis and clinical outcomes. AI has also shown increasing utility for auto-contouring and efficient creation of workflows involving multiple treatment plans. As technologies progress towards routine clinical use for bladder cancer patients, we also discuss emerging methods to improve interpretability and reliability of algorithms.
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15
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Niraula D, Cui S, Pakela J, Wei L, Luo Y, Ten Haken RK, El Naqa I. Current status and future developments in predicting outcomes in radiation oncology. Br J Radiol 2022; 95:20220239. [PMID: 35867841 PMCID: PMC9793488 DOI: 10.1259/bjr.20220239] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023] Open
Abstract
Advancements in data-driven technologies and the inclusion of information-rich multiomics features have significantly improved the performance of outcomes modeling in radiation oncology. For this current trend to be sustainable, challenges related to robust data modeling such as small sample size, low size to feature ratio, noisy data, as well as issues related to algorithmic modeling such as complexity, uncertainty, and interpretability, need to be mitigated if not resolved. Emerging computational technologies and new paradigms such as federated learning, human-in-the-loop, quantum computing, and novel interpretability methods show great potential in overcoming these challenges and bridging the gap towards precision outcome modeling in radiotherapy. Examples of these promising technologies will be presented and their potential role in improving outcome modeling will be discussed.
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Affiliation(s)
- Dipesh Niraula
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | - Sunan Cui
- Department of Radiation Oncology, Stanford Medicine, Stanford University, Stanford, USA
| | - Julia Pakela
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, USA
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, Ann Arbor, USA
| | - Yi Luo
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
| | | | - Issam El Naqa
- Department of Machine Learning, H Lee Moffitt Cancer Center and Research Institute, Tampa, USA
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16
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Artificial Intelligence for Outcome Modeling in Radiotherapy. Semin Radiat Oncol 2022; 32:351-364. [DOI: 10.1016/j.semradonc.2022.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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17
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Li G, Wu X, Ma X. Artificial intelligence in radiotherapy. Semin Cancer Biol 2022; 86:160-171. [PMID: 35998809 DOI: 10.1016/j.semcancer.2022.08.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Accepted: 08/18/2022] [Indexed: 11/19/2022]
Abstract
Radiotherapy is a discipline closely integrated with computer science. Artificial intelligence (AI) has developed rapidly over the past few years. With the explosive growth of medical big data, AI promises to revolutionize the field of radiotherapy through highly automated workflow, enhanced quality assurance, improved regional balances of expert experiences, and individualized treatment guided by multi-omics. In addition to independent researchers, the increasing number of large databases, biobanks, and open challenges significantly facilitated AI studies on radiation oncology. This article reviews the latest research, clinical applications, and challenges of AI in each part of radiotherapy including image processing, contouring, planning, quality assurance, motion management, and outcome prediction. By summarizing cutting-edge findings and challenges, we aim to inspire researchers to explore more future possibilities and accelerate the arrival of AI radiotherapy.
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Affiliation(s)
- Guangqi Li
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xin Wu
- Head & Neck Oncology ward, Division of Radiotherapy Oncology, Cancer Center, West China Hospital, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China
| | - Xuelei Ma
- Division of Biotherapy, Cancer Center, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, No. 37 GuoXue Alley, Chengdu 610041, China.
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18
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Wang J, Chen Y, Xie H, Luo L, Tang Q. Evaluation of auto-segmentation for EBRT planning structures using deep learning-based workflow on cervical cancer. Sci Rep 2022; 12:13650. [PMID: 35953516 PMCID: PMC9372087 DOI: 10.1038/s41598-022-18084-0] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 08/04/2022] [Indexed: 11/12/2022] Open
Abstract
Deep learning (DL) based approach aims to construct a full workflow solution for cervical cancer with external beam radiation therapy (EBRT) and brachytherapy (BT). The purpose of this study was to evaluate the accuracy of EBRT planning structures derived from DL based auto-segmentation compared with standard manual delineation. Auto-segmentation model based on convolutional neural networks (CNN) was developed to delineate clinical target volumes (CTVs) and organs at risk (OARs) in cervical cancer radiotherapy. A total of 300 retrospective patients from multiple cancer centers were used to train and validate the model, and 75 independent cases were selected as testing data. The accuracy of auto-segmented contours were evaluated using geometric and dosimetric metrics including dice similarity coefficient (DSC), 95% hausdorff distance (95%HD), jaccard coefficient (JC) and dose-volume index (DVI). The correlation between geometric metrics and dosimetric difference was performed by Spearman’s correlation analysis. The right and left kidney, bladder, right and left femoral head showed superior geometric accuracy (DSC: 0.88–0.93; 95%HD: 1.03 mm–2.96 mm; JC: 0.78–0.88), and the Bland–Altman test obtained dose agreement for these contours (P > 0.05) between manual and DL based methods. Wilcoxon’s signed-rank test indicated significant dosimetric differences in CTV, spinal cord and pelvic bone (P < 0.001). A strong correlation between the mean dose of pelvic bone and its 95%HD (R = 0.843, P < 0.001) was found in Spearman’s correlation analysis, and the remaining structures showed weak link between dosimetric difference and all of geometric metrics. Our auto-segmentation achieved a satisfied agreement for most EBRT planning structures, although the clinical acceptance of CTV was a concern. DL based auto-segmentation was an essential component in cervical cancer workflow which would generate the accurate contouring.
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Affiliation(s)
- Jiahao Wang
- Department of Radiation Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, Zhejiang, China
| | - Yuanyuan Chen
- Department of Radiation Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, Zhejiang, China
| | - Hongling Xie
- Department of Radiation Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, Zhejiang, China
| | - Lumeng Luo
- Department of Radiation Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, Zhejiang, China
| | - Qiu Tang
- Department of Radiation Oncology, Women's Hospital, School of Medicine, Zhejiang University, Hangzhou, 310006, Zhejiang, China.
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19
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Schack LMH, Naderi E, Fachal L, Dorling L, Luccarini C, Dunning AM, Ong EHW, Chua MLK, Langendijk JA, Alizadeh BZ, Overgaard J, Eriksen JG, Andreassen CN, Alsner J. A genome-wide association study of radiotherapy induced toxicity in head and neck cancer patients identifies a susceptibility locus associated with mucositis. Br J Cancer 2022; 126:1082-1090. [PMID: 35039627 PMCID: PMC8980077 DOI: 10.1038/s41416-021-01670-w] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Revised: 11/21/2021] [Accepted: 12/06/2021] [Indexed: 01/21/2023] Open
Abstract
PURPOSE A two-stage genome-wide association study was carried out in head and neck cancer (HNC) patients aiming to identify genetic variants associated with either specific radiotherapy-induced (RT) toxicity endpoints or a general proneness to develop toxicity after RT. MATERIALS AND METHODS The analysis included 1780 HNC patients treated with primary RT for laryngeal or oro/hypopharyngeal cancers. In a non-hypothesis-driven explorative discovery study, associations were tested in 1183 patients treated within The Danish Head and Neck Cancer Group. Significant associations were later tested in an independent Dutch cohort of 597 HNC patients and if replicated, summary data obtained from discovery and replication studies were meta-analysed. Further validation of significantly replicated findings was pursued in an Asian cohort of 235 HNC patients with nasopharynx as the primary tumour site. RESULTS We found and replicated a significant association between a locus on chromosome 5 and mucositis with a pooled OR for rs1131769*C in meta-analysis = 1.95 (95% CI 1.48-2.41; ppooled = 4.34 × 10-16). CONCLUSION This first exploratory GWAS in European cohorts of HNC patients identified and replicated a risk locus for mucositis. A larger Meta-GWAS to identify further risk variants for RT-induced toxicity in HNC patients is warranted.
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Affiliation(s)
- Line M H Schack
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark.
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark.
| | - Elnaz Naderi
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands
| | - Laura Fachal
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Leila Dorling
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Craig Luccarini
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Alison M Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK
| | - Enya H W Ong
- Division of Medical Sciences, National Cancer Centre Singapore, Singapore, Singapore
| | - Melvin L K Chua
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
- Oncology Academic Clinical Programme, Duke-NUS Medical School, Singapore, Singapore
| | - Johannes A Langendijk
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
| | - Behrooz Z Alizadeh
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, Groningen, The Netherlands
| | - Jens Overgaard
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Jesper Grau Eriksen
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Christian Nicolaj Andreassen
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
- Department of Oncology, Aarhus University Hospital, Aarhus, Denmark
| | - Jan Alsner
- Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark
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20
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Recent Applications of Artificial Intelligence in Radiotherapy: Where We Are and Beyond. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12073223] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
In recent decades, artificial intelligence (AI) tools have been applied in many medical fields, opening the possibility of finding novel solutions for managing very complex and multifactorial problems, such as those commonly encountered in radiotherapy (RT). We conducted a PubMed and Scopus search to identify the AI application field in RT limited to the last four years. In total, 1824 original papers were identified, and 921 were analyzed by considering the phase of the RT workflow according to the applied AI approaches. AI permits the processing of large quantities of information, data, and images stored in RT oncology information systems, a process that is not manageable for individuals or groups. AI allows the iterative application of complex tasks in large datasets (e.g., delineating normal tissues or finding optimal planning solutions) and might support the entire community working in the various sectors of RT, as summarized in this overview. AI-based tools are now on the roadmap for RT and have been applied to the entire workflow, mainly for segmentation, the generation of synthetic images, and outcome prediction. Several concerns were raised, including the need for harmonization while overcoming ethical, legal, and skill barriers.
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21
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Samami E, Pourali G, Arabpour M, Fanipakdel A, Shahidsales S, Javadinia SA, Hassanian SM, Mohammadparast S, Avan A. The Potential Diagnostic and Prognostic Value of Circulating MicroRNAs in the Assessment of Patients With Prostate Cancer: Rational and Progress. Front Oncol 2022; 11:716831. [PMID: 35186706 PMCID: PMC8855122 DOI: 10.3389/fonc.2021.716831] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2021] [Accepted: 12/31/2021] [Indexed: 12/20/2022] Open
Abstract
Prostate cancer (P.C.) is one of the most frequent diagnosed cancers among men and the first leading cause of death with an annual incidence of 1.4 million worldwide. Prostate-specific antigen is being used for screening/diagnosis of prostate disease, although it is associated with several limitations. Thus, identification of novel biomarkers is warranted for diagnosis of patients at earlier stages. MicroRNAs (miRNAs) are recently being emerged as potential biomarkers. It has been shown that these small molecules can be circulated in body fluids and prognosticate the risk of developing P.C. Several miRNAs, including MiR-20a, MiR-21, miR-375, miR-378, and miR-141, have been proposed to be expressed in prostate cancer. This review summarizes the current knowledge about possible molecular mechanisms and potential application of tissue specific and circulating microRNAs as diagnosis, prognosis, and therapeutic targets in prostate cancer.
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Affiliation(s)
- Elham Samami
- Network of Immunity in Infection, Malignancy and Autoimmunity (NIIMA), Universal Scientific Education and Research Network (USERN), Tehran University of Medical Sciences, Tehran, Iran
| | - Ghazaleh Pourali
- Cancer Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Mahla Arabpour
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Azar Fanipakdel
- Cancer Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | | | - Seyed Alireza Javadinia
- Vasei Clinical Research Development Unit, Sabzevar University of Medical Sciences, Sabzevar, Iran
| | - Seyed Mahdi Hassanian
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Saeid Mohammadparast
- Department of Cell, Developmental and Integrative Biology, University of Alabama at Birmingham, Birmingham, AL, United States
| | - Amir Avan
- Metabolic Syndrome Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
- Basic Medical Sciences Institute, Mashhad University of Medical Sciences, Mashhad, Iran
- *Correspondence: Amir Avan,
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22
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Kishan AU, Marco N, Schulz-Jaavall MB, Steinberg ML, Tran PT, Juarez JE, Dang A, Telesca D, Lilleby WA, Weidhaas JB. Germline variants disrupting microRNAs predict long-term genitourinary toxicity after prostate cancer radiation. Radiother Oncol 2022; 167:226-232. [PMID: 34990726 PMCID: PMC8979583 DOI: 10.1016/j.radonc.2021.12.040] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 12/23/2021] [Accepted: 12/28/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND AND PURPOSE The purpose of this study was to determine whether single nucleotide polymorphisms disrupting microRNA targets (mirSNPs) can serve as predictive biomarkers for toxicity after radiotherapy for prostate cancer and whether these may be differentially predictive depending on radiation fractionation. MATERIALS AND METHODS We identified 201 men treated with two forms of definitive radiotherapy for prostate cancer at two institutions: 108 men received conventionally-fractionated radiotherapy (CF-RT) and 93 received stereotactic body radiotherapy (SBRT). Germline DNA was evaluated for the presence of functional mirSNPs. Random forest, boosted trees and elastic net models were developed to predict late grade ≥2 GU toxicity by the RTOG scale. RESULTS The crude incidence of late grade ≥2 GU toxicity was 16% after CF-RT and 15% after SBRT. An elastic net model based on 22 mirSNPs differentiated CF-RT patients at high risk (71.5%) versus low risk (7.5%) for toxicity, with an area under the curve (AUC) values of 0.76-0.81. An elastic net model based on 32 mirSNPs differentiated SBRT patients at high risk (64.7%) versus low risk (3.9%) for toxicity, with an area under the curve (AUC) values of 0.81-0.87. These models were specific to treatment type delivered. Prospective studies are warranted to further validate these results. CONCLUSION Predictive models using germline mirSNPs have high accuracy for predicting late grade ≥2 GU toxicity after either CF-RT or SBRT, and are unique for each treatment, suggesting that germline predictors of late radiation sensitivity are fractionation-dependent. Prospective studies are warranted to further validate these results.
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Affiliation(s)
- Amar U. Kishan
- Department of Radiation Oncology, Los Angeles, United States,Department of Urology, University of California, Los Angeles, United States,Corresponding author at: Department of Radiation Oncology, Suite B265, 200 Medical Plaza, Los Angeles, CA 90095, United States. (A.U. Kishan)
| | - Nicholas Marco
- Department of Biostatistics, University of California Los Angeles Fielding School of Public Health, Los Angeles, United States
| | | | | | - Phuoc T. Tran
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, Baltimore, United States
| | - Jesus E. Juarez
- Department of Radiation Oncology, Los Angeles, United States
| | - Audrey Dang
- Department of Radiation Oncology, Los Angeles, United States
| | - Donatello Telesca
- Department of Biostatistics, University of California Los Angeles Fielding School of Public Health, Los Angeles, United States
| | - Wolfgang A. Lilleby
- Department of Oncology, Oslo University Hospital, The Norwegian Radium Hospital, Oslo, Norway
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Artificial Intelligence Applications in Urology: Reporting Standards to Achieve Fluency for Urologists. Urol Clin North Am 2022; 49:65-117. [PMID: 34776055 PMCID: PMC9147289 DOI: 10.1016/j.ucl.2021.07.009] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
The growth and adoption of artificial intelligence has led to impressive results in urology. As artificial intelligence grows more ubiquitous, it is important to establish artificial intelligence literacy in the workforce. To this end, we present a narrative review of the literature of artificial intelligence and machine learning in urology and propose a checklist of reporting standards to improve readability and evaluate the current state of the literature. The listed article demonstrated heterogeneous reporting of methodologies and outcomes, limiting generalizability of research. We hope that this review serves as a foundation for future evaluation of medical research in artificial intelligence.
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Aldraimli M, Osman S, Grishchuck D, Ingram S, Lyon R, Mistry A, Oliveira J, Samuel R, Shelley LE, Soria D, Dwek MV, Aguado-Barrera ME, Azria D, Chang-Claude J, Dunning A, Giraldo A, Green S, Gutiérrez-Enríquez S, Herskind C, van Hulle H, Lambrecht M, Lozza L, Rancati T, Reyes V, Rosenstein BS, de Ruysscher D, de Santis MC, Seibold P, Sperk E, Symonds RP, Stobart H, Taboada-Valadares B, Talbot CJ, Vakaet VJ, Vega A, Veldeman L, Veldwijk MR, Webb A, Weltens C, West CM, Chaussalet TJ, Rattay T. Development and optimisation of a machine-learning prediction model for acute desquamation following breast radiotherapy in the multi-centre REQUITE cohort. Adv Radiat Oncol 2022; 7:100890. [PMID: 35647396 PMCID: PMC9133391 DOI: 10.1016/j.adro.2021.100890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Accepted: 12/06/2021] [Indexed: 11/23/2022] Open
Abstract
Purpose Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) algorithms to develop and optimise a clinical prediction model for acute breast desquamation after whole breast external beam radiation therapy in the prospective multicenter REQUITE cohort study. Methods and Materials Using demographic and treatment-related features (m = 122) from patients (n = 2058) at 26 centers, we trained 8 ML algorithms with 10-fold cross-validation in a 50:50 random-split data set with class stratification to predict acute breast desquamation. Based on performance in the validation data set, the logistic model tree, random forest, and naïve Bayes models were taken forward to cost-sensitive learning optimisation. Results One hundred and ninety-two patients experienced acute desquamation. Resampling and cost-sensitive learning optimisation facilitated an improvement in classification performance. Based on maximising sensitivity (true positives), the “hero” model was the cost-sensitive random forest algorithm with a false-negative: false-positive misclassification penalty of 90:1 containing m = 114 predictive features. Model sensitivity and specificity were 0.77 and 0.66, respectively, with an area under the curve of 0.77 in the validation cohort. Conclusions ML algorithms with resampling and cost-sensitive learning generated clinically valid prediction models for acute desquamation using patient demographic and treatment features. Further external validation and inclusion of genomic markers in ML prediction models are worthwhile, to identify patients at increased risk of toxicity who may benefit from supportive intervention or even a change in treatment plan.
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Talwar V, Chufal KS, Joga S. Artificial Intelligence: A New Tool in Oncologist's Armamentarium. Indian J Med Paediatr Oncol 2021. [DOI: 10.1055/s-0041-1735577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022] Open
Abstract
AbstractArtificial intelligence (AI) has become an essential tool in human life because of its pivotal role in communications, transportation, media, and social networking. Inspired by the complex neuronal network and its functions in human beings, AI, using computer-based algorithms and training, had been explored since the 1950s. To tackle the enormous amount of patients' clinical data, imaging, histopathological data, and the increasing pace of research on new treatments and clinical trials, and ever-changing guidelines for treatment with the advent of novel drugs and evidence, AI is the need of the hour. There are numerous publications and active work on AI's role in the field of oncology. In this review, we discuss the fundamental terminology of AI, its applications in oncology on the whole, and its limitations. There is an inter-relationship between AI, machine learning and, deep learning. The virtual branch of AI deals with machine learning. While the physical branch of AI deals with the delivery of different forms of treatment—surgery, targeted drug delivery, and elderly care. The applications of AI in oncology include cancer screening, diagnosis (clinical, imaging, and histopathological), radiation therapy (image acquisition, tumor and organs at risk segmentation, image registration, planning, and delivery), prediction of treatment outcomes and toxicities, prediction of cancer cell sensitivity to therapeutics and clinical decision-making. A specific area of interest is in the development of effective drug combinations tailored to every patient and tumor with the help of AI. Radiomics, the new kid on the block, deals with the planning and administration of radiotherapy. As with any new invention, AI has its fallacies. The limitations include lack of external validation and proof of generalizability, difficulty in data access for rare diseases, ethical and legal issues, no precise logic behind the prediction, and last but not the least, lack of education and expertise among medical professionals. A collaboration between departments of clinical oncology, bioinformatics, and data sciences can help overcome these problems in the near future.
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Affiliation(s)
- Vineet Talwar
- Department of Medical Oncology, Rajiv Gandhi Cancer Institute & Research Centre, New Delhi, India
| | - Kundan Singh Chufal
- Department of Radiation Oncology, Rajiv Gandhi Cancer Institute & Research Centre, New Delhi, India
| | - Srujana Joga
- Department of Medical Oncology, Rajiv Gandhi Cancer Institute & Research Centre, New Delhi, India
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Petinrin OO, Li X, Wong KC. Particle Swarm Optimized Gaussian Process Classifier for Treatment Discontinuation Prediction in Multicohort Metastatic Castration-Resistant Prostate Cancer Patients. IEEE J Biomed Health Inform 2021; 26:1309-1317. [PMID: 34379600 DOI: 10.1109/jbhi.2021.3103989] [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: 11/10/2022]
Abstract
Prostate cancer is the second leading cancer in men, according to the WHO world cancer report. Its prevention and treatment demand proper attention. Despite numerous attempts for disease prevention, prostate tumours can still become metastatic by blood circulation to other organs. Several treatments have been adopted. However, findings show that the docetaxel treatment induces adverse reactions in patients. Particle Swarm Optimized Gaussian Process Classifier (PSO-GPC) is proposed to determine when to discontinue treatment. Based on three cohorts of prostate cancer patients, we propose and compare several classifiers for the best performance in determining treatment discontinuation. Given the data skewness and class imbalance, the models are evaluated based on both the area under receiver operating characteristics curve (AUC) and area under precision recall curve (AUPRC). With the AUCs ranging between 0.6717 - 0.8499, and AUPRCs ranging between 0.1392 - 0.5423, PSO-GPC performs better than the state-of-the-art. We have carried out statistical analysis for ranking methods and analyzed independent cohort data with PSO-GPC, demonstrating its unbiased performance. A proper determination of treatment discontinuation in metastatic castration-resistant prostate cancer patients will reduce the mortality rate in cancer patients.
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Akcay M, Etiz D, Celik O, Ozen A. Evaluation of acute hematological toxicity by machine learning in gynecologic cancers using postoperative radiotherapy. Indian J Cancer 2021; 59:178-186. [PMID: 34380848 DOI: 10.4103/ijc.ijc_666_19] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022]
Abstract
Background The aim of the study is to investigate the factors affecting acute hematologic toxicity (HT) in the adjuvant radiotherapy (RT) of gynecologic cancers by machine learning. Methods Between January 2015 and September 2018, 121 patients with endometrium and cervical cancer who underwent adjuvant RT with volumetric-modulated arc therapy (VMAT) were evaluated. The relationship between patient and treatment characteristics and acute HT was investigated using machine learning techniques, namely Logistic Regression, XGBoost, Artificial Neural Network, Random Forest, Naive Bayes, Support Vector Machine (SVM), and Gaussian Naive Bayes (GaussianNB) algorithms. Results No HT was observed in 11 cases (9.1%) and at least one grade of HT was observed in 110 cases. There were 55 (45.5%) cases with ≤grade 2 HT (mild HT) and 66 (54.5%) cases with grade ≥3 HT (severe HT). None of the patients developed grade 5 HT. Of 24 variables that could affect acute HT, nine were determined as important variables. According to the results, the best machine learning technique for acute HT estimation was SVM (accuracy 70%, area under curve (AUC): 0.65, sensitivity 71.4%, specificity 66.6%). Parameters affecting hematologic toxicity were evaluated also by classical statistical methods and there was a statistically significant relationship between age, RT, and bone marrow (BM) maximum dose. Conclusion It is important to predict the patients who will develop acute HT in order to minimize the side effects of treatment. If these cases can be identified in advance, toxicity rates can be reduced by taking necessary precautions. These cases can be predicted with machine learning algorithms.
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Affiliation(s)
- Melek Akcay
- Department of Radiation Oncology, Medical Faculty of Osmangazi University, Eskişehir, 26480, Turkey
| | - Durmus Etiz
- Department of Radiation Oncology, Medical Faculty of Osmangazi University, Eskişehir, 26480, Turkey
| | - Ozer Celik
- Department of Mathematics - Computer Eskisehir Osmangazi University, Eskişehir, 26480, Turkey
| | - Alaattin Ozen
- Department of Radiation Oncology, Medical Faculty of Osmangazi University, Eskişehir, 26480, Turkey
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Gajra A, Zettler ME, Miller KA, Blau S, Venkateshwaran SS, Sridharan S, Showalter J, Valley AW, Frownfelter JG. Augmented intelligence to predict 30-day mortality in patients with cancer. Future Oncol 2021; 17:3797-3807. [PMID: 34189965 DOI: 10.2217/fon-2021-0302] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Aim: An augmented intelligence tool to predict short-term mortality risk among patients with cancer could help identify those in need of actionable interventions or palliative care services. Patients & methods: An algorithm to predict 30-day mortality risk was developed using socioeconomic and clinical data from patients in a large community hematology/oncology practice. Patients were scored weekly; algorithm performance was assessed using dates of death in patients' electronic health records. Results: For patients scored as highest risk for 30-day mortality, the event rate was 4.9% (vs 0.7% in patients scored as low risk; a 7.4-times greater risk). Conclusion: The development and validation of a decision tool to accurately identify patients with cancer who are at risk for short-term mortality is feasible.
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Affiliation(s)
- Ajeet Gajra
- Cardinal Health Specialty Solutions, Dublin, OH 43017, USA
| | | | | | - Sibel Blau
- Rainier Hematology Oncology/Northwest Medical Specialties, Tacoma, WA 98405, USA
| | | | | | | | - Amy W Valley
- Cardinal Health Specialty Solutions, Dublin, OH 43017, USA
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Huang D, Bai H, Wang L, Hou Y, Li L, Xia Y, Yan Z, Chen W, Chang L, Li W. The Application and Development of Deep Learning in Radiotherapy: A Systematic Review. Technol Cancer Res Treat 2021; 20:15330338211016386. [PMID: 34142614 PMCID: PMC8216350 DOI: 10.1177/15330338211016386] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023] Open
Abstract
With the massive use of computers, the growth and explosion of data has greatly promoted the development of artificial intelligence (AI). The rise of deep learning (DL) algorithms, such as convolutional neural networks (CNN), has provided radiation oncologists with many promising tools that can simplify the complex radiotherapy process in the clinical work of radiation oncology, improve the accuracy and objectivity of diagnosis, and reduce the workload, thus enabling clinicians to spend more time on advanced decision-making tasks. As the development of DL gets closer to clinical practice, radiation oncologists will need to be more familiar with its principles to properly evaluate and use this powerful tool. In this paper, we explain the development and basic concepts of AI and discuss its application in radiation oncology based on different task categories of DL algorithms. This work clarifies the possibility of further development of DL in radiation oncology.
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Affiliation(s)
- Danju Huang
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Han Bai
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Li Wang
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Yu Hou
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Lan Li
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Yaoxiong Xia
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Zhirui Yan
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Wenrui Chen
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Li Chang
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
| | - Wenhui Li
- Department of Radiation Oncology, 531840The Third Affiliated Hospital of Kunming Medical University, Yunnan Cancer Hospital, Kunming, Yunnan, China
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30
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Chua IS, Gaziel-Yablowitz M, Korach ZT, Kehl KL, Levitan NA, Arriaga YE, Jackson GP, Bates DW, Hassett M. Artificial intelligence in oncology: Path to implementation. Cancer Med 2021; 10:4138-4149. [PMID: 33960708 PMCID: PMC8209596 DOI: 10.1002/cam4.3935] [Citation(s) in RCA: 37] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 04/06/2021] [Accepted: 04/07/2021] [Indexed: 12/21/2022] Open
Abstract
In recent years, the field of artificial intelligence (AI) in oncology has grown exponentially. AI solutions have been developed to tackle a variety of cancer‐related challenges. Medical institutions, hospital systems, and technology companies are developing AI tools aimed at supporting clinical decision making, increasing access to cancer care, and improving clinical efficiency while delivering safe, high‐value oncology care. AI in oncology has demonstrated accurate technical performance in image analysis, predictive analytics, and precision oncology delivery. Yet, adoption of AI tools is not widespread, and the impact of AI on patient outcomes remains uncertain. Major barriers for AI implementation in oncology include biased and heterogeneous data, data management and collection burdens, a lack of standardized research reporting, insufficient clinical validation, workflow and user‐design challenges, outdated regulatory and legal frameworks, and dynamic knowledge and data. Concrete actions that major stakeholders can take to overcome barriers to AI implementation in oncology include training and educating the oncology workforce in AI; standardizing data, model validation methods, and legal and safety regulations; funding and conducting future research; and developing, studying, and deploying AI tools through multidisciplinary collaboration.
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Affiliation(s)
- Isaac S Chua
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Department of Psychosocial Oncology and Palliative Care, Dana-Farber Cancer Institute, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Michal Gaziel-Yablowitz
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Zfania T Korach
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Kenneth L Kehl
- Harvard Medical School, Boston, MA, USA.,Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
| | | | | | - Gretchen P Jackson
- IBM Watson Health, Cambridge, MA, USA.,Department of Pediatric Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - David W Bates
- Division of General Internal Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Michael Hassett
- Harvard Medical School, Boston, MA, USA.,Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA.,Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
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Abstract
PURPOSE OF REVIEW This review aims to shed light on recent applications of artificial intelligence in urologic oncology. RECENT FINDINGS Artificial intelligence algorithms harness the wealth of patient data to assist in diagnosing, staging, treating, and monitoring genitourinary malignancies. Successful applications of artificial intelligence in urologic oncology include interpreting diagnostic imaging, pathology, and genomic annotations. Many of these algorithms, however, lack external validity and can only provide predictions based on one type of dataset. SUMMARY Future applications of artificial intelligence will need to incorporate several forms of data in order to truly make headway in urologic oncology. Researchers must actively ensure future artificial intelligence developments encompass the entire prospective patient population.
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Artificial Intelligence and Machine Learning in Prostate Cancer Patient Management-Current Trends and Future Perspectives. Diagnostics (Basel) 2021; 11:diagnostics11020354. [PMID: 33672608 PMCID: PMC7924061 DOI: 10.3390/diagnostics11020354] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2021] [Revised: 02/16/2021] [Accepted: 02/17/2021] [Indexed: 12/24/2022] Open
Abstract
Artificial intelligence (AI) is the field of computer science that aims to build smart devices performing tasks that currently require human intelligence. Through machine learning (ML), the deep learning (DL) model is teaching computers to learn by example, something that human beings are doing naturally. AI is revolutionizing healthcare. Digital pathology is becoming highly assisted by AI to help researchers in analyzing larger data sets and providing faster and more accurate diagnoses of prostate cancer lesions. When applied to diagnostic imaging, AI has shown excellent accuracy in the detection of prostate lesions as well as in the prediction of patient outcomes in terms of survival and treatment response. The enormous quantity of data coming from the prostate tumor genome requires fast, reliable and accurate computing power provided by machine learning algorithms. Radiotherapy is an essential part of the treatment of prostate cancer and it is often difficult to predict its toxicity for the patients. Artificial intelligence could have a future potential role in predicting how a patient will react to the therapy side effects. These technologies could provide doctors with better insights on how to plan radiotherapy treatment. The extension of the capabilities of surgical robots for more autonomous tasks will allow them to use information from the surgical field, recognize issues and implement the proper actions without the need for human intervention.
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Kang J, Thompson RF, Aneja S, Lehman C, Trister A, Zou J, Obcemea C, El Naqa I. National Cancer Institute Workshop on Artificial Intelligence in Radiation Oncology: Training the Next Generation. Pract Radiat Oncol 2021; 11:74-83. [PMID: 32544635 PMCID: PMC7293478 DOI: 10.1016/j.prro.2020.06.001] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 04/26/2020] [Accepted: 06/01/2020] [Indexed: 12/31/2022]
Abstract
PURPOSE Artificial intelligence (AI) is about to touch every aspect of radiation therapy, from consultation to treatment planning, quality assurance, therapy delivery, and outcomes modeling. There is an urgent need to train radiation oncologists and medical physicists in data science to help shepherd AI solutions into clinical practice. Poorly trained personnel may do more harm than good when attempting to apply rapidly developing and complex technologies. As the amount of AI research expands in our field, the radiation oncology community needs to discuss how to educate future generations in this area. METHODS AND MATERIALS The National Cancer Institute (NCI) Workshop on AI in Radiation Oncology (Shady Grove, MD, April 4-5, 2019) was the first of 2 data science workshops in radiation oncology hosted by the NCI in 2019. During this workshop, the Training and Education Working Group was formed by volunteers among the invited attendees. Its members represent radiation oncology, medical physics, radiology, computer science, industry, and the NCI. RESULTS In this perspective article written by members of the Training and Education Working Group, we provide and discuss action points relevant for future trainees interested in radiation oncology AI: (1) creating AI awareness and responsible conduct; (2) implementing a practical didactic curriculum; (3) creating a publicly available database of training resources; and (4) accelerating learning and funding opportunities. CONCLUSION Together, these action points can facilitate the translation of AI into clinical practice.
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Affiliation(s)
- John Kang
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, New York.
| | - Reid F Thompson
- Department of Radiation Medicine, Oregon Health & Science University, Portland, Oregon; VA Portland Healthcare System, Portland, Oregon
| | - Sanjay Aneja
- Department of Therapeutic Radiology, Yale University, New Haven, Connecticut
| | - Constance Lehman
- Department of Radiology, Harvard Medical School, Mass General Hospital, Boston, Massachusetts
| | | | - James Zou
- Department of Biomedical Data Science, Stanford University, Stanford, California; Chan Zuckerberg Biohub, San Francisco, California
| | - Ceferino Obcemea
- Radiation Research Program, National Cancer Institute, Bethesda, Maryland
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan
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Meng X, Li J, Zhang Q, Chen F, Bian C, Yao X, Yan J, Xu Z, Risacher SL, Saykin AJ, Liang H, Shen L. Multivariate genome wide association and network analysis of subcortical imaging phenotypes in Alzheimer's disease. BMC Genomics 2020; 21:896. [PMID: 33372590 PMCID: PMC7771059 DOI: 10.1186/s12864-020-07282-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 11/25/2020] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Genome-wide association studies (GWAS) have identified many individual genes associated with brain imaging quantitative traits (QTs) in Alzheimer's disease (AD). However single marker level association discovery may not be able to address the underlying biological interactions with disease mechanism. RESULTS In this paper, we used the MGAS (Multivariate Gene-based Association test by extended Simes procedure) tool to perform multivariate GWAS on eight AD-relevant subcortical imaging measures. We conducted multiple iPINBPA (integrative Protein-Interaction-Network-Based Pathway Analysis) network analyses on MGAS findings using protein-protein interaction (PPI) data, and identified five Consensus Modules (CMs) from the PPI network. Functional annotation and network analysis were performed on the identified CMs. The MGAS yielded significant hits within APOE, TOMM40 and APOC1 genes, which were known AD risk factors, as well as a few new genes such as LAMA1, XYLB, HSD17B7P2, and NPEPL1. The identified five CMs were enriched by biological processes related to disorders such as Alzheimer's disease, Legionellosis, Pertussis, and Serotonergic synapse. CONCLUSIONS The statistical power of coupling MGAS with iPINBPA was higher than traditional GWAS method, and yielded new findings that were missed by GWAS. This study provides novel insights into the molecular mechanism of Alzheimer's Disease and will be of value to novel gene discovery and functional genomic studies.
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Affiliation(s)
- Xianglian Meng
- School of Computer Information & Engineering, Changzhou Institute of Technology, Changzhou, 213032, China
| | - Jin Li
- College of Automation, Harbin Engineering University, Harbin, 150001, China
| | - Qiushi Zhang
- School of Computer Science, Northeast Electric Power University, Jilin, 132012, China
| | - Feng Chen
- College of Automation, Harbin Engineering University, Harbin, 150001, China
| | - Chenyuan Bian
- College of Automation, Harbin Engineering University, Harbin, 150001, China
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA
| | - Jingwen Yan
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
- Department of BioHealth Informatics, Indiana University School of Informatics and Computing, Indianapolis, IN, 46202, USA
| | - Zhe Xu
- School of Computer Information & Engineering, Changzhou Institute of Technology, Changzhou, 213032, China
| | - Shannon L Risacher
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Andrew J Saykin
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - Hong Liang
- College of Automation, Harbin Engineering University, Harbin, 150001, China.
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, 19104, USA.
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Oh JH, Tannenbaum A, Deasy JO. Identification of biological correlates associated with respiratory failure in COVID-19. BMC Med Genomics 2020; 13:186. [PMID: 33308225 PMCID: PMC7729705 DOI: 10.1186/s12920-020-00839-1] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Accepted: 11/29/2020] [Indexed: 12/11/2022] Open
Abstract
Background Coronavirus disease 2019 (COVID-19) is a global public health concern. Recently, a genome-wide association study (GWAS) was performed with participants recruited from Italy and Spain by an international consortium group.
Methods Summary GWAS statistics for 1610 patients with COVID-19 respiratory failure and 2205 controls were downloaded. In the current study, we analyzed the summary statistics with the information of loci and p-values for 8,582,968 single-nucleotide polymorphisms (SNPs), using gene ontology analysis to determine the top biological processes implicated in respiratory failure in COVID-19 patients. Results We considered the top 708 SNPs, using a p-value cutoff of 5 × 10− 5, which were mapped to the nearest genes, leading to 144 unique genes. The list of genes was input into a curated database to conduct gene ontology and protein-protein interaction (PPI) analyses. The top ranked biological processes were wound healing, epithelial structure maintenance, muscle system processes, and cardiac-relevant biological processes with a false discovery rate < 0.05. In the PPI analysis, the largest connected network consisted of 8 genes. Through a literature search, 7 out of the 8 gene products were found to be implicated in both pulmonary and cardiac diseases. Conclusion Gene ontology and PPI analyses identified cardio-pulmonary processes that may partially explain the risk of respiratory failure in COVID-19 patients.
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Affiliation(s)
- Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
| | - Allen Tannenbaum
- Departments of Computer Science and Applied Mathematics & Statistics, Stony Brook University, Stony Brook, NY, USA
| | - Joseph O Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA
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Benitez CM, Knox SJ. Harnessing genome-wide association studies to minimize adverse radiation-induced side effects. Radiat Oncol J 2020; 38:226-235. [PMID: 33233031 PMCID: PMC7785837 DOI: 10.3857/roj.2020.00556] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 09/22/2020] [Indexed: 12/19/2022] Open
Abstract
Radiotherapy is used as definitive treatment in approximately two-thirds of all cancers. However, like any treatment, radiation has significant acute and long-term side effects including secondary malignancies. Even when similar radiation parameters are used, 5%–10% of patients will experience adverse radiation side effects. Genomic susceptibility is thought to be responsible for approximately 40% of the clinical variability observed. In the era of precision medicine, the link between genetic susceptibility and radiation-induced side effects is further strengthening. Genome-wide association studies (GWAS) have begun to identify single-nucleotide polymorphisms (SNPs) attributed to overall and tissue-specific toxicity following radiation for treatment of breast cancer, prostate cancer, and other cancers. Here, we review the use of GWAS in identifying polymorphisms that are predictive of acute and long-term radiation-induced side effects with a focus on chest, pelvic, and head-and-neck irradiation. Integration of GWAS with “omic” data, patient characteristics, and clinical correlates into predictive models could decrease radiation-induced side effects while increasing therapeutic efficacy.
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Affiliation(s)
- Cecil M Benitez
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
| | - Susan J Knox
- Department of Radiation Oncology, Stanford University School of Medicine, Palo Alto, CA, USA
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Massi MC, Gasperoni F, Ieva F, Paganoni AM, Zunino P, Manzoni A, Franco NR, Veldeman L, Ost P, Fonteyne V, Talbot CJ, Rattay T, Webb A, Symonds PR, Johnson K, Lambrecht M, Haustermans K, De Meerleer G, de Ruysscher D, Vanneste B, Van Limbergen E, Choudhury A, Elliott RM, Sperk E, Herskind C, Veldwijk MR, Avuzzi B, Giandini T, Valdagni R, Cicchetti A, Azria D, Jacquet MPF, Rosenstein BS, Stock RG, Collado K, Vega A, Aguado-Barrera ME, Calvo P, Dunning AM, Fachal L, Kerns SL, Payne D, Chang-Claude J, Seibold P, West CML, Rancati T. A Deep Learning Approach Validates Genetic Risk Factors for Late Toxicity After Prostate Cancer Radiotherapy in a REQUITE Multi-National Cohort. Front Oncol 2020; 10:541281. [PMID: 33178576 PMCID: PMC7593843 DOI: 10.3389/fonc.2020.541281] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2020] [Accepted: 09/02/2020] [Indexed: 12/23/2022] Open
Abstract
Background: REQUITE (validating pREdictive models and biomarkers of radiotherapy toxicity to reduce side effects and improve QUalITy of lifE in cancer survivors) is an international prospective cohort study. The purpose of this project was to analyse a cohort of patients recruited into REQUITE using a deep learning algorithm to identify patient-specific features associated with the development of toxicity, and test the approach by attempting to validate previously published genetic risk factors. Methods: The study involved REQUITE prostate cancer patients treated with external beam radiotherapy who had complete 2-year follow-up. We used five separate late toxicity endpoints: ≥grade 1 late rectal bleeding, ≥grade 2 urinary frequency, ≥grade 1 haematuria, ≥ grade 2 nocturia, ≥ grade 1 decreased urinary stream. Forty-three single nucleotide polymorphisms (SNPs) already reported in the literature to be associated with the toxicity endpoints were included in the analysis. No SNP had been studied before in the REQUITE cohort. Deep Sparse AutoEncoders (DSAE) were trained to recognize features (SNPs) identifying patients with no toxicity and tested on a different independent mixed population including patients without and with toxicity. Results: One thousand, four hundred and one patients were included, and toxicity rates were: rectal bleeding 11.7%, urinary frequency 4%, haematuria 5.5%, nocturia 7.8%, decreased urinary stream 17.1%. Twenty-four of the 43 SNPs that were associated with the toxicity endpoints were validated as identifying patients with toxicity. Twenty of the 24 SNPs were associated with the same toxicity endpoint as reported in the literature: 9 SNPs for urinary symptoms and 11 SNPs for overall toxicity. The other 4 SNPs were associated with a different endpoint. Conclusion: Deep learning algorithms can validate SNPs associated with toxicity after radiotherapy for prostate cancer. The method should be studied further to identify polygenic SNP risk signatures for radiotherapy toxicity. The signatures could then be included in integrated normal tissue complication probability models and tested for their ability to personalize radiotherapy treatment planning.
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Affiliation(s)
- Michela Carlotta Massi
- Modelling and Scientific Computing Laboratory, Math Department, Politecnico di Milano, Milan, Italy
- Center for Analysis, Decisions and Society, Human Technopole, Milan, Italy
| | - Francesca Gasperoni
- Medical Research Council-Biostatistic Unit, University of Cambridge, Cambridge, United Kingdom
| | - Francesca Ieva
- Modelling and Scientific Computing Laboratory, Math Department, Politecnico di Milano, Milan, Italy
- Center for Analysis, Decisions and Society, Human Technopole, Milan, Italy
- CHRP-National Center for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy
| | - Anna Maria Paganoni
- Modelling and Scientific Computing Laboratory, Math Department, Politecnico di Milano, Milan, Italy
- Center for Analysis, Decisions and Society, Human Technopole, Milan, Italy
- CHRP-National Center for Healthcare Research and Pharmacoepidemiology, University of Milano-Bicocca, Milan, Italy
| | - Paolo Zunino
- Modelling and Scientific Computing Laboratory, Math Department, Politecnico di Milano, Milan, Italy
| | - Andrea Manzoni
- Modelling and Scientific Computing Laboratory, Math Department, Politecnico di Milano, Milan, Italy
| | - Nicola Rares Franco
- Modelling and Scientific Computing Laboratory, Math Department, Politecnico di Milano, Milan, Italy
| | - Liv Veldeman
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
- Department of Radiation Oncology, Ghent University Hospital, Ghent, Belgium
| | - Piet Ost
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
- Department of Radiation Oncology, Ghent University Hospital, Ghent, Belgium
| | - Valérie Fonteyne
- Department of Human Structure and Repair, Ghent University, Ghent, Belgium
- Department of Radiation Oncology, Ghent University Hospital, Ghent, Belgium
| | - Christopher J. Talbot
- Leicester Cancer Research Centre, Department of Genetics and Genome Biology, University of Leicester, Leicester, United Kingdom
| | - Tim Rattay
- Leicester Cancer Research Centre, Department of Genetics and Genome Biology, University of Leicester, Leicester, United Kingdom
| | - Adam Webb
- Leicester Cancer Research Centre, Department of Genetics and Genome Biology, University of Leicester, Leicester, United Kingdom
| | - Paul R. Symonds
- Leicester Cancer Research Centre, Department of Genetics and Genome Biology, University of Leicester, Leicester, United Kingdom
| | - Kerstie Johnson
- Leicester Cancer Research Centre, Department of Genetics and Genome Biology, University of Leicester, Leicester, United Kingdom
| | - Maarten Lambrecht
- Department of Radiation Oncology, University Hospitals Leuven, Leuven, Belgium
| | - Karin Haustermans
- Department of Radiation Oncology, University Hospitals Leuven, Leuven, Belgium
| | - Gert De Meerleer
- Department of Radiation Oncology, University Hospitals Leuven, Leuven, Belgium
| | - Dirk de Ruysscher
- Maastricht University Medical Center, Maastricht, Netherlands
- Department of Radiation Oncology (Maastro), GROW Institute for Oncology and Developmental Biology, Maastricht, Netherlands
| | - Ben Vanneste
- Department of Radiation Oncology (Maastro), GROW Institute for Oncology and Developmental Biology, Maastricht, Netherlands
| | - Evert Van Limbergen
- Maastricht University Medical Center, Maastricht, Netherlands
- Department of Radiation Oncology (Maastro), GROW Institute for Oncology and Developmental Biology, Maastricht, Netherlands
| | - Ananya Choudhury
- Translational Radiobiology Group, Division of Cancer Sciences, Manchester Academic Health Science Centre, Christie Hospital, University of Manchester, Manchester, United Kingdom
| | - Rebecca M. Elliott
- Translational Radiobiology Group, Division of Cancer Sciences, Manchester Academic Health Science Centre, Christie Hospital, University of Manchester, Manchester, United Kingdom
| | - Elena Sperk
- Department of Radiation Oncology, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Carsten Herskind
- Department of Radiation Oncology, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Marlon R. Veldwijk
- Department of Radiation Oncology, Universitätsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Barbara Avuzzi
- Department of Radiation Oncology 1, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Tommaso Giandini
- Department of Medical Physics, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Riccardo Valdagni
- Department of Radiation Oncology 1, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
- Department of Oncology and Haemato-Oncology, University of Milan, Milan, Italy
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - Alessandro Cicchetti
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
| | - David Azria
- Department of Radiation Oncology, University Federation of Radiation Oncology, Montpellier Cancer Institute, Univ Montpellier MUSE, Grant INCa_Inserm_DGOS_12553, Inserm U1194, Montpellier, France
| | - Marie-Pierre Farcy Jacquet
- Department of Radiation Oncology, University Federation of Radiation Oncology, CHU Caremeau, Nîmes, France
| | - Barry S. Rosenstein
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Richard G. Stock
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Kayla Collado
- Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, United States
| | - Ana Vega
- Fundación Pública Galega de Medicina Xenómica, Grupo de Medicina Xenómica (USC), Santiago de Compostela, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela, Santiago de Compostela, Spain
- Biomedical Network on Rare Diseases (CIBERER), Madrid, Spain
| | - Miguel Elías Aguado-Barrera
- Fundación Pública Galega de Medicina Xenómica, Grupo de Medicina Xenómica (USC), Santiago de Compostela, Spain
- Instituto de Investigación Sanitaria de Santiago de Compostela, Santiago de Compostela, Spain
| | - Patricia Calvo
- Instituto de Investigación Sanitaria de Santiago de Compostela, Santiago de Compostela, Spain
- Department of Radiation Oncology, Complexo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain
| | - Alison M. Dunning
- Strangeways Research Labs, Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
| | - Laura Fachal
- Strangeways Research Labs, Department of Oncology, Centre for Cancer Genetic Epidemiology, University of Cambridge, Cambridge, United Kingdom
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, United Kingdom
| | - Sarah L. Kerns
- Departments of Radiation Oncology and Surgery, University of Rochester Medical Center, Rochester, New York, NY, United States
| | - Debbie Payne
- Centre for Integrated Genomic Medical Research (CIGMR), University of Manchester, Manchester, United Kingdom
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
- University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Petra Seibold
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Catharine M. L. West
- Translational Radiobiology Group, Division of Cancer Sciences, Manchester Academic Health Science Centre, Christie Hospital, University of Manchester, Manchester, United Kingdom
| | - Tiziana Rancati
- Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
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Desideri I, Loi M, Francolini G, Becherini C, Livi L, Bonomo P. Application of Radiomics for the Prediction of Radiation-Induced Toxicity in the IMRT Era: Current State-of-the-Art. Front Oncol 2020; 10:1708. [PMID: 33117669 PMCID: PMC7574641 DOI: 10.3389/fonc.2020.01708] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2020] [Accepted: 07/30/2020] [Indexed: 12/14/2022] Open
Abstract
Normal tissue complication probability (NTCP) models that were formulated in the Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) are one of the pillars in support of everyday’s clinical radiation oncology. Because of steady therapeutic refinements and the availability of cutting-edge technical solutions, the ceiling of organs-at-risk-sparing has been reached for photon-based intensity modulated radiotherapy (IMRT). The possibility to capture heterogeneity of patients and tissues in the prediction of toxicity is still an unmet need in modern radiation therapy. Potentially, a major step towards a wider therapeutic index could be obtained from refined assessment of radiation-induced morbidity at an individual level. The rising integration of quantitative imaging and machine learning applications into radiation oncology workflow offers an unprecedented opportunity to further explore the biologic interplay underlying the normal tissue response to radiation. Based on these premises, in this review we focused on the current-state-of-the-art on the use of radiomics for the prediction of toxicity in the field of head and neck, lung, breast and prostate radiotherapy.
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Affiliation(s)
- Isacco Desideri
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
| | - Mauro Loi
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
| | - Giulio Francolini
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
| | - Carlotta Becherini
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
| | - Lorenzo Livi
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
| | - Pierluigi Bonomo
- Radiation Oncology, Azienda Ospedaliero-Universitaria Careggi, University of Florence, Florence, Italy
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Lee S, Deasy JO, Oh JH, Di Meglio A, Dumas A, Menvielle G, Charles C, Boyault S, Rousseau M, Besse C, Thomas E, Boland A, Cottu P, Tredan O, Levy C, Martin AL, Everhard S, Ganz PA, Partridge AH, Michiels S, Deleuze JF, Andre F, Vaz-Luis I. Prediction of Breast Cancer Treatment-Induced Fatigue by Machine Learning Using Genome-Wide Association Data. JNCI Cancer Spectr 2020. [PMID: 33490863 DOI: 10.1093/jncics/pkaa039/5835872] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/04/2023] Open
Abstract
BACKGROUND We aimed at predicting fatigue after breast cancer treatment using machine learning on clinical covariates and germline genome-wide data. METHODS We accessed germline genome-wide data of 2799 early-stage breast cancer patients from the Cancer Toxicity study (NCT01993498). The primary endpoint was defined as scoring zero at diagnosis and higher than quartile 3 at 1 year after primary treatment completion on European Organization for Research and Treatment of Cancer quality-of-life questionnaires for Overall Fatigue and on the multidimensional questionnaire for Physical, Emotional, and Cognitive fatigue. First, we tested univariate associations of each endpoint with clinical variables and genome-wide variants. Then, using preselected clinical (false discovery rate < 0.05) and genomic (P < .001) variables, a multivariable preconditioned random-forest regression model was built and validated on a hold-out subset to predict fatigue. Gene set enrichment analysis identified key biological correlates (MetaCore). All statistical tests were 2-sided. RESULTS Statistically significant clinical associations were found only with Emotional and Cognitive Fatigue, including receipt of chemotherapy, anxiety, and pain. Some single nucleotide polymorphisms had some degree of association (P < .001) with the different fatigue endpoints, although there were no genome-wide statistically significant (P < 5.00 × 10-8) associations. Only for Cognitive Fatigue, the predictive ability of the genomic multivariable model was statistically significantly better than random (area under the curve = 0.59, P = .01) and marginally improved with clinical variables (area under the curve = 0.60, P = .005). Single nucleotide polymorphisms found to be associated (P < .001) with Cognitive Fatigue belonged to genes linked to inflammation (false discovery rate adjusted P = .03), cognitive disorders (P = 1.51 × 10-12), and synaptic transmission (P = 6.28 × 10-8). CONCLUSIONS Genomic analyses in this large cohort of breast cancer survivors suggest a possible genetic role for severe Cognitive Fatigue that warrants further exploration.
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Affiliation(s)
- Sangkyu Lee
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Gustave Roussy, INSERM Unit 981, Villejuif, France
| | - Joseph O Deasy
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jung Hun Oh
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Agnes Dumas
- Gustave Roussy, INSERM Unit 1018, Villejuif, France
| | - Gwenn Menvielle
- INSERM, Institut Pierre Louis d'Epidémiologie et de Santé Publique, Sorbonne Université, Paris, France
| | | | | | | | - Celine Besse
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
- Fondation Synergie Lyon Cancer, Lyon, France
| | | | - Anne Boland
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
- Fondation Synergie Lyon Cancer, Lyon, France
| | - Paul Cottu
- Département d'Oncologie Médicale, Institut Curie, Paris, France
| | | | - Christelle Levy
- Department of Medical Oncology, Centre François Baclesse, Caen, France
| | | | | | | | | | | | - Jean-François Deleuze
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
- Fondation Synergie Lyon Cancer, Lyon, France
- Centre d' Etude du Polymorphisme Humain, The Laboratory of Excellence in Medical Genomics (LabEx GenMed), Paris, France
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40
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Lee S, Deasy JO, Oh JH, Di Meglio A, Dumas A, Menvielle G, Charles C, Boyault S, Rousseau M, Besse C, Thomas E, Boland A, Cottu P, Tredan O, Levy C, Martin AL, Everhard S, Ganz PA, Partridge AH, Michiels S, Deleuze JF, Andre F, Vaz-Luis I. Prediction of Breast Cancer Treatment-Induced Fatigue by Machine Learning Using Genome-Wide Association Data. JNCI Cancer Spectr 2020; 4:pkaa039. [PMID: 33490863 PMCID: PMC7583150 DOI: 10.1093/jncics/pkaa039] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2019] [Revised: 03/23/2020] [Accepted: 05/22/2020] [Indexed: 01/14/2023] Open
Abstract
BACKGROUND We aimed at predicting fatigue after breast cancer treatment using machine learning on clinical covariates and germline genome-wide data. METHODS We accessed germline genome-wide data of 2799 early-stage breast cancer patients from the Cancer Toxicity study (NCT01993498). The primary endpoint was defined as scoring zero at diagnosis and higher than quartile 3 at 1 year after primary treatment completion on European Organization for Research and Treatment of Cancer quality-of-life questionnaires for Overall Fatigue and on the multidimensional questionnaire for Physical, Emotional, and Cognitive fatigue. First, we tested univariate associations of each endpoint with clinical variables and genome-wide variants. Then, using preselected clinical (false discovery rate < 0.05) and genomic (P < .001) variables, a multivariable preconditioned random-forest regression model was built and validated on a hold-out subset to predict fatigue. Gene set enrichment analysis identified key biological correlates (MetaCore). All statistical tests were 2-sided. RESULTS Statistically significant clinical associations were found only with Emotional and Cognitive Fatigue, including receipt of chemotherapy, anxiety, and pain. Some single nucleotide polymorphisms had some degree of association (P < .001) with the different fatigue endpoints, although there were no genome-wide statistically significant (P < 5.00 × 10-8) associations. Only for Cognitive Fatigue, the predictive ability of the genomic multivariable model was statistically significantly better than random (area under the curve = 0.59, P = .01) and marginally improved with clinical variables (area under the curve = 0.60, P = .005). Single nucleotide polymorphisms found to be associated (P < .001) with Cognitive Fatigue belonged to genes linked to inflammation (false discovery rate adjusted P = .03), cognitive disorders (P = 1.51 × 10-12), and synaptic transmission (P = 6.28 × 10-8). CONCLUSIONS Genomic analyses in this large cohort of breast cancer survivors suggest a possible genetic role for severe Cognitive Fatigue that warrants further exploration.
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Affiliation(s)
- Sangkyu Lee
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
- Gustave Roussy, INSERM Unit 981, Villejuif, France
| | - Joseph O Deasy
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Jung Hun Oh
- Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | | | - Agnes Dumas
- Gustave Roussy, INSERM Unit 1018, Villejuif, France
| | - Gwenn Menvielle
- INSERM, Institut Pierre Louis d’Epidémiologie et de Santé Publique, Sorbonne Université, Paris, France
| | | | | | | | - Celine Besse
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
- Fondation Synergie Lyon Cancer, Lyon, France
| | | | - Anne Boland
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
- Fondation Synergie Lyon Cancer, Lyon, France
| | - Paul Cottu
- Département d'Oncologie Médicale, Institut Curie, Paris, France
| | | | - Christelle Levy
- Department of Medical Oncology, Centre François Baclesse, Caen, France
| | | | | | | | | | | | - Jean-François Deleuze
- Centre National de Recherche en Génomique Humaine (CNRGH), Institut de Biologie François Jacob, CEA, Université Paris-Saclay, Evry, France
- Fondation Synergie Lyon Cancer, Lyon, France
- Centre d' Etude du Polymorphisme Humain, The Laboratory of Excellence in Medical Genomics (LabEx GenMed), Paris, France
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The Application of Artificial Intelligence in Prostate Cancer Management—What Improvements Can Be Expected? A Systematic Review. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10186428] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Artificial Intelligence (AI) is progressively remodeling our daily life. A large amount of information from “big data” now enables machines to perform predictions and improve our healthcare system. AI has the potential to reshape prostate cancer (PCa) management thanks to growing applications in the field. The purpose of this review is to provide a global overview of AI in PCa for urologists, pathologists, radiotherapists, and oncologists to consider future changes in their daily practice. A systematic review was performed, based on PubMed MEDLINE, Google Scholar, and DBLP databases for original studies published in English from January 2009 to January 2019 relevant to PCa, AI, Machine Learning, Artificial Neural Networks, Convolutional Neural Networks, and Natural-Language Processing. Only articles with full text accessible were considered. A total of 1008 articles were reviewed, and 48 articles were included. AI has potential applications in all fields of PCa management: analysis of genetic predispositions, diagnosis in imaging, and pathology to detect PCa or to differentiate between significant and non-significant PCa. AI also applies to PCa treatment, whether surgical intervention or radiotherapy, skills training, or assessment, to improve treatment modalities and outcome prediction. AI in PCa management has the potential to provide a useful role by predicting PCa more accurately, using a multiomic approach and risk-stratifying patients to provide personalized medicine.
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Abstract
Artificial intelligence (AI) has the potential to fundamentally alter the way medicine is practised. AI platforms excel in recognizing complex patterns in medical data and provide a quantitative, rather than purely qualitative, assessment of clinical conditions. Accordingly, AI could have particularly transformative applications in radiation oncology given the multifaceted and highly technical nature of this field of medicine with a heavy reliance on digital data processing and computer software. Indeed, AI has the potential to improve the accuracy, precision, efficiency and overall quality of radiation therapy for patients with cancer. In this Perspective, we first provide a general description of AI methods, followed by a high-level overview of the radiation therapy workflow with discussion of the implications that AI is likely to have on each step of this process. Finally, we describe the challenges associated with the clinical development and implementation of AI platforms in radiation oncology and provide our perspective on how these platforms might change the roles of radiotherapy medical professionals.
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Ford CT, Janies D. Ensemble machine learning modeling for the prediction of artemisinin resistance in malaria. F1000Res 2020; 9:62. [PMID: 35903243 PMCID: PMC9274019 DOI: 10.12688/f1000research.21539.5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 06/23/2020] [Indexed: 11/20/2022] Open
Abstract
Resistance in malaria is a growing concern affecting many areas of Sub-Saharan Africa and Southeast Asia. Since the emergence of artemisinin resistance in the late 2000s in Cambodia, research into the underlying mechanisms has been underway. The 2019 Malaria Challenge posited the task of developing computational models that address important problems in advancing the fight against malaria. The first goal was to accurately predict artemisinin drug resistance levels of Plasmodium falciparum isolates, as quantified by the IC50. The second goal was to predict the parasite clearance rate of malaria parasite isolates based on in vitro transcriptional profiles. In this work, we develop machine learning models using novel methods for transforming isolate data and handling the tens of thousands of variables that result from these data transformation exercises. This is demonstrated by using massively parallel processing of the data vectorization for use in scalable machine learning. In addition, we show the utility of ensemble machine learning modeling for highly effective predictions of both goals of this challenge. This is demonstrated by the use of multiple machine learning algorithms combined with various scaling and normalization preprocessing steps. Then, using a voting ensemble, multiple models are combined to generate a final model prediction.
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Affiliation(s)
- Colby T. Ford
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, 28223, USA
- School of Data Science, University of North Carolina at Charlotte, Charlotte, North Carolina, 28223, USA
| | - Daniel Janies
- Department of Bioinformatics and Genomics, University of North Carolina at Charlotte, Charlotte, North Carolina, 28223, USA
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Thor M, Oh JH, Apte AP, Deasy JO. Registering Study Analysis Plans (SAPs) Before Dissecting Your Data—Updating and Standardizing Outcome Modeling. Front Oncol 2020; 10:978. [PMID: 32670879 PMCID: PMC7327097 DOI: 10.3389/fonc.2020.00978] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2020] [Accepted: 05/18/2020] [Indexed: 12/13/2022] Open
Abstract
Public preregistration of study analysis plans (SAPs) is widely recognized for clinical trials, but adopted to a much lesser extent in observational studies. Registration of SAPs prior to analysis is encouraged to not only increase transparency and exactness but also to avoid positive finding bias and better standardize outcome modeling. Efforts to generally standardize outcome modeling, which can be based on clinical trial and/or observational data, have recently spurred. We suggest a three-step SAP concept in which investigators are encouraged to (1) Design the SAP and circulate it among the co-investigators, (2) Log the SAP with a public repository, which recognizes the SAP with a digital object identifier (DOI), and (3) Cite (using the DOI), briefly summarize and motivate any deviations from the SAP in the associated manuscript. More specifically, the SAP should include the scope (brief data and study description, co-investigators, hypotheses, primary outcome measure, study title), in addition to step-by-step details of the analysis (handling of missing data, resampling, defined significance level, statistical function, validation, and variables and parameterization).
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Valle LF, Ruan D, Dang A, Levin-Epstein RG, Patel AP, Weidhaas JB, Nickols NG, Lee PP, Low DA, Qi XS, King CR, Steinberg ML, Kupelian PA, Cao M, Kishan AU. Development and Validation of a Comprehensive Multivariate Dosimetric Model for Predicting Late Genitourinary Toxicity Following Prostate Cancer Stereotactic Body Radiotherapy. Front Oncol 2020; 10:786. [PMID: 32509582 PMCID: PMC7251156 DOI: 10.3389/fonc.2020.00786] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2019] [Accepted: 04/22/2020] [Indexed: 12/31/2022] Open
Abstract
Purpose: Dosimetric predictors of toxicity after Stereotactic Body Radiation Therapy (SBRT) are not well-established. We sought to develop a multivariate model that predicts Common Terminology Criteria for Adverse Events (CTCAE) late grade 2 or greater genitourinary (GU) toxicity by interrogating the entire dose-volume histogram (DVH) from a large cohort of prostate cancer patients treated with SBRT on prospective trials. Methods: Three hundred and thirty-nine patients with late CTCAE toxicity data treated with prostate SBRT were identified and analyzed. All patients received 40 Gy in five fractions, every other day, using volumetric modulated arc therapy. For each patient, we examined 910 candidate dosimetric features including maximum dose, volumes of each organ [CTV, organs at risk (OARs)], V100%, and other granular volumetric/dosimetric indices at varying volumetric/dosimetric values from the entire DVH as well as ADT use to model and predict toxicity from SBRT. Training and validation subsets were generated with 90 and 10% of the patients in our cohort, respectively. Predictive accuracy was assessed by calculating the area under the receiver operating curve (AROC). Univariate analysis with student t-test was first performed on each candidate DVH feature. We subsequently performed advanced machine-learning multivariate analyses including classification and regression tree (CART), random forest, boosted tree, and multilayer neural network. Results: Median follow-up time was 32.3 months (range 3–98.9 months). Late grade ≥2 GU toxicity occurred in 20.1% of patients in our series. No single dosimetric parameter had an AROC for predicting late grade ≥2 GU toxicity on univariate analysis that exceeded 0.599. Optimized CART modestly improved prediction accuracy, with an AROC of 0.601, whereas other machine learning approaches did not improve upon univariate analyses. Conclusions: CART-based machine learning multivariate analyses drawing from 910 dosimetric features and ADT use modestly improves upon clinical prediction of late GU toxicity alone, yielding an AROC of 0.601. Biologic predictors may enhance predictive models for identifying patients at risk for late toxicity after SBRT.
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Affiliation(s)
- Luca F Valle
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Dan Ruan
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Audrey Dang
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Rebecca G Levin-Epstein
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Ankur P Patel
- David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, United States
| | - Joanne B Weidhaas
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Nicholas G Nickols
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Percy P Lee
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Daniel A Low
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - X Sharon Qi
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Christopher R King
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Michael L Steinberg
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Patrick A Kupelian
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Minsong Cao
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
| | - Amar U Kishan
- Department of Radiation Oncology, University of California, Los Angeles, Los Angeles, CA, United States
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46
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Isaksson LJ, Pepa M, Zaffaroni M, Marvaso G, Alterio D, Volpe S, Corrao G, Augugliaro M, Starzyńska A, Leonardi MC, Orecchia R, Jereczek-Fossa BA. Machine Learning-Based Models for Prediction of Toxicity Outcomes in Radiotherapy. Front Oncol 2020; 10:790. [PMID: 32582539 PMCID: PMC7289968 DOI: 10.3389/fonc.2020.00790] [Citation(s) in RCA: 51] [Impact Index Per Article: 12.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Accepted: 04/22/2020] [Indexed: 12/20/2022] Open
Abstract
In order to limit radiotherapy (RT)-related side effects, effective toxicity prediction and assessment schemes are essential. In recent years, the growing interest toward artificial intelligence and machine learning (ML) within the science community has led to the implementation of innovative tools in RT. Several researchers have demonstrated the high performance of ML-based models in predicting toxicity, but the application of these approaches in clinics is still lagging, partly due to their low interpretability. Therefore, an overview of contemporary research is needed in order to familiarize practitioners with common methods and strategies. Here, we present a review of ML-based models for predicting and classifying RT-induced complications from both a methodological and a clinical standpoint, focusing on the type of features considered, the ML methods used, and the main results achieved. Our work overviews published research in multiple cancer sites, including brain, breast, esophagus, gynecological, head and neck, liver, lung, and prostate cancers. The aim is to define the current state of the art and main achievements within the field for both researchers and clinicians.
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Affiliation(s)
- Lars J Isaksson
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Matteo Pepa
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Mattia Zaffaroni
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Giulia Marvaso
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Daniela Alterio
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Stefania Volpe
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Giulia Corrao
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Matteo Augugliaro
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Anna Starzyńska
- Department of Oral Surgery, Medical University of Gdańsk, Gdańsk, Poland
| | - Maria C Leonardi
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Roberto Orecchia
- Scientific Directorate, IEO European Institute of Oncology IRCCS, Milan, Italy
| | - Barbara A Jereczek-Fossa
- Division of Radiotherapy, IEO European Institute of Oncology IRCCS, Milan, Italy.,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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47
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Kang J, Coates JT, Strawderman RL, Rosenstein BS, Kerns SL. Genomics models in radiotherapy: From mechanistic to machine learning. Med Phys 2020; 47:e203-e217. [PMID: 32418335 PMCID: PMC8725063 DOI: 10.1002/mp.13751] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 06/28/2019] [Accepted: 07/17/2019] [Indexed: 12/28/2022] Open
Abstract
Machine learning (ML) provides a broad framework for addressing high-dimensional prediction problems in classification and regression. While ML is often applied for imaging problems in medical physics, there are many efforts to apply these principles to biological data toward questions of radiation biology. Here, we provide a review of radiogenomics modeling frameworks and efforts toward genomically guided radiotherapy. We first discuss medical oncology efforts to develop precision biomarkers. We next discuss similar efforts to create clinical assays for normal tissue or tumor radiosensitivity. We then discuss modeling frameworks for radiosensitivity and the evolution of ML to create predictive models for radiogenomics.
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Affiliation(s)
- John Kang
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY 14642, USA
| | - James T. Coates
- CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, Oxford OX3 7DQ, UK
| | - Robert L. Strawderman
- Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY 14642, USA
| | - Barry S. Rosenstein
- Department of Radiation Oncology and the Department of Genetics and Genomic Sciences, Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Sarah L. Kerns
- Department of Radiation Oncology, University of Rochester Medical Center, Rochester, NY 14642, USA
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Romero-Rosales BL, Tamez-Pena JG, Nicolini H, Moreno-Treviño MG, Trevino V. Improving predictive models for Alzheimer's disease using GWAS data by incorporating misclassified samples modeling. PLoS One 2020; 15:e0232103. [PMID: 32324812 PMCID: PMC7179850 DOI: 10.1371/journal.pone.0232103] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Accepted: 04/07/2020] [Indexed: 01/14/2023] Open
Abstract
Late-onset Alzheimer’s Disease (LOAD) is the most common form of dementia in the elderly. Genome-wide association studies (GWAS) for LOAD have open new avenues to identify genetic causes and to provide diagnostic tools for early detection. Although several predictive models have been proposed using the few detected GWAS markers, there is still a need for improvement and identification of potential markers. Commonly, polygenic risk scores are being used for prediction. Nevertheless, other methods to generate predictive models have been suggested. In this research, we compared three machine learning methods that have been proved to construct powerful predictive models (genetic algorithms, LASSO, and step-wise) and propose the inclusion of markers from misclassified samples to improve overall prediction accuracy. Our results show that the addition of markers from an initial model plus the markers of the model fitted to misclassified samples improves the area under the receiving operative curve by around 5%, reaching ~0.84, which is highly competitive using only genetic information. The computational strategy used here can help to devise better methods to improve classification models for AD. Our results could have a positive impact on the early diagnosis of Alzheimer’s disease.
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Affiliation(s)
| | - Jose-Gerardo Tamez-Pena
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo Leon, Mexico
| | - Humberto Nicolini
- Genomics of Psychiatric and Neurodegenerative Diseases Laboratory, National Institute of Genomic Medicine (INMEGEN), Mexico City, Mexico
| | | | - Victor Trevino
- Tecnologico de Monterrey, Escuela de Medicina y Ciencias de la Salud, Monterrey, Nuevo Leon, Mexico
- * E-mail:
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49
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Artificial Intelligence in radiotherapy: state of the art and future directions. Med Oncol 2020; 37:50. [PMID: 32323066 DOI: 10.1007/s12032-020-01374-w] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 04/13/2020] [Indexed: 02/06/2023]
Abstract
Recent advances in computing capability allowed the development of sophisticated predictive models to assess complex relationships within observational data, described as Artificial Intelligence. Medicine is one of the several fields of application and Radiation oncology could benefit from these approaches, particularly in patients' medical records, imaging, baseline pathology, planning or instrumental data. Artificial Intelligence systems could simplify many steps of the complex workflow of radiotherapy such as segmentation, planning or delivery. However, Artificial Intelligence could be considered as a "black box" in which human operator may only understand input and output predictions and its application to the clinical practice remains a challenge. The low transparency of the overall system is questionable from manifold points of view (ethical included). Given the complexity of this issue, we collected the basic definitions to help the clinician to understand current literature, and overviewed experiences regarding implementation of AI within radiotherapy clinical workflow, aiming to describe this field from the clinician perspective.
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50
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Lee S, Liang X, Woods M, Reiner AS, Concannon P, Bernstein L, Lynch CF, Boice JD, Deasy JO, Bernstein JL, Oh JH. Machine learning on genome-wide association studies to predict the risk of radiation-associated contralateral breast cancer in the WECARE Study. PLoS One 2020; 15:e0226157. [PMID: 32106268 PMCID: PMC7046218 DOI: 10.1371/journal.pone.0226157] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Accepted: 11/20/2019] [Indexed: 01/13/2023] Open
Abstract
The purpose of this study was to identify germline single nucleotide polymorphisms (SNPs) that optimally predict radiation-associated contralateral breast cancer (RCBC) and to provide new biological insights into the carcinogenic process. Fifty-two women with contralateral breast cancer and 153 women with unilateral breast cancer were identified within the Women’s Environmental Cancer and Radiation Epidemiology (WECARE) Study who were at increased risk of RCBC because they were ≤ 40 years of age at first diagnosis of breast cancer and received a scatter radiation dose > 1 Gy to the contralateral breast. A previously reported algorithm, preconditioned random forest regression, was applied to predict the risk of developing RCBC. The resulting model produced an area under the curve (AUC) of 0.62 (p = 0.04) on hold-out validation data. The biological analysis identified the cyclic AMP-mediated signaling and Ephrin-A as significant biological correlates, which were previously shown to influence cell survival after radiation in an ATM-dependent manner. The key connected genes and proteins that are identified in this analysis were previously identified as relevant to breast cancer, radiation response, or both. In summary, machine learning/bioinformatics methods applied to genome-wide genotyping data have great potential to reveal plausible biological correlates associated with the risk of RCBC.
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Affiliation(s)
- Sangkyu Lee
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Xiaolin Liang
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Meghan Woods
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Anne S. Reiner
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Patrick Concannon
- Genetics Institute and Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, United States of America
| | - Leslie Bernstein
- Department of Population Sciences, Beckman Research Institute of the City of Hope, Duarte, CA, United States of America
| | - Charles F. Lynch
- Department of Epidemiology, The University of Iowa, Iowa City, IA, United States of America
| | - John D. Boice
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - Joseph O. Deasy
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Jonine L. Bernstein
- Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
| | - Jung Hun Oh
- Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, United States of America
- * E-mail:
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