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Chakrabarty N, Mahajan A. Imaging Analytics using Artificial Intelligence in Oncology: A Comprehensive Review. Clin Oncol (R Coll Radiol) 2024; 36:498-513. [PMID: 37806795 DOI: 10.1016/j.clon.2023.09.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 08/09/2023] [Accepted: 09/21/2023] [Indexed: 10/10/2023]
Abstract
The present era has seen a surge in artificial intelligence-related research in oncology, mainly using deep learning, because of powerful computer hardware, improved algorithms and the availability of large amounts of data from open-source domains and the use of transfer learning. Here we discuss the multifaceted role of deep learning in cancer care, ranging from risk stratification, the screening and diagnosis of cancer, to the prediction of genomic mutations, treatment response and survival outcome prediction, through the use of convolutional neural networks. Another role of artificial intelligence is in the generation of automated radiology reports, which is a boon in high-volume centres to minimise report turnaround time. Although a validated and deployable deep-learning model for clinical use is still in its infancy, there is ongoing research to overcome the barriers for its universal implementation and we also delve into this aspect. We also briefly describe the role of radiomics in oncoimaging. Artificial intelligence can provide answers pertaining to cancer management at baseline imaging, saving cost and time. Imaging biobanks, which are repositories of anonymised images, are also briefly described. We also discuss the commercialisation and ethical issues pertaining to artificial intelligence. The latest generation generalist artificial intelligence model is also briefly described at the end of the article. We believe this article will not only enrich knowledge, but also promote research acumen in the minds of readers to take oncoimaging to another level using artificial intelligence and also work towards clinical translation of such research.
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Affiliation(s)
- N Chakrabarty
- Department of Radiodiagnosis, Advanced Centre for Treatment, Research and Education in Cancer, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Parel, Mumbai, Maharashtra, India.
| | - A Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK.
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Choo H, Yoo SY, Moon S, Park M, Lee J, Sung KW, Cha WC, Shin SY, Son MH. Deep-learning-based personalized prediction of absolute neutrophil count recovery and comparison with clinicians for validation. J Biomed Inform 2023; 137:104268. [PMID: 36513332 DOI: 10.1016/j.jbi.2022.104268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2022] [Revised: 11/27/2022] [Accepted: 12/07/2022] [Indexed: 12/14/2022]
Abstract
Neutropenia and its complications are major adverse effects of cytotoxic chemotherapy. The time to recovery from neutropenia varies from patient to patient, and cannot be easily predicted even by experts. Therefore, we trained a deep learning model using data from 525 pediatric patients with solid tumors to predict the day when patients recover from severe neutropenia after high-dose chemotherapy. We validated the model with data from 99 patients and compared its performance to those of clinicians. The accuracy of the model at predicting the recovery day, with a 1-day error, was 76%; its performance was better than those of the specialist group (58.59%) and the resident group (32.33%). In addition, 80% of clinicians changed their initial predictions at least once after the model's prediction was conveyed to them. In total, 86 prediction changes (90.53%) improved the recovery day estimate.
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Affiliation(s)
- Hyunwoo Choo
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea; Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Seoul, Republic of Korea
| | - Su Young Yoo
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Suhyeon Moon
- Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
| | - Minsu Park
- Department of Information and Statistics, Chungnam National University, Korea 99 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea
| | - Jiwon Lee
- Department of Pediatrics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Ki Woong Sung
- Department of Pediatrics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Won Chul Cha
- Department of Emergency Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Soo-Yong Shin
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea; Department of Intelligent Precision Healthcare Convergence, Sungkyunkwan University, Seoul, Republic of Korea; Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea.
| | - Meong Hi Son
- Department of Pediatrics, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
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Prediction of chemotherapy-related complications in pediatric oncology patients: artificial intelligence and machine learning implementations. Pediatr Res 2023; 93:390-395. [PMID: 36302858 DOI: 10.1038/s41390-022-02356-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 10/08/2022] [Accepted: 10/12/2022] [Indexed: 11/08/2022]
Abstract
Although the overall incidence of pediatric oncological diseases tends to increase over the years, it is among the rare diseases of the pediatric population. The diagnosis, treatment, and healthcare management of this group of diseases are important. Prevention of treatment-related complications is vital for patients, particularly in the pediatric population. Nowadays, the use of artificial intelligence and machine learning technologies in the management of oncological diseases is becoming increasingly important. With the advancement of software technologies, improvements have been made in the early diagnosis of risk groups in oncological diseases, in radiology, pathology, and imaging technologies, in cancer staging and management. In addition, these technologies can be used to predict the outcome in chemotherapy treatment of oncological diseases. In this context, this study identifies artificial intelligence and machine learning methods used in the prediction of complications due to chemotherapeutic agents used in childhood cancer treatment. For this purpose, the concepts of artificial intelligence and machine learning are explained in this review. A general framework for the use of machine learning in healthcare and pediatric oncology has been drawn and examples of studies conducted on this topic in pediatric oncology have been given. IMPACT: Artificial intelligence and machine learning are advanced tools that can be used to predict chemotherapy-related complications. Algorithms can assist clinicians' decision-making processes in the management of complications. Although studies are using these methods, there is a need to increase the number of studies on artificial intelligence applications in pediatric clinics.
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Farina E, Nabhen JJ, Dacoregio MI, Batalini F, Moraes FY. An overview of artificial intelligence in oncology. Future Sci OA 2022; 8:FSO787. [PMID: 35369274 PMCID: PMC8965797 DOI: 10.2144/fsoa-2021-0074] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 01/19/2022] [Indexed: 11/23/2022] Open
Abstract
Cancer is associated with significant morbimortality globally. Advances in screening, diagnosis, management and survivorship were substantial in the last decades, however, challenges in providing personalized and data-oriented care remain. Artificial intelligence (AI), a branch of computer science used for predictions and automation, has emerged as potential solution to improve the healthcare journey and to promote precision in healthcare. AI applications in oncology include, but are not limited to, optimization of cancer research, improvement of clinical practice (eg., prediction of the association of multiple parameters and outcomes - prognosis and response) and better understanding of tumor molecular biology. In this review, we examine the current state of AI in oncology, including fundamentals, current applications, limitations and future perspectives.
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Affiliation(s)
- Eduardo Farina
- Department of Radiology, Federal University of São Paulo, SP, 04021-001, Brazil; Diagnósticos da America SA (Dasa), 05425-020, Brazil
| | - Jacqueline J Nabhen
- School of Medicine, Federal University of Paraná, Curitiba, PR, 80060-000, Brazil
| | - Maria Inez Dacoregio
- School of Medicine, State University of Centro-Oeste, Guarapuava, PR, 85040-167, Brazil
| | - Felipe Batalini
- Department of Medicine, Division of Medical Oncology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Fabio Y Moraes
- Department of Oncology, Division of Radiation Oncology, Queen's University, Kingston, ON, K7L 3N6, Canada
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PrACTiC: A Predictive Algorithm for Chemoradiotherapy-Induced Cytopenia in Glioblastoma Patients. JOURNAL OF ONCOLOGY 2022; 2022:1438190. [PMID: 35111223 PMCID: PMC8803420 DOI: 10.1155/2022/1438190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 01/06/2022] [Indexed: 11/18/2022]
Abstract
Background Chemotherapy-induced cytopenia is the most frequent side effect of chemoradiotherapy in glioblastoma patients which may lead to reduced delivery of treatment. This study aims to develop a predictive model that is able to forecast the cytopenia induced by temozolomide (TMZ) during concomitant chemoradiotherapy. Methods Medical records of 128 patients with newly diagnosed glioblastoma were evaluated to extract the baseline complete blood test before and during the six weeks of chemoradiotherapy to create a dataset for the development of ML models. Using the constructed dataset, different ML algorithms were trained and tested. Results Our proposed algorithm achieved accuracies of 85.6%, 88.7%, and 89.3% in predicting thrombocytopenia, lymphopenia, and neutropenia, respectively. Conclusions The algorithm designed and developed in this study, called PrACTiC, showed promising results in the accurate prediction of thrombocytopenia, neutropenia, and lymphopenia induced by TMZ in glioblastoma patients. PrACTiC can provide valuable insight for physicians and help them to make the necessary treatment modifications and prevent the toxicities.
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Ramesh S, Chokkara S, Shen T, Major A, Volchenboum SL, Mayampurath A, Applebaum MA. Applications of Artificial Intelligence in Pediatric Oncology: A Systematic Review. JCO Clin Cancer Inform 2021; 5:1208-1219. [PMID: 34910588 DOI: 10.1200/cci.21.00102] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
Abstract
PURPOSE There is a need for an improved understanding of clinical and biologic risk factors in pediatric cancer to improve patient outcomes. Machine learning (ML) represents the application of computational inference from advanced statistical methods that can be applied to increasing amount of data available for study in pediatric oncology. The goal of this systematic review was to systematically characterize the state of ML in pediatric oncology and highlight advances and opportunities in the field. METHODS We conducted a systematic review of the Embase, Scopus, and MEDLINE databases for applications of ML in pediatric oncology. Query results from all three databases were aggregated and duplicate studies were removed. RESULTS A total of 42 unique articles that examined the applications of ML in pediatric oncology met inclusion criteria for review. We identified 20 studies of CNS tumors, 13 of solid tumors, and nine of leukemia. ML tasks included classification, prediction of treatment response, and dose optimization with a variety of methods being used including neural network, k-nearest neighbor, random forest, naive Bayes, and support vector machines. Strengths of the identified studies included matching or outperforming physician comparators via automated analysis and predicting therapeutic response. Common limitations included significant heterogeneity in reporting standards, clinical applicability, small sample sizes, and missing external validation cohorts. CONCLUSION We identified areas where ML can enhance clinical care in ways that may not otherwise be achievable. Although ML promises enormous potential in improving diagnostics, decision making, and monitoring for children with cancer, the field remains in early stages and future work will be aided by standards and guidelines to ensure rigorous methodologic design and maximizing clinical utility.
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Affiliation(s)
- Siddhi Ramesh
- Pritzker School of Medicine, University of Chicago, Chicago, IL
| | - Sukarn Chokkara
- Pritzker School of Medicine, University of Chicago, Chicago, IL
| | - Timothy Shen
- Pritzker School of Medicine, University of Chicago, Chicago, IL
| | - Ajay Major
- Department of Medicine, Section of Hematology/Oncology, University of Chicago, Chicago, IL
| | - Samuel L Volchenboum
- Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL
| | - Anoop Mayampurath
- Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL
| | - Mark A Applebaum
- Department of Pediatrics, Section of Hematology/Oncology, University of Chicago, Chicago, IL
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