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Fechter T, Sachpazidis I, Baltas D. The use of deep learning in interventional radiotherapy (brachytherapy): A review with a focus on open source and open data. Z Med Phys 2024; 34:180-196. [PMID: 36376203 PMCID: PMC11156786 DOI: 10.1016/j.zemedi.2022.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Revised: 10/07/2022] [Accepted: 10/10/2022] [Indexed: 11/13/2022]
Abstract
Deep learning advanced to one of the most important technologies in almost all medical fields. Especially in areas, related to medical imaging it plays a big role. However, in interventional radiotherapy (brachytherapy) deep learning is still in an early phase. In this review, first, we investigated and scrutinised the role of deep learning in all processes of interventional radiotherapy and directly related fields. Additionally, we summarised the most recent developments. For better understanding, we provide explanations of key terms and approaches to solving common deep learning problems. To reproduce results of deep learning algorithms both source code and training data must be available. Therefore, a second focus of this work is on the analysis of the availability of open source, open data and open models. In our analysis, we were able to show that deep learning plays already a major role in some areas of interventional radiotherapy, but is still hardly present in others. Nevertheless, its impact is increasing with the years, partly self-propelled but also influenced by closely related fields. Open source, data and models are growing in number but are still scarce and unevenly distributed among different research groups. The reluctance in publishing code, data and models limits reproducibility and restricts evaluation to mono-institutional datasets. The conclusion of our analysis is that deep learning can positively change the workflow of interventional radiotherapy but there is still room for improvements when it comes to reproducible results and standardised evaluation methods.
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Affiliation(s)
- Tobias Fechter
- Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Germany.
| | - Ilias Sachpazidis
- Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Germany
| | - Dimos Baltas
- Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Germany; Faculty of Medicine, University of Freiburg, Germany; German Cancer Consortium (DKTK), Partner Site Freiburg, Germany
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Garg P, Mohanty A, Ramisetty S, Kulkarni P, Horne D, Pisick E, Salgia R, Singhal SS. Artificial intelligence and allied subsets in early detection and preclusion of gynecological cancers. Biochim Biophys Acta Rev Cancer 2023; 1878:189026. [PMID: 37980945 DOI: 10.1016/j.bbcan.2023.189026] [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: 09/17/2023] [Revised: 11/09/2023] [Accepted: 11/14/2023] [Indexed: 11/21/2023]
Abstract
Gynecological cancers including breast, cervical, ovarian, uterine, and vaginal, pose the greatest threat to world health, with early identification being crucial to patient outcomes and survival rates. The application of machine learning (ML) and artificial intelligence (AI) approaches to the study of gynecological cancer has shown potential to revolutionize cancer detection and diagnosis. The current review outlines the significant advancements, obstacles, and prospects brought about by AI and ML technologies in the timely identification and accurate diagnosis of different types of gynecological cancers. The AI-powered technologies can use genomic data to discover genetic alterations and biomarkers linked to a particular form of gynecologic cancer, assisting in the creation of targeted treatments. Furthermore, it has been shown that the potential benefits of AI and ML technologies in gynecologic tumors can greatly increase the accuracy and efficacy of cancer diagnosis, reduce diagnostic delays, and possibly eliminate the need for needless invasive operations. In conclusion, the review focused on the integrative part of AI and ML based tools and techniques in the early detection and exclusion of various cancer types; together with a collaborative coordination between research clinicians, data scientists, and regulatory authorities, which is suggested to realize the full potential of AI and ML in gynecologic cancer care.
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Affiliation(s)
- Pankaj Garg
- Department of Chemistry, GLA University, Mathura, Uttar Pradesh 281406, India
| | - Atish Mohanty
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Sravani Ramisetty
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Prakash Kulkarni
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - David Horne
- Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Evan Pisick
- Department of Medical Oncology, City of Hope, Chicago, IL 60099, USA
| | - Ravi Salgia
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA
| | - Sharad S Singhal
- Departments of Medical Oncology & Therapeutics Research, Molecular Medicine, Beckman Research Institute of City of Hope, Comprehensive Cancer Center and National Medical Center, Duarte, CA 91010, USA.
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Jan YT, Tsai PS, Huang WH, Chou LY, Huang SC, Wang JZ, Lu PH, Lin DC, Yen CS, Teng JP, Mok GSP, Shih CT, Wu TH. Machine learning combined with radiomics and deep learning features extracted from CT images: a novel AI model to distinguish benign from malignant ovarian tumors. Insights Imaging 2023; 14:68. [PMID: 37093321 PMCID: PMC10126170 DOI: 10.1186/s13244-023-01412-x] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 03/20/2023] [Indexed: 04/25/2023] Open
Abstract
BACKGROUND To develop an artificial intelligence (AI) model with radiomics and deep learning (DL) features extracted from CT images to distinguish benign from malignant ovarian tumors. METHODS We enrolled 149 patients with pathologically confirmed ovarian tumors. A total of 185 tumors were included and divided into training and testing sets in a 7:3 ratio. All tumors were manually segmented from preoperative contrast-enhanced CT images. CT image features were extracted using radiomics and DL. Five models with different combinations of feature sets were built. Benign and malignant tumors were classified using machine learning (ML) classifiers. The model performance was compared with five radiologists on the testing set. RESULTS Among the five models, the best performing model is the ensemble model with a combination of radiomics, DL, and clinical feature sets. The model achieved an accuracy of 82%, specificity of 89% and sensitivity of 68%. Compared with junior radiologists averaged results, the model had a higher accuracy (82% vs 66%) and specificity (89% vs 65%) with comparable sensitivity (68% vs 67%). With the assistance of the model, the junior radiologists achieved a higher average accuracy (81% vs 66%), specificity (80% vs 65%), and sensitivity (82% vs 67%), approaching to the performance of senior radiologists. CONCLUSIONS We developed a CT-based AI model that can differentiate benign and malignant ovarian tumors with high accuracy and specificity. This model significantly improved the performance of less-experienced radiologists in ovarian tumor assessment, and may potentially guide gynecologists to provide better therapeutic strategies for these patients.
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Affiliation(s)
- Ya-Ting Jan
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Pei-Shan Tsai
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Wen-Hui Huang
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Ling-Ying Chou
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Shih-Chieh Huang
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Jing-Zhe Wang
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Pei-Hsuan Lu
- Department of Radiology, MacKay Memorial Hospital, Taipei, Taiwan
- Department of Medicine, MacKay Medical College, New Taipei City, Taiwan
- MacKay Junior College of Medicine, Nursing and Management, New Taipei City, Taiwan
| | - Dao-Chen Lin
- Division of Endocrine and Metabolism, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Chun-Sheng Yen
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Ju-Ping Teng
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan
| | - Greta S P Mok
- Biomedical Imaging Laboratory (BIG), Department of Electrical and Computer Engineering, Faculty of Science and Technology, University of Macau, Macau, China
| | - Cheng-Ting Shih
- Department of Biomedical Imaging and Radiological Science, China Medical University, Taichung, 404, Taiwan.
| | - Tung-Hsin Wu
- Department of Biomedical Imaging and Radiological Sciences, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan.
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Daoud T, Sardana S, Stanietzky N, Klekers AR, Bhosale P, Morani AC. Recent Imaging Updates and Advances in Gynecologic Malignancies. Cancers (Basel) 2022; 14:cancers14225528. [PMID: 36428624 PMCID: PMC9688526 DOI: 10.3390/cancers14225528] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Revised: 10/31/2022] [Accepted: 11/09/2022] [Indexed: 11/12/2022] Open
Abstract
Gynecologic malignancies are among the most common cancers in women worldwide and account for significant morbidity and mortality. Management and consequently overall patient survival is reliant upon early detection, accurate staging and early detection of any recurrence. Ultrasound, Computed Tomography (CT), Magnetic resonance imaging (MRI) and Positron Emission Tomography-Computed Tomography (PET-CT) play an essential role in the detection, characterization, staging and restaging of the most common gynecologic malignancies, namely the cervical, endometrial and ovarian malignancies. Recent advances in imaging including functional MRI, hybrid imaging with Positron Emission Tomography (PET/MRI) contribute even more to lesion specification and overall role of imaging in gynecologic malignancies. Radiomics is a neoteric approach which aspires to enhance decision support by extracting quantitative information from radiological imaging.
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Laios A, Kalampokis E, Johnson R, Munot S, Thangavelu A, Hutson R, Broadhead T, Theophilou G, Leach C, Nugent D, De Jong D. Factors Predicting Surgical Effort Using Explainable Artificial Intelligence in Advanced Stage Epithelial Ovarian Cancer. Cancers (Basel) 2022; 14:cancers14143447. [PMID: 35884506 PMCID: PMC9316555 DOI: 10.3390/cancers14143447] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Revised: 07/04/2022] [Accepted: 07/05/2022] [Indexed: 12/24/2022] Open
Abstract
(1) Background: Surgical cytoreduction for epithelial ovarian cancer (EOC) is a complex procedure. Encompassed within the performance skills to achieve surgical precision, intra-operative surgical decision-making remains a core feature. The use of eXplainable Artificial Intelligence (XAI) could potentially interpret the influence of human factors on the surgical effort for the cytoreductive outcome in question; (2) Methods: The retrospective cohort study evaluated 560 consecutive EOC patients who underwent cytoreductive surgery between January 2014 and December 2019 in a single public institution. The eXtreme Gradient Boosting (XGBoost) and Deep Neural Network (DNN) algorithms were employed to develop the predictive model, including patient- and operation-specific features, and novel features reflecting human factors in surgical heuristics. The precision, recall, F1 score, and area under curve (AUC) were compared between both training algorithms. The SHapley Additive exPlanations (SHAP) framework was used to provide global and local explainability for the predictive model; (3) Results: A surgical complexity score (SCS) cut-off value of five was calculated using a Receiver Operator Characteristic (ROC) curve, above which the probability of incomplete cytoreduction was more likely (area under the curve [AUC] = 0.644; 95% confidence interval [CI] = 0.598−0.69; sensitivity and specificity 34.1%, 86.5%, respectively; p = 0.000). The XGBoost outperformed the DNN assessment for the prediction of the above threshold surgical effort outcome (AUC = 0.77; 95% [CI] 0.69−0.85; p < 0.05 vs. AUC 0.739; 95% [CI] 0.655−0.823; p < 0.95). We identified “turning points” that demonstrated a clear preference towards above the given cut-off level of surgical effort; in consultant surgeons with <12 years of experience, age <53 years old, who, when attempting primary cytoreductive surgery, recorded the presence of ascites, an Intraoperative Mapping of Ovarian Cancer score >4, and a Peritoneal Carcinomatosis Index >7, in a surgical environment with the optimization of infrastructural support. (4) Conclusions: Using XAI, we explain how intra-operative decisions may consider human factors during EOC cytoreduction alongside factual knowledge, to maximize the magnitude of the selected trade-off in effort. XAI techniques are critical for a better understanding of Artificial Intelligence frameworks, and to enhance their incorporation in medical applications.
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Affiliation(s)
- Alexandros Laios
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
- Correspondence:
| | | | - Racheal Johnson
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Sarika Munot
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Amudha Thangavelu
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Richard Hutson
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Tim Broadhead
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Georgios Theophilou
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Chris Leach
- School of Human & Health Sciences, University of Huddersfield, Huddersfield HD1 3DH, UK;
- Department of Psychology Services, South West Yorkshire Mental Health NHS Foundation Trust, The Laura Mitchell Health & Wellbeing Centre, Halifax HX1 1YR, UK
| | - David Nugent
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
| | - Diederick De Jong
- Department of Gynaecologic Oncology, St James’s University Hospital, Leeds LS9 7TF, UK; (R.J.); (S.M.); (A.T.); (R.H.); (T.B.); (G.T.); (D.N.); (D.D.J.)
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Urushibara A, Saida T, Mori K, Ishiguro T, Inoue K, Masumoto T, Satoh T, Nakajima T. The efficacy of deep learning models in the diagnosis of endometrial cancer using MRI: a comparison with radiologists. BMC Med Imaging 2022; 22:80. [PMID: 35501705 PMCID: PMC9063362 DOI: 10.1186/s12880-022-00808-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 04/21/2022] [Indexed: 12/02/2022] Open
Abstract
Purpose To compare the diagnostic performance of deep learning models using convolutional neural networks (CNN) with that of radiologists in diagnosing endometrial cancer and to verify suitable imaging conditions. Methods This retrospective study included patients with endometrial cancer or non-cancerous lesions who underwent MRI between 2015 and 2020. In Experiment 1, single and combined image sets of several sequences from 204 patients with cancer and 184 patients with non-cancerous lesions were used to train CNNs. Subsequently, testing was performed using 97 images from 51 patients with cancer and 46 patients with non-cancerous lesions. The test image sets were independently interpreted by three blinded radiologists. Experiment 2 investigated whether the addition of different types of images for training using the single image sets improved the diagnostic performance of CNNs. Results The AUC of the CNNs pertaining to the single and combined image sets were 0.88–0.95 and 0.87–0.93, respectively, indicating non-inferior diagnostic performance than the radiologists. The AUC of the CNNs trained with the addition of other types of single images to the single image sets was 0.88–0.95. Conclusion CNNs demonstrated high diagnostic performance for the diagnosis of endometrial cancer using MRI. Although there were no significant differences, adding other types of images improved the diagnostic performance for some single image sets.
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Affiliation(s)
- Aiko Urushibara
- Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | - Tsukasa Saida
- Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.
| | - Kensaku Mori
- Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | - Toshitaka Ishiguro
- Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | - Kei Inoue
- Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | - Tomohiko Masumoto
- Department of Diagnostic Radiology, Toranomon Hospital, 2-2-2 Toranomon, Minato-ku, Tokyo, 105-8470, Japan
| | - Toyomi Satoh
- Department of Obstetrics and Gynecology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan
| | - Takahito Nakajima
- Department of Radiology, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan.
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Laios A, Kalampokis E, Johnson R, Thangavelu A, Tarabanis C, Nugent D, De Jong D. Explainable Artificial Intelligence for Prediction of Complete Surgical Cytoreduction in Advanced-Stage Epithelial Ovarian Cancer. J Pers Med 2022; 12:607. [PMID: 35455723 PMCID: PMC9030484 DOI: 10.3390/jpm12040607] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Revised: 03/31/2022] [Accepted: 04/06/2022] [Indexed: 02/04/2023] Open
Abstract
Complete surgical cytoreduction (R0 resection) is the single most important prognosticator in epithelial ovarian cancer (EOC). Explainable Artificial Intelligence (XAI) could clarify the influence of static and real-time features in the R0 resection prediction. We aimed to develop an AI-based predictive model for the R0 resection outcome, apply a methodology to explain the prediction, and evaluate the interpretability by analysing feature interactions. The retrospective cohort finally assessed 571 consecutive advanced-stage EOC patients who underwent cytoreductive surgery. An eXtreme Gradient Boosting (XGBoost) algorithm was employed to develop the predictive model including mostly patient- and surgery-specific variables. The Shapley Additive explanations (SHAP) framework was used to provide global and local explainability for the predictive model. The XGBoost accurately predicted R0 resection (area under curve [AUC] = 0.866; 95% confidence interval [CI] = 0.8−0.93). We identified “turning points” that increased the probability of complete cytoreduction including Intraoperative Mapping of Ovarian Cancer Score and Peritoneal Carcinomatosis Index < 4 and <5, respectively, followed by Surgical Complexity Score > 4, patient’s age < 60 years, and largest tumour bulk < 5 cm in a surgical environment of optimized infrastructural support. We demonstrated high model accuracy for the R0 resection prediction in EOC patients and provided novel global and local feature explainability that can be used for quality control and internal audit.
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Affiliation(s)
- Alexandros Laios
- Department of Gynaecologic Oncology, St James's University Hospital, Leeds LS9 7TF, UK
| | - Evangelos Kalampokis
- Department of Business Administration, University of Macedonia, 54636 Thessaloniki, Greece
- Center for Research & Technology HELLAS (CERTH), 6th km Charilaou-Thermi Rd., 57001 Thessaloniki, Greece
| | - Racheal Johnson
- Department of Gynaecologic Oncology, St James's University Hospital, Leeds LS9 7TF, UK
| | - Amudha Thangavelu
- Department of Gynaecologic Oncology, St James's University Hospital, Leeds LS9 7TF, UK
| | - Constantine Tarabanis
- Department of Internal Medicine, School of Medicine, New York University, NYU, Langone Health, New York, NY 10016, USA
| | - David Nugent
- Department of Gynaecologic Oncology, St James's University Hospital, Leeds LS9 7TF, UK
| | - Diederick De Jong
- Department of Gynaecologic Oncology, St James's University Hospital, Leeds LS9 7TF, UK
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Mikdadi D, O'Connell KA, Meacham PJ, Dugan MA, Ojiere MO, Carlson TB, Klenk JA. Applications of artificial intelligence (AI) in ovarian cancer, pancreatic cancer, and image biomarker discovery. Cancer Biomark 2022; 33:173-184. [PMID: 35213360 DOI: 10.3233/cbm-210301] [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: 12/24/2022]
Abstract
BACKGROUND Artificial intelligence (AI), including machine learning (ML) and deep learning, has the potential to revolutionize biomedical research. Defined as the ability to "mimic" human intelligence by machines executing trained algorithms, AI methods are deployed for biomarker discovery. OBJECTIVE We detail the advancements and challenges in the use of AI for biomarker discovery in ovarian and pancreatic cancer. We also provide an overview of associated regulatory and ethical considerations. METHODS We conducted a literature review using PubMed and Google Scholar to survey the published findings on the use of AI in ovarian cancer, pancreatic cancer, and cancer biomarkers. RESULTS Most AI models associated with ovarian and pancreatic cancer have yet to be applied in clinical settings, and imaging data in many studies are not publicly available. Low disease prevalence and asymptomatic disease limits data availability required for AI models. The FDA has yet to qualify imaging biomarkers as effective diagnostic tools for these cancers. CONCLUSIONS Challenges associated with data availability, quality, bias, as well as AI transparency and explainability, will likely persist. Explainable and trustworthy AI efforts will need to continue so that the research community can better understand and construct effective models for biomarker discovery in rare cancers.
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Affiliation(s)
- Dina Mikdadi
- Biomedical Data Science Lab, Deloitte Consulting LLP, Arlington, VA, USA
| | - Kyle A O'Connell
- Biomedical Data Science Lab, Deloitte Consulting LLP, Arlington, VA, USA.,Department of Biology, George Washington University, Washington, DC, USA
| | - Philip J Meacham
- Biomedical Data Science Lab, Deloitte Consulting LLP, Arlington, VA, USA
| | - Madeleine A Dugan
- Biomedical Data Science Lab, Deloitte Consulting LLP, Arlington, VA, USA
| | - Michael O Ojiere
- Biomedical Data Science Lab, Deloitte Consulting LLP, Arlington, VA, USA
| | - Thaddeus B Carlson
- Biomedical Data Science Lab, Deloitte Consulting LLP, Arlington, VA, USA
| | - Juergen A Klenk
- Biomedical Data Science Lab, Deloitte Consulting LLP, Arlington, VA, USA
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Weichert J, Welp A, Scharf JL, Dracopoulos C, Becker WH, Gembicki M. The Use of Artificial Intelligence in Automation in the Fields of Gynaecology and Obstetrics - an Assessment of the State of Play. Geburtshilfe Frauenheilkd 2021; 81:1203-1216. [PMID: 34754270 PMCID: PMC8568505 DOI: 10.1055/a-1522-3029] [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: 04/22/2021] [Accepted: 06/01/2021] [Indexed: 11/20/2022] Open
Abstract
The long-awaited progress in digitalisation is generating huge amounts of medical data every day, and manual analysis and targeted, patient-oriented evaluation of this data is becoming increasingly difficult or even infeasible. This state of affairs and the associated, increasingly complex requirements for individualised precision medicine underline the need for modern software solutions and algorithms across the entire healthcare system. The utilisation of state-of-the-art equipment and techniques in almost all areas of medicine over the past few years has now indeed enabled automation processes to enter - at least in part - into routine clinical practice. Such systems utilise a wide variety of artificial intelligence (AI) techniques, the majority of which have been developed to optimise medical image reconstruction, noise reduction, quality assurance, triage, segmentation, computer-aided detection and classification and, as an emerging field of research, radiogenomics. Tasks handled by AI are completed significantly faster and more precisely, clearly demonstrated by now in the annual findings of the ImageNet Large-Scale Visual Recognition Challenge (ILSVCR), first conducted in 2015, with error rates well below those of humans. This review article will discuss the potential capabilities and currently available applications of AI in gynaecological-obstetric diagnostics. The article will focus, in particular, on automated techniques in prenatal sonographic diagnostics.
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Affiliation(s)
- Jan Weichert
- Klinik für Frauenheilkunde und Geburtshilfe, Bereich Pränatalmedizin und Spezielle Geburtshilfe, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
- Zentrum für Pränatalmedizin an der Elbe, Hamburg, Germany
| | - Amrei Welp
- Klinik für Frauenheilkunde und Geburtshilfe, Bereich Pränatalmedizin und Spezielle Geburtshilfe, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Jann Lennard Scharf
- Klinik für Frauenheilkunde und Geburtshilfe, Bereich Pränatalmedizin und Spezielle Geburtshilfe, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | - Christoph Dracopoulos
- Klinik für Frauenheilkunde und Geburtshilfe, Bereich Pränatalmedizin und Spezielle Geburtshilfe, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
| | | | - Michael Gembicki
- Klinik für Frauenheilkunde und Geburtshilfe, Bereich Pränatalmedizin und Spezielle Geburtshilfe, Universitätsklinikum Schleswig-Holstein, Campus Lübeck, Lübeck, Germany
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Laios A, Katsenou A, Tan YS, Johnson R, Otify M, Kaufmann A, Munot S, Thangavelu A, Hutson R, Broadhead T, Theophilou G, Nugent D, De Jong D. Feature Selection is Critical for 2-Year Prognosis in Advanced Stage High Grade Serous Ovarian Cancer by Using Machine Learning. Cancer Control 2021; 28:10732748211044678. [PMID: 34693730 PMCID: PMC8549478 DOI: 10.1177/10732748211044678] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023] Open
Abstract
Introduction Accurate prediction of patient prognosis can be especially useful for the selection of best treatment protocols. Machine Learning can serve this purpose by making predictions based upon generalizable clinical patterns embedded within learning datasets. We designed a study to support the feature selection for the 2-year prognostic period and compared the performance of several Machine Learning prediction algorithms for accurate 2-year prognosis estimation in advanced-stage high grade serous ovarian cancer (HGSOC) patients. Methods The prognosis estimation was formulated as a binary classification problem. Dataset was split into training and test cohorts with repeated random sampling until there was no significant difference (p = 0.20) between the two cohorts. A ten-fold cross-validation was applied. Various state-of-the-art supervised classifiers were used. For feature selection, in addition to the exhaustive search for the best combination of features, we used the-chi square test of independence and the MRMR method. Results Two hundred nine patients were identified. The model's mean prediction accuracy reached 73%. We demonstrated that Support-Vector-Machine and Ensemble Subspace Discriminant algorithms outperformed Logistic Regression in accuracy indices. The probability of achieving a cancer-free state was maximised with a combination of primary cytoreduction, good performance status and maximal surgical effort (AUC 0.63). Standard chemotherapy, performance status, tumour load and residual disease were consistently predictive of the mid-term overall survival (AUC 0.63–0.66). The model recall and precision were greater than 80%. Conclusion Machine Learning appears to be promising for accurate prognosis estimation. Appropriate feature selection is required when building an HGSOC model for 2-year prognosis prediction. We provide evidence as to what combination of prognosticators leads to the largest impact on the HGSOC 2-year prognosis.
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Affiliation(s)
- Alexandros Laios
- Department of Gynaecologic Oncology, Leeds Teaching Hospitals, 98540St James's University Hospital, Leeds, UK
| | - Angeliki Katsenou
- Department of Electrical and Electronic Engineering, Visual Information Lab, 1980University of Bristol, Bristol, UK
| | - Yong Sheng Tan
- Department of Gynaecologic Oncology, Leeds Teaching Hospitals, 98540St James's University Hospital, Leeds, UK
| | - Racheal Johnson
- Department of Gynaecologic Oncology, Leeds Teaching Hospitals, 98540St James's University Hospital, Leeds, UK
| | - Mohamed Otify
- Department of Gynaecologic Oncology, Leeds Teaching Hospitals, 98540St James's University Hospital, Leeds, UK
| | - Angelika Kaufmann
- Department of Gynaecologic Oncology, Leeds Teaching Hospitals, 98540St James's University Hospital, Leeds, UK
| | - Sarika Munot
- Department of Gynaecologic Oncology, Leeds Teaching Hospitals, 98540St James's University Hospital, Leeds, UK
| | - Amudha Thangavelu
- Department of Gynaecologic Oncology, Leeds Teaching Hospitals, 98540St James's University Hospital, Leeds, UK
| | - Richard Hutson
- Department of Gynaecologic Oncology, Leeds Teaching Hospitals, 98540St James's University Hospital, Leeds, UK
| | - Tim Broadhead
- Department of Gynaecologic Oncology, Leeds Teaching Hospitals, 98540St James's University Hospital, Leeds, UK
| | - Georgios Theophilou
- Department of Gynaecologic Oncology, Leeds Teaching Hospitals, 98540St James's University Hospital, Leeds, UK
| | - David Nugent
- Department of Gynaecologic Oncology, Leeds Teaching Hospitals, 98540St James's University Hospital, Leeds, UK
| | - Diederick De Jong
- Department of Gynaecologic Oncology, Leeds Teaching Hospitals, 98540St James's University Hospital, Leeds, UK
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