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Zhou T, Luo Y, Li J, Zhang H, Meng Z, Xiong W, Zhang J. Application of Artificial Intelligence in Oncology Nursing: A Scoping Review. Cancer Nurs 2024; 47:436-450. [PMID: 37272743 DOI: 10.1097/ncc.0000000000001254] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
BACKGROUND Artificial intelligence (AI) has been increasingly used in healthcare during the last decade, and recent applications in oncology nursing have shown great potential in improving care for patients with cancer. It is timely to comprehensively synthesize knowledge about the progress of AI technologies in oncology nursing. OBJECTIVE The aims of this study were to synthesize and evaluate the existing evidence of AI technologies applied in oncology nursing. METHODS A scoping review was conducted based on the methodological framework proposed by Arksey and O'Malley and later improved by the Joanna Briggs Institute. Six English databases and 3 Chinese databases were searched dating from January 2010 to November 2022. RESULTS A total of 28 articles were included in this review-26 in English and 2 in Chinese. Half of the studies used a descriptive design (level VI). The most widely used AI technologies were hybrid AI methods (28.6%) and machine learning (25.0%), which were primarily used for risk identification/prediction (28.6%). Almost half of the studies (46.4%) explored developmental stages of AI technologies. Ethical concerns were rarely addressed. CONCLUSIONS The applicability and prospect of AI in oncology nursing are promising, although there is a lack of evidence on the efficacy of these technologies in practice. More randomized controlled trials in real-life oncology nursing settings are still needed. IMPLICATIONS FOR PRACTICE This scoping review presents comprehensive findings for consideration of translation into practice and may provide guidance for future AI education, research, and clinical implementation in oncology nursing.
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Affiliation(s)
- Tianji Zhou
- Author Affiliations: Xiangya School of Nursing (Drs Zhou, Luo, Li, and Jingping Zhang; Mr Meng; and Miss Xiong) and Xiangya Hospital (Dr Hanyi Zhang), Central South University, Changsha, Hunan, China
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2
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Maita KC, Avila FR, Torres-Guzman RA, Garcia JP, De Sario Velasquez GD, Borna S, Brown SA, Haider CR, Ho OS, Forte AJ. The usefulness of artificial intelligence in breast reconstruction: a systematic review. Breast Cancer 2024; 31:562-571. [PMID: 38619786 DOI: 10.1007/s12282-024-01582-6] [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: 03/29/2023] [Accepted: 03/30/2024] [Indexed: 04/16/2024]
Abstract
BACKGROUND Artificial Intelligence (AI) offers an approach to predictive modeling. The model learns to determine specific patterns of undesirable outcomes in a dataset. Therefore, a decision-making algorithm can be built based on these patterns to prevent negative results. This systematic review aimed to evaluate the usefulness of AI in breast reconstruction. METHODS A systematic review was conducted in August 2022 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. MEDLINE, EMBASE, SCOPUS, and Google Scholar online databases were queried to capture all publications studying the use of artificial intelligence in breast reconstruction. RESULTS A total of 23 studies were full text-screened after removing duplicates, and twelve articles fulfilled our inclusion criteria. The Machine Learning algorithms applied for neuropathic pain, lymphedema diagnosis, microvascular abdominal flap failure, donor site complications associated to muscle sparing Transverse Rectus Abdominis flap, surgical complications, financial toxicity, and patient-reported outcomes after breast surgery demonstrated that AI is a helpful tool to accurately predict patient results. In addition, one study used Computer Vision technology to assist in Deep Inferior Epigastric Perforator Artery detection for flap design, considerably reducing the preoperative time compared to manual identification. CONCLUSIONS In breast reconstruction, AI can help the surgeon by optimizing the perioperative patients' counseling to predict negative outcomes, allowing execution of timely interventions and reducing the postoperative burden, which leads to obtaining the most successful results and improving patient satisfaction.
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Affiliation(s)
- Karla C Maita
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | - Francisco R Avila
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | | | - John P Garcia
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | | | - Sahar Borna
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | - Sally A Brown
- Department of Administration, Mayo Clinic, Jacksonville, FL, USA
| | - Clifton R Haider
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN, USA
| | - Olivia S Ho
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA
| | - Antonio Jorge Forte
- Division of Plastic Surgery, Mayo Clinic, 4500 San Pablo Rd, Jacksonville, FL, 32224, USA.
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3
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Qiu JM, Fu MR, Finlayson CS, Tilley CP, Payo RM, Korth S, Kremer HL, Lippincott CLR. Lymphatic pain in breast cancer survivors: An overview of the current evidence and recommendations. WOMEN AND CHILDREN NURSING 2024; 2:33-38. [PMID: 39421196 PMCID: PMC11486487 DOI: 10.1016/j.wcn.2024.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2024]
Abstract
Among the 7.8 million women with breast cancer worldwide, at least 33% to 44% of them are affected by lymphatic pain. Lymphatic pain refers to co-occurring pain (e.g., pain, aching or soreness) and swelling. Pharmacological approaches, such as the uses of NSAIDS, opioids, antiepileptics, ketamine and lidocaine, have very limited effects on lymphatic pain. Limited research in this field has made it difficult for patients and clinicians to differentiate lymphatic pain from other types of pain. Precision assessment to distinguish different types of pain is essential for finding efficacious cure for pain. Innovative behavioral interventions to promote lymph flow and reduce inflammation are promising to reduce lymphatic pain. The goal of this review is to provide a comprehensive understanding of lymphatic pain through research evidence-based knowledge and insights into precision assessment and therapeutic behavioral intervention for lymphatic pain.
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Affiliation(s)
- Jeanna Mary Qiu
- Harvard Medical School, 25 Shattuck Street, Boston, MA 02115
| | - Mei Rosemary Fu
- The Dorothy and Dale Thompson Missouri Endowed Professor in Nursing, Associate Dean for Research, School of Nursing and Health Studies, University of Missouri-Kansas City, 2464 Charlotte Street, 2nd Floor, Room 2326, Kansas City, Missouri 64108
| | - Catherine S. Finlayson
- Lienhard School of Nursing, College of Health Professions, Pace University, Wright Cottage, 861 Bedford Road, Pleasantville, NY 10570
| | | | - Rubén Martín Payo
- Faculty of Medicine & Health Sciences, University of Oviedo, Cristo Campus, 33006, Oviedo, Principality of Asturias, Spain
- Principality of Asturias Health Research Institute (ISPA), University Hospital Avenue, 33011, Oviedo, Principality of Asturias, Spain
| | - Stephanie Korth
- University Health Kansas City, Building #1, 2101 Charlotte Street, Suite #110, Kansas City, Missouri 64108
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Seth I, Lim B, Joseph K, Gracias D, Xie Y, Ross RJ, Rozen WM. Use of artificial intelligence in breast surgery: a narrative review. Gland Surg 2024; 13:395-411. [PMID: 38601286 PMCID: PMC11002485 DOI: 10.21037/gs-23-414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Accepted: 02/21/2024] [Indexed: 04/12/2024]
Abstract
Background and Objective We have witnessed tremendous advances in artificial intelligence (AI) technologies. Breast surgery, a subspecialty of general surgery, has notably benefited from AI technologies. This review aims to evaluate how AI has been integrated into breast surgery practices, to assess its effectiveness in improving surgical outcomes and operational efficiency, and to identify potential areas for future research and application. Methods Two authors independently conducted a comprehensive search of PubMed, Google Scholar, EMBASE, and Cochrane CENTRAL databases from January 1, 1950, to September 4, 2023, employing keywords pertinent to AI in conjunction with breast surgery or cancer. The search focused on English language publications, where relevance was determined through meticulous screening of titles, abstracts, and full-texts, followed by an additional review of references within these articles. The review covered a range of studies illustrating the applications of AI in breast surgery encompassing lesion diagnosis to postoperative follow-up. Publications focusing specifically on breast reconstruction were excluded. Key Content and Findings AI models have preoperative, intraoperative, and postoperative applications in the field of breast surgery. Using breast imaging scans and patient data, AI models have been designed to predict the risk of breast cancer and determine the need for breast cancer surgery. In addition, using breast imaging scans and histopathological slides, models were used for detecting, classifying, segmenting, grading, and staging breast tumors. Preoperative applications included patient education and the display of expected aesthetic outcomes. Models were also designed to provide intraoperative assistance for precise tumor resection and margin status assessment. As well, AI was used to predict postoperative complications, survival, and cancer recurrence. Conclusions Extra research is required to move AI models from the experimental stage to actual implementation in healthcare. With the rapid evolution of AI, further applications are expected in the coming years including direct performance of breast surgery. Breast surgeons should be updated with the advances in AI applications in breast surgery to provide the best care for their patients.
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Affiliation(s)
- Ishith Seth
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Central Clinical School at Monash University, The Alfred Centre, Melbourne, Victoria, Australia
| | - Bryan Lim
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Central Clinical School at Monash University, The Alfred Centre, Melbourne, Victoria, Australia
| | - Konrad Joseph
- Department of Surgery, Port Macquarie Base Hospital, New South Wales, Australia
| | - Dylan Gracias
- Department of Surgery, Townsville Hospital, Queensland, Australia
| | - Yi Xie
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
| | - Richard J. Ross
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Central Clinical School at Monash University, The Alfred Centre, Melbourne, Victoria, Australia
| | - Warren M. Rozen
- Department of Plastic Surgery, Peninsula Health, Melbourne, Victoria, Australia
- Central Clinical School at Monash University, The Alfred Centre, Melbourne, Victoria, Australia
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Jayawickrama SM, Ranaweera PM, Pradeep RGGR, Jayasinghe YA, Senevirathna K, Hilmi AJ, Rajapakse RMG, Kanmodi KK, Jayasinghe RD. Developments and future prospects of personalized medicine in head and neck squamous cell carcinoma diagnoses and treatments. Cancer Rep (Hoboken) 2024; 7:e2045. [PMID: 38522008 PMCID: PMC10961052 DOI: 10.1002/cnr2.2045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 02/07/2024] [Accepted: 03/05/2024] [Indexed: 03/25/2024] Open
Abstract
BACKGROUND Precision healthcare has entered a new era because of the developments in personalized medicine, especially in the diagnosis and treatment of head and neck squamous cell carcinoma (HNSCC). This paper explores the dynamic landscape of personalized medicine as applied to HNSCC, encompassing both current developments and future prospects. RECENT FINDINGS The integration of personalized medicine strategies into HNSCC diagnosis is driven by the utilization of genetic data and biomarkers. Epigenetic biomarkers, which reflect modifications to DNA that can influence gene expression, have emerged as valuable indicators for early detection and risk assessment. Treatment approaches within the personalized medicine framework are equally promising. Immunotherapy, gene silencing, and editing techniques, including RNA interference and CRISPR/Cas9, offer innovative means to modulate gene expression and correct genetic aberrations driving HNSCC. The integration of stem cell research with personalized medicine presents opportunities for tailored regenerative approaches. The synergy between personalized medicine and technological advancements is exemplified by artificial intelligence (AI) and machine learning (ML) applications. These tools empower clinicians to analyze vast datasets, predict patient responses, and optimize treatment strategies with unprecedented accuracy. CONCLUSION The developments and prospects of personalized medicine in HNSCC diagnosis and treatment offer a transformative approach to managing this complex malignancy. By harnessing genetic insights, biomarkers, immunotherapy, gene editing, stem cell therapies, and advanced technologies like AI and ML, personalized medicine holds the key to enhancing patient outcomes and ushering in a new era of precision oncology.
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Affiliation(s)
| | | | | | | | - Kalpani Senevirathna
- Centre for Research in Oral Cancer, Faculty of Dental SciencesUniversity of PeradeniyaKandySri Lanka
| | | | | | - Kehinde Kazeem Kanmodi
- School of DentistryUniversity of RwandaKigaliRwanda
- Faculty of DentistryUniversity of PuthisastraPhnom PenhCambodia
- Cephas Health Research Initiative IncIbadanNigeria
- School of Health and Life SciencesTeesside UniversityMiddlesbroughUK
| | - Ruwan Duminda Jayasinghe
- Centre for Research in Oral Cancer, Faculty of Dental SciencesUniversity of PeradeniyaKandySri Lanka
- Faculty of DentistryUniversity of PuthisastraPhnom PenhCambodia
- School of Health and Life SciencesTeesside UniversityMiddlesbroughUK
- Department of Oral Medicine and Periodontology, Faculty of Dental SciencesUniversity of PeradeniyaKandySri Lanka
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Du J, Yang J, Yang Q, Zhang X, Yuan L, Fu B. Comparison of machine learning models to predict the risk of breast cancer-related lymphedema among breast cancer survivors: a cross-sectional study in China. Front Oncol 2024; 14:1334082. [PMID: 38410115 PMCID: PMC10895296 DOI: 10.3389/fonc.2024.1334082] [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: 11/06/2023] [Accepted: 01/24/2024] [Indexed: 02/28/2024] Open
Abstract
Objective The aim of this study was to develop and validate a series of breast cancer-related lymphoedema risk prediction models using machine learning algorithms for early identification of high-risk individuals to reduce the incidence of postoperative breast cancer lymphoedema. Methods This was a retrospective study conducted from January 2012 to July 2022 in a tertiary oncology hospital. Subsequent to the collection of clinical data, variables with predictive capacity for breast cancer-related lymphoedema (BCRL) were subjected to scrutiny utilizing the Least Absolute Shrinkage and Selection Operator (LASSO) technique. The entire dataset underwent a randomized partition into training and test subsets, adhering to a 7:3 distribution. Nine classification models were developed, and the model performance was evaluated based on accuracy, sensitivity, specificity, recall, precision, F-score, and area under curve (AUC) of the ROC curve. Ultimately, the selection of the optimal model hinged upon the AUC value. Grid search and 10-fold cross-validation was used to determine the best parameter setting for each algorithm. Results A total of 670 patients were investigated, of which 469 were in the modeling group and 201 in the validation group. A total of 174 had BCRL (25.97%). The LASSO regression model screened for the 13 features most valuable in predicting BCRL. The range of each metric in the test set for the nine models was, in order: accuracy (0.75-0.84), sensitivity (0.50-0.79), specificity (0.79-0.93), recall (0.50-0.79), precision (0.51-0.70), F score (0.56-0.69), and AUC value (0.71-0.87). Overall, LR achieved the best performance in terms of accuracy (0.81), precision (0.60), sensitivity (0.79), specificity (0.82), recall (0.79), F-score (0.68), and AUC value (0.87) for predicting BCRL. Conclusion The study established that the constructed logistic regression (LR) model exhibits a more favorable amalgamation of accuracy, sensitivity, specificity, recall, and AUC value. This configuration adeptly discerns patients who are at an elevated risk of BCRL. Consequently, this precise identification equips nurses with the means to undertake timely and tailored interventions, thus averting the onset of BCRL.
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Affiliation(s)
- Jiali Du
- Department of Breast Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Jing Yang
- Department of Breast Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Qing Yang
- Department of Nursing, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Xin Zhang
- Department of Breast Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Ling Yuan
- Department of Breast Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
| | - Bing Fu
- Department of Breast Surgery, Sichuan Clinical Research Center for Cancer, Sichuan Cancer Hospital & Institute, Sichuan Cancer Center, Affiliated Cancer Hospital of University of Electronic Science and Technology of China, Chengdu, China
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Mangion A, Ivasic B, Piller N. The Utilization of e-Health in Lymphedema Care: A Narrative Review. Telemed J E Health 2024; 30:331-340. [PMID: 37527411 DOI: 10.1089/tmj.2023.0122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2023] Open
Abstract
Background: Electronic health (e-Health), refers to technologies that can be utilized to enhance patient care as well as collect and share health information. e-Health comprises several umbrella terms, including telehealth, mobile health, e-Health, wearables, and artificial intelligence. The types of e-Health technologies being utilized in lymphedema (LE) care are unknown. Method: In this narrative review, a search of published research on the utilization of e-Health technologies in LE-related care was conducted. Results: Five different types of e-Health modalities were found (robotics, artificial intelligence, electronic medical records, smart wearable devices, and instructive online information) spanning 14 use cases and 4 phases of care (preventative, diagnostic, assessment, and treatment phases). Broad e-Health utilization examples were found including robotic-assisted surgery to reduce the likelihood of LE after lymphadenectomy, machine learning to predict patients at risk of filarial-related LE, and a novel wearable device prototype designed to provide lymphatic drainage. Conclusions: e-Health has reported merit in the prevention, diagnoses, assessment, and treatment of LE with utilization demonstrating cutting edge applicability of e-Health for achieving optimal patient care and outcomes. As technology continues to advance, additional research into the utilization of e-Health in LE care is warranted.
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Affiliation(s)
- Andrea Mangion
- Lymphoedema Clinical Research Unit, Flinders University, Adelaide, Australia
| | - Bruno Ivasic
- Lymphoedema Clinical Research Unit, Flinders University, Adelaide, Australia
| | - Neil Piller
- Lymphoedema Clinical Research Unit, Flinders University, Adelaide, Australia
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Trinh XT, Chien PN, Long NV, Van Anh LT, Giang NN, Nam SY, Myung Y. Development of predictive models for lymphedema by using blood tests and therapy data. Sci Rep 2023; 13:19720. [PMID: 37957217 PMCID: PMC10643602 DOI: 10.1038/s41598-023-46567-1] [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: 05/15/2023] [Accepted: 11/02/2023] [Indexed: 11/15/2023] Open
Abstract
Lymphedema is a disease that refers to tissue swelling caused by an accumulation of protein-rich fluid that is usually drained through the lymphatic system. Detection of lymphedema is often based on expensive diagnoses such as bioimpedance spectroscopy, shear wave elastography, computed tomography, etc. In current machine learning models for lymphedema prediction, reliance on observable symptoms reported by patients introduces the possibility of errors in patient-input data. Moreover, these symptoms are often absent during the initial stages of lymphedema, creating challenges in its early detection. Identifying lymphedema before these observable symptoms manifest would greatly benefit patients by potentially minimizing the discomfort caused by these symptoms. In this study, we propose to use new data, such as complete blood count, serum, and therapy data, to develop predictive models for lymphedema. This approach aims to compensate for the limitations of using only observable symptoms data. We collected data from 2137 patients, including 356 patients with lymphedema and 1781 patients without lymphedema, with the lymphedema status of each patient confirmed by clinicians. The data for each patient included: (1) a complete blood count (CBC) test, (2) a serum test, and (3) therapy information. We used various machine learning algorithms (i.e. random forest, gradient boosting, decision tree, logistic regression, and artificial neural network) to develop predictive models on the training dataset (i.e. 80% of the data) and evaluated the models on the external validation dataset (i.e. 20% of the data). After selecting the best predictive models, we created a web application to aid medical doctors and clinicians in the rapid screening of lymphedema patients. A dataset of 2137 patients was assembled from Seoul National University Bundang Hospital. Predictive models based on the random forest algorithm exhibited satisfactory performance (balanced accuracy = 87.0 ± 0.7%, sensitivity = 84.3 ± 0.6%, specificity = 89.1 ± 1.5%, precision = 97.4 ± 0.7%, F1 score = 90.4 ± 0.4%, and AUC = 0.931 ± 0.007). We developed a web application to facilitate the swift screening of lymphedema among medical practitioners: https://snubhtxt.shinyapps.io/SNUBH_Lymphedema . Our study introduces a novel tool for the early detection of lymphedema and establishes the foundation for future investigations into predicting different stages of the condition.
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Affiliation(s)
- Xuan-Tung Trinh
- Department of Plastic and Reconstructive Surgery, Seoul National University Bundang Hospital, Seongnam, 13620, Republic of Korea
| | - Pham Ngoc Chien
- Department of Plastic and Reconstructive Surgery, Seoul National University Bundang Hospital, Seongnam, 13620, Republic of Korea
| | - Nguyen-Van Long
- Department of Plastic and Reconstructive Surgery, Seoul National University Bundang Hospital, Seongnam, 13620, Republic of Korea
| | - Le Thi Van Anh
- Department of Plastic and Reconstructive Surgery, Seoul National University Bundang Hospital, Seongnam, 13620, Republic of Korea
| | - Nguyen Ngan Giang
- Department of Plastic and Reconstructive Surgery, Seoul National University Bundang Hospital, Seongnam, 13620, Republic of Korea
- Department of Medical Device Development, College of Medicine, Seoul National University, Seoul, 03080, Republic of Korea
| | - Sun-Young Nam
- Department of Plastic and Reconstructive Surgery, Seoul National University Bundang Hospital, Seongnam, 13620, Republic of Korea.
| | - Yujin Myung
- Department of Plastic and Reconstructive Surgery, Seoul National University Bundang Hospital, Seongnam, 13620, Republic of Korea.
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Nomura Y, Hoshiyama M, Akita S, Naganishi H, Zenbutsu S, Matsuoka A, Ohnishi T, Haneishi H, Mitsukawa N. Computer-aided diagnosis for screening of lower extremity lymphedema in pelvic computed tomography images using deep learning. Sci Rep 2023; 13:16214. [PMID: 37758908 PMCID: PMC10533488 DOI: 10.1038/s41598-023-43503-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2023] [Accepted: 09/25/2023] [Indexed: 09/29/2023] Open
Abstract
Lower extremity lymphedema (LEL) is a common complication after gynecological cancer treatment, which significantly reduces the quality of life. While early diagnosis and intervention can prevent severe complications, there is currently no consensus on the optimal screening strategy for postoperative LEL. In this study, we developed a computer-aided diagnosis (CAD) software for LEL screening in pelvic computed tomography (CT) images using deep learning. A total of 431 pelvic CT scans from 154 gynecological cancer patients were used for this study. We employed ResNet-18, ResNet-34, and ResNet-50 models as the convolutional neural network (CNN) architecture. The input image for the CNN model used a single CT image at the greater trochanter level. Fat-enhanced images were created and used as input to improve classification performance. Receiver operating characteristic analysis was used to evaluate our method. The ResNet-34 model with fat-enhanced images achieved the highest area under the curve of 0.967 and an accuracy of 92.9%. Our CAD software enables LEL diagnosis from a single CT image, demonstrating the feasibility of LEL screening only on CT images after gynecologic cancer treatment. To increase the usefulness of our CAD software, we plan to validate it using external datasets.
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Affiliation(s)
- Yukihiro Nomura
- Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-Cho, Inage-ku, Chiba, 263-8522, Japan.
| | - Masato Hoshiyama
- Department of Medical Engineering, Faculty of Engineering, Chiba University, 1-33 Yayoi-Cho, Inage-ku, Chiba, 263-8522, Japan
| | - Shinsuke Akita
- Department of Plastic, Reconstructive and Aesthetic Surgery, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
| | - Hiroki Naganishi
- Department of Plastic Surgery, Saiseikai Yokohamashi Nanbu Hospital, 3-2-10 Konandai, Konan-ku, Yokohama City, Kanagawa, 234-0054, Japan
| | - Satoki Zenbutsu
- Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-Cho, Inage-ku, Chiba, 263-8522, Japan
| | - Ayumu Matsuoka
- Department of Gynecology and Maternal-Fetal Medicine, Chiba University Hospital, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
| | - Takashi Ohnishi
- Department of Pathology and Laboratory Medicine, Memorial Sloan Kettering Cancer Center, 1133 York Avenue, New York, NY, 10065, USA
| | - Hideaki Haneishi
- Center for Frontier Medical Engineering, Chiba University, 1-33 Yayoi-Cho, Inage-ku, Chiba, 263-8522, Japan
| | - Nobuyuki Mitsukawa
- Department of Plastic, Reconstructive and Aesthetic Surgery, Graduate School of Medicine, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba, 260-8670, Japan
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蒋 慧, 熊 代, 张 瑞. [Application Status and Prospects of Precision Nursing Under the Concept of Enhanced Recovery After Surgery]. SICHUAN DA XUE XUE BAO. YI XUE BAN = JOURNAL OF SICHUAN UNIVERSITY. MEDICAL SCIENCE EDITION 2023; 54:712-716. [PMID: 37545061 PMCID: PMC10442622 DOI: 10.12182/20230760503] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Indexed: 08/08/2023]
Abstract
With the development of enhanced recovery after surgery (ERAS), major breakthroughs have been made in this field of study. However, the research fields still need to be continuously expanded to meet the needs of patients. The concept of precision therapy is widely applied in the field of nursing. Under the concept of ERAS, practical studies of applying precision nursing for the perioperative period have already been conducted, exploring such issues as precision nursing assessment, precision nursing intervention design, precision risk prediction model, and information technology to assist precision nursing practice. Research findings have preliminarily validated the safety and effectiveness of applying precision nursing for ERAS in the perioperative period. Herein, we reviewed the reported findings of relevant research published in recent years and identified the following problems in the implementation of precision nursing under the ERAS concept, a lack of implementation standards, challenges concerning the the role of nurses, a lack of high-quality research evidence in the existing literature, and a relevant big data processing platform that China does not have and therefore cannot carry out data sharing, integration, mining, and utilization. We also made suggestions for effective improvement and discussed research prospects. In the future, multidisciplinary collaboration, translational medical research, and the development of various innovative tools are to be strengthened to help improve the quality and effectiveness of nursing care. We hope to provide reference for improving the scientific and targeted implementation of precision nursing for ERAS in the perioperative period.
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Affiliation(s)
- 慧琴 蒋
- 广东省人民医院(广东省医学科学院) (广州 510080)Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China
| | - 代兰 熊
- 广东省人民医院(广东省医学科学院) (广州 510080)Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China
| | - 瑞英 张
- 广东省人民医院(广东省医学科学院) (广州 510080)Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Guangzhou 510080, China
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Bianchi LMG, Irmici G, Cè M, D'Ascoli E, Della Pepa G, Di Vita F, Casati O, Soresina M, Menozzi A, Khenkina N, Cellina M. Diagnosis and Treatment of Post-Prostatectomy Lymphedema: What's New? Curr Oncol 2023; 30:4512-4526. [PMID: 37232799 DOI: 10.3390/curroncol30050341] [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: 03/14/2023] [Revised: 04/24/2023] [Accepted: 04/24/2023] [Indexed: 05/27/2023] Open
Abstract
Lymphedema is a chronic progressive disorder that significantly compromises patients' quality of life. In Western countries, it often results from cancer treatment, as in the case of post-radical prostatectomy lymphedema, where it can affect up to 20% of patients, with a significant disease burden. Traditionally, diagnosis, assessment of severity, and management of disease have relied on clinical assessment. In this landscape, physical and conservative treatments, including bandages and lymphatic drainage have shown limited results. Recent advances in imaging technology are revolutionizing the approach to this disorder: magnetic resonance imaging has shown satisfactory results in differential diagnosis, quantitative classification of severity, and most appropriate treatment planning. Further innovations in microsurgical techniques, based on the use of indocyanine green to map lymphatic vessels during surgery, have improved the efficacy of secondary LE treatment and led to the development of new surgical approaches. Physiologic surgical interventions, including lymphovenous anastomosis (LVA) and vascularized lymph node transplant (VLNT), are going to face widespread diffusion. A combined approach to microsurgical treatment provides the best results: LVA is effective in promoting lymphatic drainage, bridging VLNT delayed lymphangiogenic and immunological effects in the lymphatic impairment site. Simultaneous VLNT and LVA are safe and effective for patients with both early and advanced stages of post-prostatectomy LE. A new perspective is now represented by the combination of microsurgical treatments with the positioning of nano fibrillar collagen scaffolds (BioBridgeTM) to favor restoring the lymphatic function, allowing for improved and sustained volume reduction. In this narrative review, we proposed an overview of new strategies for diagnosing and treating post-prostatectomy lymphedema to get the most appropriate and successful patient treatment with an overview of the main artificial intelligence applications in the prevention, diagnosis, and management of lymphedema.
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Affiliation(s)
| | - Giovanni Irmici
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy
| | - Maurizio Cè
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy
| | - Elisa D'Ascoli
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy
| | - Gianmarco Della Pepa
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy
| | - Filippo Di Vita
- Postgraduation School in Plastic Surgery, University of Milan, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Omar Casati
- Postgraduation School in Plastic Surgery, University of Milan, Via Festa del Perdono, 7, 20122 Milan, Italy
| | - Massimo Soresina
- Plastic Surgery Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, 20121 Milan, Italy
| | - Andrea Menozzi
- Plastic Surgery Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, 20121 Milan, Italy
| | - Natallia Khenkina
- Postgraduation School in Radiodiagnostics, University of Milan, 20122 Milan, Italy
| | - Michaela Cellina
- Radiology Department, Fatebenefratelli Hospital, ASST Fatebenefratelli Sacco, 20121 Milan, Italy
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12
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Nascimben M, Lippi L, de Sire A, Invernizzi M, Rimondini L. Algorithm-Based Risk Identification in Patients with Breast Cancer-Related Lymphedema: A Cross-Sectional Study. Cancers (Basel) 2023; 15:cancers15020336. [PMID: 36672283 PMCID: PMC9856619 DOI: 10.3390/cancers15020336] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Accepted: 12/27/2022] [Indexed: 01/06/2023] Open
Abstract
Background: Breast cancer-related lymphedema (BCRL) could be one consequence of breast cancer (BC). Although several risk factors have been identified, a predictive algorithm still needs to be made available to determine the patient's risk from an ensemble of clinical variables. Therefore, this study aimed to characterize the risk of BCRL by investigating the characteristics of autogenerated clusters of patients. Methods: The dataset under analysis was a multi-centric data collection of twenty-three clinical features from patients undergoing axillary dissection for BC and presenting BCRL or not. The patients' variables were initially analyzed separately in two low-dimensional embeddings. Afterward, the two models were merged in a bi-dimensional prognostic map, with patients categorized into three clusters using a Gaussian mixture model. Results: The prognostic map represented the medical records of 294 women (mean age: 59.823±12.879 years) grouped into three clusters with a different proportion of subjects affected by BCRL (probability that a patient with BCRL belonged to Cluster A: 5.71%; Cluster B: 71.42%; Cluster C: 22.86%). The investigation evaluated intra- and inter-cluster factors and identified a subset of clinical variables meaningful in determining cluster membership and significantly associated with BCRL biological hazard. Conclusions: The results of this study provide potential insight for precise risk assessment of patients affected by BCRL, with implications in prevention strategies, for instance, focusing the resources on identifying patients at higher risk.
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Affiliation(s)
- Mauro Nascimben
- Center for Translational Research on Autoimmune and Allergic Diseases-CAAD, Department of Health Sciences, Università del Piemonte Orientale “A. Avogadro”, 28100 Novara, Italy
- Enginsoft SpA, 35129 Padua, Italy
- Correspondence:
| | - Lorenzo Lippi
- Physical and Rehabilitative Medicine, Department of Health Sciences, Università del Piemonte Orientale “A. Avogadro”, 28100 Novara, Italy
- Infrastruttura Ricerca Formazione Innovazione (IRFI), Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo, 15121 Alessandria, Italy
| | - Alessandro de Sire
- Physical and Rehabilitative Medicine Unit, Department of Medical and Surgical Sciences, University of Catanzaro “Magna Græcia”, 88100 Catanzaro, Italy
| | - Marco Invernizzi
- Physical and Rehabilitative Medicine, Department of Health Sciences, Università del Piemonte Orientale “A. Avogadro”, 28100 Novara, Italy
- Infrastruttura Ricerca Formazione Innovazione (IRFI), Azienda Ospedaliera SS. Antonio e Biagio e Cesare Arrigo, 15121 Alessandria, Italy
| | - Lia Rimondini
- Center for Translational Research on Autoimmune and Allergic Diseases-CAAD, Department of Health Sciences, Università del Piemonte Orientale “A. Avogadro”, 28100 Novara, Italy
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13
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Soh CL, Shah V, Arjomandi Rad A, Vardanyan R, Zubarevich A, Torabi S, Weymann A, Miller G, Malawana J. Present and future of machine learning in breast surgery: systematic review. Br J Surg 2022; 109:1053-1062. [PMID: 35945894 PMCID: PMC10364755 DOI: 10.1093/bjs/znac224] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Revised: 02/09/2022] [Accepted: 05/30/2022] [Indexed: 08/02/2023]
Abstract
BACKGROUND Machine learning is a set of models and methods that can automatically detect patterns in vast amounts of data, extract information, and use it to perform decision-making under uncertain conditions. The potential of machine learning is significant, and breast surgeons must strive to be informed with up-to-date knowledge and its applications. METHODS A systematic database search of Embase, MEDLINE, the Cochrane database, and Google Scholar, from inception to December 2021, was conducted of original articles that explored the use of machine learning and/or artificial intelligence in breast surgery in EMBASE, MEDLINE, Cochrane database and Google Scholar. RESULTS The search yielded 477 articles, of which 14 studies were included in this review, featuring 73 847 patients. Four main areas of machine learning application were identified: predictive modelling of surgical outcomes; breast imaging-based context; screening and triaging of patients with breast cancer; and as network utility for detection. There is evident value of machine learning in preoperative planning and in providing information for surgery both in a cancer and an aesthetic context. Machine learning outperformed traditional statistical modelling in all studies for predicting mortality, morbidity, and quality of life outcomes. Machine learning patterns and associations could support planning, anatomical visualization, and surgical navigation. CONCLUSION Machine learning demonstrated promising applications for improving breast surgery outcomes and patient-centred care. Neveretheless, there remain important limitations and ethical concerns relating to implementing artificial intelligence into everyday surgical practices.
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Affiliation(s)
- Chien Lin Soh
- School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Viraj Shah
- Department of Medicine, Faculty of Medicine, Imperial College London, London, UK
| | - Arian Arjomandi Rad
- Correspondence to: Arian Arjomandi Rad, Imperial College London, Department of Medicine, Faculty of Medicine, South Kensington Campus, Sir Alexander Fleming Building, London SW7 2AZ, UK (e-mail: )
| | - Robert Vardanyan
- Department of Medicine, Faculty of Medicine, Imperial College London, London, UK
| | - Alina Zubarevich
- Department of Thoracic and Cardiovascular Surgery, West German Heart and Vascular Center Essen, University Hospital of Essen, University Duisburg-Essen, Essen, Germany
| | - Saeed Torabi
- Department of Anesthesiology and Intensive Care Medicine, University Hospital of Cologne, Cologne, Germany
| | - Alexander Weymann
- Department of Thoracic and Cardiovascular Surgery, West German Heart and Vascular Center Essen, University Hospital of Essen, University Duisburg-Essen, Essen, Germany
| | - George Miller
- Research Unit, The Healthcare Leadership Academy, London, UK
- Centre for Digital Health and Education Research (CoDHER), University of Central Lancashire Medical School, Preston, UK
| | - Johann Malawana
- Research Unit, The Healthcare Leadership Academy, London, UK
- Centre for Digital Health and Education Research (CoDHER), University of Central Lancashire Medical School, Preston, UK
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Lin Q, Yang T, Yongmei J, Die YM. Prediction models for breast cancer-related lymphedema: a systematic review and critical appraisal. Syst Rev 2022; 11:217. [PMID: 36229876 PMCID: PMC9559764 DOI: 10.1186/s13643-022-02084-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2022] [Accepted: 09/28/2022] [Indexed: 12/24/2022] Open
Abstract
PURPOSE The development of risk prediction models for breast cancer lymphedema is increasing, but few studies focus on the quality of the model and its application. Therefore, this study aimed to systematically review and critically evaluate prediction models developed to predict breast cancer-related lymphedema. METHODS PubMed, Web of Science, Embase, MEDLINE, CNKI, Wang Fang DATA, Vip Database, and SinoMed were searched for studies published from 1 January 2000 to 1 June 2021. And it will be re-run before the final analysis. Two independent investigators will undertake the literature search and screening, and discrepancies will be resolved by another investigator. The Prediction model Risk Of Bias Assessment Tool will be used to assess the prediction models' risk of bias and applicability. RESULTS Seventeen studies were included in the systematic review, including 7 counties, of which 6 were prospective studies, only 7 models were validation studies, and 4 models were externally validated. The area under the curve of 17 models was 0.680~0.908. All studies had a high risk of bias, primarily due to the participants, outcome, and analysis. The most common predictors included body mass index, radiotherapy, chemotherapy, and axillary lymph node dissection. CONCLUSIONS The predictive factors' strength, external validation, and clinical application of the breast cancer lymphedema risk prediction model still need further research. Healthcare workers should choose prediction models in clinical practice judiciously. SYSTEMATIC REVIEW REGISTRATION PROSPERO CRD42021258832.
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Affiliation(s)
- Qiu Lin
- Department of Nursing, 7th Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Tong Yang
- Department of Nail-Breast Hernia Surgery, 7th Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Jin Yongmei
- Department of Nursing, 7th Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China.
| | - Ye Mao Die
- Department of Nursing, 7th Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China
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15
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Pel E, Engelberts I, Schermer M. Diversity of interpretations of the concept "patient-centered care for breast cancer patients"; a scoping review of current literature. J Eval Clin Pract 2022; 28:773-793. [PMID: 34002460 PMCID: PMC9788211 DOI: 10.1111/jep.13584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 04/23/2021] [Accepted: 04/27/2021] [Indexed: 12/30/2022]
Abstract
RATIONALE, AIMS AND OBJECTIVES Patient-centered care is considered a vital component of good quality care for breast cancer patients. Nevertheless, the implementation of this valuable concept in clinical practice appears to be difficult. The goal of this study is to bridge the gap between theoretical elaboration of "patient-centered care" and clinical practice. To that purpose, a scoping analysis was performed of the application of the term "patient-centered care in breast cancer treatment" in present-day literature. METHOD For data-extraction, a literature search was performed extracting references that were published in 2018 and included the terms "patient-centered care" and "breast cancer". The articles were systematically traced for answers to the following three questions: "What is patient-centered care?", "Why perform patient-centered care?", and "How to realize patient-centered care?". For the content analysis, these answers were coded and assembled into meaningful clusters until separate themes arose which concur with various interpretations of the term "patient-centered care". RESULTS A total of 60 publications were retained for analysis. Traced answers to the three questions "what", "why", and "how" varied considerably in recent literature concerning breast cancer treatment. Despite the inconsistent use of the term "patient-centered care," we did not find any critical consideration about the nature of the concept, regardless of the applied interpretation. Interventions that are supposed to contribute to the heterogeneous concept of patient-centered care as such, seem to be judged desirable, virtually without empirical justification. CONCLUSIONS We propose, contrary to previous efforts to define "patient-centered care" more accurately, to embrace the heterogeneity of the concept and apply "patient-centered care" as an umbrella-term for all healthcare that intends to contribute to the acknowledgement of the person in the patient. For the justification of measures to realize patient-centered care for breast cancer patients, instead of a mere contribution to the abstract concept, we insist on the demonstration of desirable real-world effects.
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Affiliation(s)
- Elise Pel
- Department of Medical Ethics, Philosophy and History of MedicineErasmus MC, University Medical Center of RotterdamRotterdamThe Netherlands
| | - Ingeborg Engelberts
- Department of Medical Ethics, Philosophy and History of MedicineErasmus MC, University Medical Center of RotterdamRotterdamThe Netherlands
- The Franciscus Breast Clinic, Department of SurgeryFranciscus Gasthuis & VlietlandSchiedamThe Netherlands
| | - Maartje Schermer
- Department of Medical Ethics, Philosophy and History of MedicineErasmus MC, University Medical Center of RotterdamRotterdamThe Netherlands
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Meng L, Zhang R, Fa L, Zhang L, Wang L, Shao G. ATRX status in patients with gliomas: Radiomics analysis. Medicine (Baltimore) 2022; 101:e30189. [PMID: 36123880 PMCID: PMC9478307 DOI: 10.1097/md.0000000000030189] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
Abstract
MATERIAL AND METHODS A cohort of 123 patients diagnosed with gliomas (World Health Organization grades II-IV) who underwent surgery and was treated at our center between January 2016 and July 2020, was enrolled in this retrospective study. Radiomics features were extracted from MR T1WI, T2WI, T2FLAIR, CE-T1WI, and ADC images. Patients were randomly split into training and validation sets at a ratio of 4:1. A radiomics signature was constructed using the least absolute shrinkage and selection operator (LASSO) to train the SVM model using the training set. The prediction accuracy and area under curve and other evaluation indexes were used to explore the performance of the model established in this study for predicting the ATRX mutation state. RESULTS Fifteen radiomic features were selected to generate an ATRX-associated radiomic signature using the LASSO logistic regression model. The area under curve for ATRX mutation (ATRX(-)) on training set was 0.93 (95% confidence interval [CI]: 0.87-1.0), with the sensitivity, specificity and accuracy being 0.91, 0.82 and 0.88, while on the validation set were 0.84 (95% CI: 0.63-0.91), with the sensitivity, specificity and accuracy of 0.73, 0.86, and 0.79, respectively. CONCLUSIONS These results indicate that radiomic features derived from preoperative MRI facilitat efficient prediction of ATRX status in gliomas, thus providing a novel evaluation method for noninvasive imaging biomarkers.
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Affiliation(s)
- Linlin Meng
- Department of Radiology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Ran Zhang
- Huiying Medical Technology Co. Ltd, Beijing, China
| | - Liangguo Fa
- Department of Radiology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Lulu Zhang
- Department of Pathology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Linlin Wang
- Department of Radiology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Guangrui Shao
- Department of Radiology, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- * Correspondence: Guangrui Shao, Department of Radiology, The Second Hospital, Cheeloo College of Medicine, Shandong University, No. 247 Beiyuan Road, Jinan, Shandong (e-mail: )
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Yaghoobi Notash A, Yaghoobi Notash A, Omidi Z, Haghighat S. Prediction of lymphedema occurrence in patients with breast cancer using the optimized combination of ensemble learning algorithm and feature selection. BMC Med Inform Decis Mak 2022; 22:195. [PMID: 35879760 PMCID: PMC9310496 DOI: 10.1186/s12911-022-01937-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Accepted: 07/15/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Breast cancer-related lymphedema is one of the most important complications that adversely affect patients' quality of life. Lymphedema can be managed if its risk factors are known and can be modified. This study aimed to select an appropriate model to predict the risk of lymphedema and determine the factors affecting lymphedema. METHOD This study was conducted on data of 970 breast cancer patients with lymphedema referred to a lymphedema clinic. This study was designed in two phases: developing an appropriate model to predict the risk of lymphedema and identifying the risk factors. The first phase included data preprocessing, optimizing feature selection for each base learner by the Genetic algorithm, optimizing the combined ensemble learning method, and estimating fitness function for evaluating an appropriate model. In the second phase, the influential variables were assessed and introduced based on the average number of variables in the output of the proposed algorithm. RESULT Once the sensitivity and accuracy of the algorithms were evaluated and compared, the Support Vector Machine algorithm showed the highest sensitivity and was found to be the superior model for predicting lymphedema. Meanwhile, the combined method had an accuracy coefficient of 91%. The extracted significant features in the proposed model were the number of lymph nodes to the number of removed lymph nodes ratio (68%), feeling of heaviness (67%), limited range of motion in the affected limb (65%), the number of the removed lymph nodes ( 64%), receiving radiotherapy (63%), misalignment of the dominant and the involved limb (62%), presence of fibrotic tissue (62%), type of surgery (62%), tingling sensation (62%), the number of the involved lymph nodes (61%), body mass index (61%), the number of chemotherapy sessions (60%), age (58%), limb injury (53%), chemotherapy regimen (53%), and occupation (50%). CONCLUSION Applying a combination of ensemble learning approach with the selected classification algorithms, feature selection, and optimization by Genetic algorithm, Lymphedema can be predicted with appropriate accuracy. Developing applications by effective variables to determine the risk of lymphedema can help lymphedema clinics choose the proper preventive and therapeutic method.
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Affiliation(s)
- Anaram Yaghoobi Notash
- The Computer Engineering Department, Rasht Branch, Islamic Azad University, Rasht, Iran
- Shariati Hospital, Tehran University of Medical Science (TUMS), Tehran, Iran
| | | | - Zahra Omidi
- Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran
| | - Shahpar Haghighat
- Breast Cancer Research Center, Motamed Cancer Institute, ACECR, Tehran, Iran.
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Artificial intelligence and lymphedema: State of the art. J Clin Transl Res 2022; 8:234-242. [PMID: 35813896 PMCID: PMC9260343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Revised: 04/30/2022] [Accepted: 05/01/2022] [Indexed: 11/01/2022] Open
Abstract
Background Lymphedema practice is facing many challenges. Some of these challenges include eradication of tropical lymphedema, preclinical diagnosis of cancer-related lymphedema, and delivery of appropriate individualized care. The past two decades have witnessed an increasing implementation of artificial intelligence (AI) in health-care services. The nature of the challenges facing the lymphedema practice is suitable for AI applications. Aim The aim of this study was to explore the current AI applications in lymphedema prevention, diagnosis, and management and investigate the potential future applications. Methods and Results Four databases were searched: PubMed, Scopus, Web of Science, and EMBASE. We used the Preferred Reporting Items for Systematic Reviews and Meta-Analysis as our basis of organization. Our analysis showed that several domains of AI, including machine learning (ML), fuzzy models, deep learning, and robotics, were successfully implemented in lymphedema practice. ML can guide the eradication campaigns of tropical lymphedema by estimating disease prevalence and mapping the risk areas. Robotic-assisted surgery for gynecological cancer was associated with a lower risk for the lower limb lymphedema. Several feasible models were described for the early detection and diagnosis of lymphedema. The proposed models are more accurate, sensitive, and specific than current methods in practice. ML was also used to guide and monitor patients during the rehabilitation exercises. Conclusion AI offers a variety of solutions to the most challenging problems in lymphedema practice. Further, implementation into the practice can revolutionize many aspects of lymphedema prevention, diagnosis, and management. Relevance to Patients Lymphedema is a chronic debilitating disease that is affecting millions of patients. Developing new modalities for prevention, early diagnosis, and treatment are critical to improve the outcomes. AI offers a variety of solutions for some of the complexities of lymphedema management. In this systematic review, we summarize and discuss the latest AI advances in lymphedema practice.
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Wu X, Guan Q, Cheng ASK, Guan C, Su Y, Jiang J, Wang B, Zeng L, Zeng Y. Comparison of machine learning models for predicting the risk of breast cancer-related lymphedema in Chinese women. Asia Pac J Oncol Nurs 2022; 9:100101. [PMID: 36276882 PMCID: PMC9579303 DOI: 10.1016/j.apjon.2022.100101] [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: 01/30/2022] [Accepted: 05/30/2022] [Indexed: 11/21/2022] Open
Abstract
Objective Predictive models for the occurrence of cancer symptoms by using machine learning (ML) algorithms could be used to aid clinical decision-making in order to enhance the quality of cancer care. This study aimed to develop and validate a selection of classification models that used ML algorithms to predict the occurrence of breast cancer-related lymphedema (BCRL) among Chinese women. Methods This was a retrospective cohort study of consecutive cases that had been diagnosed with breast cancer, stages I-IV. Forty-eight variables were grouped into five feature sets. Five classification models with ML algorithms were developed, and the models' performance and the variables’ relative importance were assessed accordingly. Results Of 370 eligible female participants, 91 had BCRL (24.6%). The mean age of this study sample was 49.89 (SD = 7.45). All participants had had breast cancer surgery, and more than half of them had had a modified radical mastectomy (n = 206, 55.5%). The mean follow-up time after breast cancer surgery was 28.73 months (SD = 11.71). Most of the tumors were either stage I (n = 49, 31.2%) or stage II (n = 252, 68.1%). More than half of the sample had had postoperative chemotherapy (n = 227, 61.4%). Overall, the logistic regression model achieved the best performance in terms of accuracy (91.6%), precision (82.1%), and recall (91.4%) for BCRL. Although this study included 48 predicting variables, we found that the five models required only 22 variables to achieve predictive performance. The most important variable was the number of positive lymph nodes, followed in descending order by the BCRL occurring on the same side as the surgery, a history of sentinel lymph node biopsy, a dietary preference for meat and fried food, and an exercise frequency of less than three times per week. These factors were the most influential predictors for enhancing the ML models’ performance. Conclusions This study found that in the ML training dataset, the multilayer perceptron model and the logistic regression model were the best discrimination models for predicting the outcome of BCRL, and the k-nearest neighbors and support vector machine models demonstrated good calibration performance in the ML validation dataset. Future research will need to use large-sample datasets to establish a more robust ML model for predicting BCRL deeply and reliably.
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Coriddi M, Kim L, McGrath L, Encarnacion E, Brereton N, Shen Y, Barrio AV, Mehrara B, Dayan JH. Accuracy, Sensitivity, and Specificity of the LLIS and ULL27 in Detecting Breast Cancer-Related Lymphedema. Ann Surg Oncol 2022; 29:438-445. [PMID: 34264409 PMCID: PMC8958312 DOI: 10.1245/s10434-021-10469-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Accepted: 07/01/2021] [Indexed: 01/03/2023]
Abstract
INTRODUCTION Breast cancer-related lymphedema occurs in up to 30% of women following axillary lymph node dissection (ALND) and less commonly following sentinel lymph node biopsy. To quantify disability in these patients, patient-reported outcome measures (PROMs) have proven useful; however, given the overlap of symptoms between ALND and lymphedema, examination of their accuracy, sensitivity, and specificity in detecting lymphedema in breast cancer patients undergoing ALND is needed. METHODS The Lymphedema Life Impact Scale (LLIS) and the Upper Limb Lymphedema 27 scale (ULL27) were administered to patients who had undergone ALND at least 2 years prior and either did or did not develop lymphedema. Survey responses and the degree of disability were compared to generate receiver operator characteristic (ROC) curves, and the sensitivity and specificity of PROMs to diagnose lymphedema were analyzed. RESULTS Both PROMs were highly accurate, sensitive, and specific for detecting lymphedema. The LLIS had an accuracy of 97%, sensitivity of 100%, and specificity of 84.8% at a cutoff of ≥ 5.88 overall percent impairment score (higher scores indicate worse disability). The ULL27 had an accuracy of 93%, sensitivity of 88.6%, and specificity of 90.9% at a cutoff of ≤ 83.3 global score (lower scores indicate worse disability). CONCLUSIONS The LLIS and the ULL27 appear to be highly specific for lymphedema and capable of differentiating it from symptoms resulting from ALND alone. Our findings suggest that use of these questionnaires with a threshold may be effective for diagnosing lymphedema, potentially reducing the need for frequent clinic visits and time-consuming measurements.
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Affiliation(s)
- Michelle Coriddi
- Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Leslie Kim
- Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Leslie McGrath
- Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Elizabeth Encarnacion
- Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Nicholas Brereton
- Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Yin Shen
- Department of Epidemiology-Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Andrea V. Barrio
- Breast Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Babak Mehrara
- Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
| | - Joseph H. Dayan
- Plastic and Reconstructive Surgery Service, Department of Surgery, Memorial Sloan Kettering Cancer Center, New York, NY
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Wei X, Lu Q, Jin S, Li F, Zhao Q, Cui Y, Jin S, Cao Y, Fu MR. Developing and validating a prediction model for lymphedema detection in breast cancer survivors. Eur J Oncol Nurs 2021; 54:102023. [PMID: 34500318 DOI: 10.1016/j.ejon.2021.102023] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Revised: 07/30/2021] [Accepted: 08/25/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE Early detection and intervention of lymphedema is essential for improving the quality of life of breast cancer survivors. Previous studies have shown that patients have symptoms such as arm tightness and arm heaviness before experiencing obvious limb swelling. Thus, this study aimed to develop a symptom-warning model for the early detection of breast cancer-related lymphedema. METHODS A cross-sectional study was conducted at a tertiary hospital in Beijing between April 2017 and December 2018. A total of 24 lymphedema-associated symptoms were identified as candidate predictors. Circumferential measurements were used to diagnose lymphedema. The data were randomly split into training and validation sets with a 7:3 ratio to derive and evaluate six machine learning models. Both the discrimination and calibration of each model were assessed on the validation set. RESULTS A total of 533 patients were included in the study. The logistic regression model showed the best performance for early detection of lymphedema, with AUC = 0.889 (0.840-0.938), sensitivity = 0.771, specificity = 0.883, accuracy = 0.825, and Brier scores = 0.141. Calibration was also acceptable. It has been deployed as an open-access web application, allowing users to estimate the probability of lymphedema individually in real time. The application can be found at https://apredictiontoolforlymphedema.shinyapps.io/dynnomapp/. CONCLUSION The symptom-warning model developed by logistic regression performed well in the early detection of lymphedema. Integrating this model into an open-access web application is beneficial to patients and healthcare providers to monitor lymphedema status in real-time.
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Affiliation(s)
- Xiaoxia Wei
- Division of Medical & Surgical Nursing, School of Nursing, Peking University, 100191, Beijing, China
| | - Qian Lu
- Division of Medical & Surgical Nursing, School of Nursing, Peking University, 100191, Beijing, China.
| | - Sanli Jin
- Division of Medical & Surgical Nursing, School of Nursing, Peking University, 100191, Beijing, China
| | - Fenglian Li
- Division of Medical & Surgical Nursing, School of Nursing, Peking University, 100191, Beijing, China
| | - Quanping Zhao
- Department of Breast Surgery, People's Hospital, Peking University, 100044, Beijing, China
| | - Ying Cui
- Department of Breast Surgery, People's Hospital, Peking University, 100044, Beijing, China
| | - Shuai Jin
- Division of Medical & Surgical Nursing, School of Nursing, Peking University, 100191, Beijing, China
| | - Yiwei Cao
- Division of Medical & Surgical Nursing, School of Nursing, Peking University, 100191, Beijing, China
| | - Mei R Fu
- Rutgers, The State University of New Jersey School of Nursing, Camden, USA
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22
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Findings From a Provider-Led, Mindfulness-Based, Internet-Streamed Yoga Video Addressing the Psychological Outcomes of Breast Cancer Survivors. Holist Nurs Pract 2021; 35:281-289. [PMID: 34407026 DOI: 10.1097/hnp.0000000000000465] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
The aim of this study was to explore the psychological outcomes of a mindfulness-based Internet-streamed yoga video in breast cancer survivors. A one-group, repeated-measures, purposive sample using a directed qualitative descriptive and convergent mixed-methods approach was used. Participants were recruited from breast oncology practices across 2 settings in the northeastern United States in April 2019. Education about the video was provided, and the link to the video was sent to participants. Demographic information, Knowing Participation in Change Short Form (KPCSF), Short Warwick-Edinburgh Mental Well-being Scale (WEMWBS), and the Generalized Anxiety Distress Scale (GAD-7) were obtained at baseline and at 2 and 4 weeks. A semistructured interview was conducted at 4 weeks. Thirty-five women (mean age = 56 years) participated. A one-group, repeated-measures analysis of variance indicated statistically significant changes occurred in all measures between week 0 and week 4: decreased GAD (t = -2.97, P = .004), improved WEMWBS (t = 2.52, P = .008), and increased KPC (t = 2.99, P = .004). Qualitative findings suggest the overall experience of the video was positive and the women would recommend its use to others. Improvements in all psychological measures were achieved with video use. Findings indicate an improvement in psychological measures and support the theory of Knowing Participation in Change. This work further contributes to accessible, flexible interventions available through the Internet and/or mobile applications aimed at improving breast cancer survivorship.
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Gursen C, Dylke ES, Moloney N, Meeus M, De Vrieze T, Devoogdt N, De Groef A. Self-reported signs and symptoms of secondary upper limb lymphoedema related to breast cancer treatment: Systematic review. Eur J Cancer Care (Engl) 2021; 30:e13440. [PMID: 33733550 DOI: 10.1111/ecc.13440] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 01/15/2021] [Accepted: 02/25/2021] [Indexed: 01/11/2023]
Abstract
INTRODUCTION Breast cancer survivors with secondary upper limb lymphoedema (ULL) may report a wide range of self-reported symptoms. At the moment, no overview of ULL-specific symptoms is available. The first aim, therefore, was to compare the prevalence rates of self-reported signs and symptoms in people with and without secondary ULL due to breast cancer treatment. The second aim was to determine whether symptoms of lymphoedema could be predictive for the development of ULL. The third aim was to describe the association between the presence/severity of symptoms and the presence/severity of ULL. METHODS A systematic search was conducted in Medline, Scopus, CINAHL and EMBASE databases, with key words related to breast cancer, symptoms and ULL. RESULTS Twenty-nine articles were eligible. The most frequently reported signs and symptoms were swelling (80.9%) and heaviness (66.7%) in the ULL group and tenderness (37%) and numbness (27%) in the non-ULL group. Perceived larger arm size, as well as feelings of arm tightness, stiffness, puffiness, pain, sensory disturbances and functional changes were predictive for the development of ULL. Moderate correlations were found between the presence of swelling, firmness in the past year and tightness now and severity of ULL. There was also moderate correlation between the presence of swelling and heaviness now and the presence of ULL. CONCLUSIONS Swelling and heaviness are the most commonly reported symptoms in patients with ULL. The presences of these two symptoms are moderately correlated with the presence and/or severity of ULL. Although limited information regarding the predictive self-reported symptoms for the development of ULL was found. Further research with standardised definitions of ULL and validated questionnaires for self-reported signs and symptoms are needed to confirm which signs and symptoms are related to ULL and which to other upper limb morbidities.
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Affiliation(s)
- Ceren Gursen
- Faculty of Physical Therapy and Rehabilitation, Hacettepe University, Ankara, Turkey
| | | | - Niamh Moloney
- Department of Health Professions, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, Australia
| | - Mira Meeus
- Department of Rehabilitation Sciences and Physiotherapy, Ghent University, Ghent, Belgium.,Department of Rehabilitation Sciences and Physiotherapy, University of Antwerp, Antwerp, Belgium.,Pain in Motion International Research Group
| | - Tessa De Vrieze
- Department of Rehabilitation Sciences, KU Leuven - University of Leuven, Leuven, Belgium
| | - Nele Devoogdt
- Department of Rehabilitation Sciences, KU Leuven - University of Leuven, Leuven, Belgium.,Department of Vascular Surgery, University Hospitals Leuven, Leuven, Belgium.,Department of Physical Medicine and Rehabilitation, Centre for Lymphedema, Leuven, Belgium
| | - An De Groef
- Department of Vascular Surgery, University Hospitals Leuven, Leuven, Belgium.,Department of Physical Medicine and Rehabilitation, Centre for Lymphedema, Leuven, Belgium
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24
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Havens LM, Brunelle CL, Gillespie TC, Bernstein M, Bucci LK, Kassamani YW, Taghian AG. Use of technology to facilitate a prospective surveillance program for breast cancer-related lymphedema at the Massachusetts General Hospital. Mhealth 2021; 7:11. [PMID: 33634194 PMCID: PMC7882272 DOI: 10.21037/mhealth-19-218] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 04/29/2020] [Indexed: 11/06/2022] Open
Abstract
Breast cancer-related lymphedema (BCRL) is a negative sequela of breast cancer (BC) caused by trauma to the lymphatic system during surgery or radiation to the axillary lymph nodes. BCRL affects approximately one in five patients treated for BC, and patients are at a lifelong risk for BCRL after treatment. Early diagnosis of BCRL may prevent its progression and reduce negative effects on quality of life, necessitating comprehensive prospective screening. This paper provides an overview of technology that may be used as part of a BCRL screening program, including objective measures such as perometry, bioimpedance spectroscopy, tissue tonometry, and three-dimensional optical imaging. Furthermore, this paper comprehensively reviews the technology incorporated into the established prospective screening program at Massachusetts General Hospital. Our prospective screening program consists of longitudinal measurements via perometry, symptoms assessment, and clinical examination by a certified lymphedema therapist (CLT) as needed. Discussion about use of perometry within the screening program and incorporation of arm volume measurements into equations to determine change over time and accurate diagnosis is included [relative volume change (RVC) and weight-adjusted change (WAC) equations]. Use of technology throughout the program is discussed, including a HIPPA-compliant online research database, the patient's electronic medical record, and incorporation of BCRL-related symptoms [BC and lymphedema symptom experience index (BCLE-SEI) survey]. Ultimately, both subjective and objective data are used to inform BCRL diagnosis and treatment by the CLT. In conclusion, the role of technology in facilitating BCRL screening is indispensable, and the continued development of objective assessment methods that are not only reliable and valid, but also cost-effective and portable will help establish BCRL screening as the standard of care for patients treated for BC.
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Affiliation(s)
- Lauren M. Havens
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Cheryl L. Brunelle
- Department of Physical and Occupational Therapy, Massachusetts General Hospital, Boston, MA, USA
| | - Tessa C. Gillespie
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Madison Bernstein
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Loryn K. Bucci
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Yara W. Kassamani
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA
| | - Alphonse G. Taghian
- Department of Radiation Oncology, Massachusetts General Hospital, Boston, MA, USA
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25
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Nahum JL, Fu MR, Scagliola J, Rodorigo M, Tobik S, Guth A, Axelrod D. Real-time electronic patient evaluation of lymphedema symptoms, referral, and satisfaction: a cross-sectional study. Mhealth 2021; 7:20. [PMID: 33898589 PMCID: PMC8063004 DOI: 10.21037/mhealth-20-118] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 05/22/2020] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND Lymphedema is a progressive and chronic illness. Early detection and treatment often lead to better clinical outcomes and improvement of patients' quality of life. Lymphedema symptoms can assist in detecting lymphedema. However, the use of patient-reported symptom evaluation is still limited in clinical practice. To address this gap in clinical practice, a metropolitan cancer center implemented an electronic patient evaluation of lymphedema symptoms (EPE-LE) to enable patients' real-time symptom report during patients' routine clinical visit while waiting to see their doctors in a waiting room. The purpose of this clinical project was to evaluate the usefulness of EPE-LE during patients' routine clinical visit. METHODS A cross-sectional design was used. Participants were outpatient post-surgical breast cancer patients and clinicians who were involved in the EPE-LE implementation at a metropolitan cancer center of US. Data were collected during the three-month EPE-LE implementation, including patients' report of lymphedema symptoms, patient and clinician satisfaction, and referral to lymphedema specialists. Descriptive statistics were used for data analysis. RESULTS During the three-month implementation, a total of 334 patients utilized the EPE-LE to report their lymphedema symptoms and 24 referrals to lymphedema specialists. Nearly all of the patients found that the EPE-LE was easy to use (91%) and that they were satisfied with the EPE-LE for reporting lymphedema symptoms (89%). The majority (70%) of patients reported that the EPE-LE helped them to learn about symptoms related to lymphedema and encouraged them to monitor their symptoms. All clinicians (100%) agreed that the use of the EPE-LE improved their lymphedema symptom assessment in post-surgical breast cancer patients; 75% reported that the EPE-LE increased their communication with patients related to lymphedema symptoms, 75% agreed they would recommend the EPE-LE for use at other cancer centers, and 75% reported that the information retrieved from the EPE-LE was helpful in evaluation of lymphedema. CONCLUSIONS The use of EPE-LE enhanced patients' real-time report of lymphedema symptoms, improved patient education on lymphedema symptoms, and helped clinicians for evaluation of lymphedema. The use of EPE-LE is an example how to implement evidence-based research into clinical practice that provides benefits for both patients and clinicians.
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Affiliation(s)
| | - Mei R. Fu
- NYU Rory Meyers College of Nursing, New York, NY, USA
- The Boston College William F. Connell School of Nursing, Chestnut Hill, MA, USA
| | - Joan Scagliola
- Director of Nursing, NYU Clinical Cancer Center, New York, NY, USA
| | | | - Sandy Tobik
- NYU Rory Meyers College of Nursing, New York, NY, USA
| | - Amber Guth
- New York University School of Medicine, NYU Clinical Cancer Center, New York, NY, USA
| | - Deborah Axelrod
- New York University School of Medicine, NYU Clinical Cancer Center, New York, NY, USA
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26
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Fu MR. Real-time detection and management of chronic illnesses. Mhealth 2021; 7:1. [PMID: 33634184 PMCID: PMC7882261 DOI: 10.21037/mhealth-2020-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 08/18/2020] [Indexed: 11/06/2022] Open
Affiliation(s)
- Mei R Fu
- Barry Family & Goldman Sachs Endowed Professor, Boston College William F. Connell School of Nursing, Chestnut Hill, MA, USA.
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27
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Ward TM, Skubic M, Rantz M, Vorderstrasse A. Human-centered approaches that integrate sensor technology across the lifespan: Opportunities and challenges. Nurs Outlook 2020; 68:734-744. [PMID: 32631796 PMCID: PMC8104265 DOI: 10.1016/j.outlook.2020.05.004] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Revised: 04/29/2020] [Accepted: 05/03/2020] [Indexed: 01/22/2023]
Abstract
Children, parents, older adults, and caregivers routinely use sensor technology as a source of health information and health monitoring. The purpose of this paper is to describe three exemplars of research that used a human-centered approach to engage participants in the development, design, and usability of interventions that integrate technology to promote health. The exemplars are based on current research studies that integrate sensor technology into pediatric, adult, and older adult populations living with a chronic health condition. Lessons learned and considerations for future studies are discussed. Nurses have successfully implemented interventions that use technology to improve health and detect, prevent, and manage diseases in children, families, individuals and communities. Nurses are key stakeholders to inform clinically relevant health monitoring that can support timely and personalized intervention and recommendations.
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Affiliation(s)
- Teresa M Ward
- School of Nursing, University of Washington, Seattle, WA.
| | - Marjorie Skubic
- Electrical Engineering and Computer Science, University of Missouri, Columbia, MO
| | - Marilyn Rantz
- Sinclair School of Nursing, University of Missouri, Columbia, MO
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28
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Mosayebi A, Mojaradi B, Bonyadi Naeini A, Khodadad Hosseini SH. Modeling and comparing data mining algorithms for prediction of recurrence of breast cancer. PLoS One 2020; 15:e0237658. [PMID: 33057328 PMCID: PMC7561198 DOI: 10.1371/journal.pone.0237658] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Accepted: 07/30/2020] [Indexed: 12/13/2022] Open
Abstract
Breast cancer is the most common invasive cancer and the second leading cause of cancer death in women. and regrettably, this rate is increasing every year. One of the aspects of all cancers, including breast cancer, is the recurrence of the disease, which causes painful consequences to the patients. Moreover, the practical application of data mining in the field of breast cancer can help to provide some necessary information and knowledge required by physicians for accurate prediction of breast cancer recurrence and better decision-making. The main objective of this study is to compare different data mining algorithms to select the most accurate model for predicting breast cancer recurrence. This study is cross-sectional and data gathering of this research performed from June 2018 to June 2019 from the official statistics of Ministry of Health and Medical Education and the Iran Cancer Research Center for patients with breast cancer who had been followed for a minimum of 5 years from February 2014 to April 2019, including 5471 independent records. After initial pre-processing in dataset and variables, seven new and conventional data mining algorithms have been applied that each one represents one kind of data mining approach. Results show that the C5.0 algorithm possibly could be a helpful tool for the prediction of breast cancer recurrence at the stage of distant recurrence and nonrecurrence, especially in the first to third years. also, LN involvement rate, Her2 value, Tumor size, free or closed tumor margin were found to be the most important features in our dataset to predict breast cancer recurrence.
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Affiliation(s)
- Alireza Mosayebi
- Department of Management and Business Engineering, School of Progress Engineering, Iran University of Science and Technology, Tehran, Iran
| | - Barat Mojaradi
- Department of Geomatics, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
- * E-mail:
| | - Ali Bonyadi Naeini
- Department of Management and Business Engineering, School of Progress Engineering, Iran University of Science and Technology, Tehran, Iran
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29
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Lee SK, Ahn J, Shin JH, Lee JY. Application of Machine Learning Methods in Nursing Home Research. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E6234. [PMID: 32867250 PMCID: PMC7503291 DOI: 10.3390/ijerph17176234] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Revised: 08/23/2020] [Accepted: 08/24/2020] [Indexed: 12/13/2022]
Abstract
Background: A machine learning (ML) system is able to construct algorithms to continue improving predictions and generate automated knowledge through data-driven predictors or decisions. Objective: The purpose of this study was to compare six ML methods (random forest (RF), logistics regression, linear support vector machine (SVM), polynomial SVM, radial SVM, and sigmoid SVM) of predicting falls in nursing homes (NHs). Methods: We applied three representative six-ML algorithms to the preprocessed dataset to develop a prediction model (N = 60). We used an accuracy measure to evaluate prediction models. Results: RF was the most accurate model (0.883), followed by the logistic regression model, SVM linear, and polynomial SVM (0.867). Conclusions: RF was a powerful algorithm to discern predictors of falls in NHs. For effective fall management, researchers should consider organizational characteristics as well as personal factors. Recommendations for Future Research: To confirm the superiority of ML in NH research, future studies are required to discern additional potential factors using newly introduced ML methods.
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Affiliation(s)
- Soo-Kyoung Lee
- College of Nursing, Keimyung University, 1095, Dalgubeol-daero, Dalseo-gu, Daegu 42601, Korea;
| | - Jinhyun Ahn
- Department of Management Information Systems, Jeju National University, Jeju-do 63243, Korea;
| | - Juh Hyun Shin
- College of Nursing, Ewha Womans University, Seoul 03760, Korea;
| | - Ji Yeon Lee
- College of Nursing, Ewha Womans University, Seoul 03760, Korea;
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30
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Herrada AA, Mejías C, Lazo-Amador R, Olate-Briones A, Lara D, Escobedo N. Development of New Serum Biomarkers for Early Lymphedema Detection. Lymphat Res Biol 2020; 18:136-145. [DOI: 10.1089/lrb.2019.0008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Affiliation(s)
- Andrés A. Herrada
- Lymphatic Vasculature and Inflammation Research Laboratory, Facultad de Ciencias de la Salud, Instituto de Ciencias Biomédicas, Universidad Autónoma de Chile, Talca, Chile
| | - Camila Mejías
- Lymphatic Vasculature and Inflammation Research Laboratory, Facultad de Ciencias de la Salud, Instituto de Ciencias Biomédicas, Universidad Autónoma de Chile, Talca, Chile
| | - Rodrigo Lazo-Amador
- Lymphatic Vasculature and Inflammation Research Laboratory, Facultad de Ciencias de la Salud, Instituto de Ciencias Biomédicas, Universidad Autónoma de Chile, Talca, Chile
| | - Alexandra Olate-Briones
- Lymphatic Vasculature and Inflammation Research Laboratory, Facultad de Ciencias de la Salud, Instituto de Ciencias Biomédicas, Universidad Autónoma de Chile, Talca, Chile
| | - Danitza Lara
- Lymphatic Vasculature and Inflammation Research Laboratory, Facultad de Ciencias de la Salud, Instituto de Ciencias Biomédicas, Universidad Autónoma de Chile, Talca, Chile
| | - Noelia Escobedo
- Lymphatic Vasculature and Inflammation Research Laboratory, Facultad de Ciencias de la Salud, Instituto de Ciencias Biomédicas, Universidad Autónoma de Chile, Talca, Chile
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Sun Z, Li Y, Wang Y, Fan X, Xu K, Wang K, Li S, Zhang Z, Jiang T, Liu X. Radiogenomic analysis of vascular endothelial growth factor in patients with diffuse gliomas. Cancer Imaging 2019; 19:68. [PMID: 31639060 PMCID: PMC6805458 DOI: 10.1186/s40644-019-0256-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2019] [Accepted: 09/25/2019] [Indexed: 01/02/2023] Open
Abstract
OBJECTIVE To predict vascular endothelial growth factor (VEGF) expression in patients with diffuse gliomas using radiomic analysis. MATERIALS AND METHODS Preoperative magnetic resonance images were retrospectively obtained from 239 patients with diffuse gliomas (World Health Organization grades II-IV). The patients were randomly assigned to a training group (n = 160) or a validation group (n = 79) at a 2:1 ratio. For each patient, a total of 431 radiomic features were extracted. The minimum redundancy maximum relevance (mRMR) algorithm was used for feature selection. A machine-learning model for predicting VEGF status was then developed using the selected features and a support vector machine classifier. The predictive performance of the model was evaluated in both groups using receiver operating characteristic curve analysis, and correlations between selected features were assessed. RESULTS Nine radiomic features were selected to generate a VEGF-associated radiomic signature of diffuse gliomas based on the mRMR algorithm. This radiomic signature consisted of two first-order statistics or related wavelet features (Entropy and Minimum) and seven textural features or related wavelet features (including Cluster Tendency and Long Run Low Gray Level Emphasis). The predictive efficiencies measured by the area under the curve were 74.1% in the training group and 70.2% in the validation group. The overall correlations between the 9 radiomic features were low in both groups. CONCLUSIONS Radiomic analysis facilitated efficient prediction of VEGF status in diffuse gliomas, suggesting that using tumor-derived radiomic features for predicting genomic information is feasible.
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Affiliation(s)
- Zhiyan Sun
- Beijing Neurosurgical Institute, Capital Medical University, 6 Tiantanxili, Beijing, 100050, China
| | - Yiming Li
- Beijing Neurosurgical Institute, Capital Medical University, 6 Tiantanxili, Beijing, 100050, China
| | - Yinyan Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Xing Fan
- Beijing Neurosurgical Institute, Capital Medical University, 6 Tiantanxili, Beijing, 100050, China
| | - Kaibin Xu
- Chinese Academy of Sciences, Institute of Automation, Beijing, China
| | - Kai Wang
- Department of Nuclear Medicine, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Shaowu Li
- Beijing Neurosurgical Institute, Capital Medical University, 6 Tiantanxili, Beijing, 100050, China
| | - Zhong Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tao Jiang
- Beijing Neurosurgical Institute, Capital Medical University, 6 Tiantanxili, Beijing, 100050, China.,Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.,Center of Brain Tumor, Beijing Institute for Brain Disorders, Beijing, China.,China National Clinical Research Center for Neurological Diseases, Beijing, China.,Chinese Glioma Genome Atlas Network (CGGA) and Asian Glioma Genome Atlas Network (AGGA), Beijing, China
| | - Xing Liu
- Beijing Neurosurgical Institute, Capital Medical University, 6 Tiantanxili, Beijing, 100050, China.
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Hosni M, Abnane I, Idri A, Carrillo de Gea JM, Fernández Alemán JL. Reviewing ensemble classification methods in breast cancer. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2019; 177:89-112. [PMID: 31319964 DOI: 10.1016/j.cmpb.2019.05.019] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 05/16/2019] [Accepted: 05/18/2019] [Indexed: 05/09/2023]
Abstract
CONTEXT Ensemble methods consist of combining more than one single technique to solve the same task. This approach was designed to overcome the weaknesses of single techniques and consolidate their strengths. Ensemble methods are now widely used to carry out prediction tasks (e.g. classification and regression) in several fields, including that of bioinformatics. Researchers have particularly begun to employ ensemble techniques to improve research into breast cancer, as this is the most frequent type of cancer and accounts for most of the deaths among women. OBJECTIVE AND METHOD The goal of this study is to analyse the state of the art in ensemble classification methods when applied to breast cancer as regards 9 aspects: publication venues, medical tasks tackled, empirical and research types adopted, types of ensembles proposed, single techniques used to construct the ensembles, validation framework adopted to evaluate the proposed ensembles, tools used to build the ensembles, and optimization methods used for the single techniques. This paper was undertaken as a systematic mapping study. RESULTS A total of 193 papers that were published from the year 2000 onwards, were selected from four online databases: IEEE Xplore, ACM digital library, Scopus and PubMed. This study found that of the six medical tasks that exist, the diagnosis medical task was that most frequently researched, and that the experiment-based empirical type and evaluation-based research type were the most dominant approaches adopted in the selected studies. The homogeneous type was that most widely used to perform the classification task. With regard to single techniques, this mapping study found that decision trees, support vector machines and artificial neural networks were those most frequently adopted to build ensemble classifiers. In the case of the evaluation framework, the Wisconsin Breast Cancer dataset was the most frequently used by researchers to perform their experiments, while the most noticeable validation method was k-fold cross-validation. Several tools are available to perform experiments related to ensemble classification methods, such as Weka and R Software. Few researchers took into account the optimisation of the single technique of which their proposed ensemble was composed, while the grid search method was that most frequently adopted to tune the parameter settings of a single classifier. CONCLUSION This paper reports an in-depth study of the application of ensemble methods as regards breast cancer. Our results show that there are several gaps and issues and we, therefore, provide researchers in the field of breast cancer research with recommendations. Moreover, after analysing the papers found in this systematic mapping study, we discovered that the majority report positive results concerning the accuracy of ensemble classifiers when compared to the single classifiers. In order to aggregate the evidence reported in literature, it will, therefore, be necessary to perform a systematic literature review and meta-analysis in which an in-depth analysis could be conducted so as to confirm the superiority of ensemble classifiers over the classical techniques.
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Affiliation(s)
- Mohamed Hosni
- Software Project Management Research Team, ENSIAS, University Mohammed V of Rabat, Morocco.
| | - Ibtissam Abnane
- Software Project Management Research Team, ENSIAS, University Mohammed V of Rabat, Morocco.
| | - Ali Idri
- Software Project Management Research Team, ENSIAS, University Mohammed V of Rabat, Morocco.
| | - Juan M Carrillo de Gea
- Department of Informatics and Systems, Faculty of Computer Science, University of Murcia, Spain.
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