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Zhu Z, Jin Y, Zhou J, Chen F, Chen M, Gao Z, Hu L, Xuan J, Li X, Song Z, Guo X. PD1/PD-L1 blockade in clear cell renal cell carcinoma: mechanistic insights, clinical efficacy, and future perspectives. Mol Cancer 2024; 23:146. [PMID: 39014460 PMCID: PMC11251344 DOI: 10.1186/s12943-024-02059-y] [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: 05/31/2024] [Accepted: 07/04/2024] [Indexed: 07/18/2024] Open
Abstract
The advent of PD1/PD-L1 inhibitors has significantly transformed the therapeutic landscape for clear cell renal cell carcinoma (ccRCC). This review provides an in-depth analysis of the biological functions and regulatory mechanisms of PD1 and PD-L1 in ccRCC, emphasizing their role in tumor immune evasion. We comprehensively evaluate the clinical efficacy and safety profiles of PD1/PD-L1 inhibitors, such as Nivolumab and Pembrolizumab, through a critical examination of recent clinical trial data. Furthermore, we discuss the challenges posed by resistance mechanisms to these therapies and potential strategies to overcome them. We also explores the synergistic potential of combination therapies, integrating PD1/PD-L1 inhibitors with other immunotherapies, targeted therapies, and conventional modalities such as chemotherapy and radiotherapy. In addition, we examine emerging predictive biomarkers for response to PD1/PD-L1 blockade and biomarkers indicative of resistance, providing a foundation for personalized therapeutic approaches. Finally, we outline future research directions, highlighting the need for novel therapeutic strategies, deeper mechanistic insights, and the development of individualized treatment regimens. Our work summarizes the latest knowledge and progress in this field, aiming to provide a valuable reference for improving clinical efficacy and guiding future research on the application of PD1/PD-L1 inhibitors in ccRCC.
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Affiliation(s)
- Zhaoyang Zhu
- Jiaxing University Master Degree Cultivation Base, Zhejiang Chinese Medical University, Hangzhou, 310000, Zhejiang, P.R. China
- Department of Urology, The Second Affiliated Hospital of Jiaxing University, Jiaxing, 310000, Zhejiang, P.R. China
| | - Yigang Jin
- Department of Urology, The Second Affiliated Hospital of Jiaxing University, Jiaxing, 310000, Zhejiang, P.R. China
| | - Jing Zhou
- Department of Surgery, the Second Affiliated Hospital of Jiaxing University, Jiaxing, 310000, Zhejiang, P.R. China
| | - Fei Chen
- Department of Surgery, the Second Affiliated Hospital of Jiaxing University, Jiaxing, 310000, Zhejiang, P.R. China
| | - Minjie Chen
- Department of Surgery, the Second Affiliated Hospital of Jiaxing University, Jiaxing, 310000, Zhejiang, P.R. China
| | - Zhaofeng Gao
- Department of Surgery, the Second Affiliated Hospital of Jiaxing University, Jiaxing, 310000, Zhejiang, P.R. China
| | - Lingyu Hu
- Department of Surgery, the Second Affiliated Hospital of Jiaxing University, Jiaxing, 310000, Zhejiang, P.R. China
| | - Jinyan Xuan
- Department of General Practice, the Second Affiliated Hospital of Jiaxing University, Jiaxing, 310000, Zhejiang, P.R. China
| | - Xiaoping Li
- Department of Surgery, the Second Affiliated Hospital of Jiaxing University, Jiaxing, 310000, Zhejiang, P.R. China.
| | - Zhengwei Song
- Department of Surgery, the Second Affiliated Hospital of Jiaxing University, Jiaxing, 310000, Zhejiang, P.R. China.
| | - Xiao Guo
- Department of Urology, The Second Affiliated Hospital of Jiaxing University, Jiaxing, 310000, Zhejiang, P.R. China.
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Smrke U, Abalde-Cela S, Loly C, Calbimonte JP, Pires LR, Lin S, Sánchez A, Tement S, Mlakar I. Quality of Life of Colorectal Cancer Survivors: Mapping the Key Indicators by Expert Consensus and Measures for Their Assessment. Healthcare (Basel) 2024; 12:1235. [PMID: 38921349 PMCID: PMC11203183 DOI: 10.3390/healthcare12121235] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 05/15/2024] [Accepted: 06/18/2024] [Indexed: 06/27/2024] Open
Abstract
Quality of life (QoL) assessments are integral to cancer care, yet their effectiveness in providing essential information for supporting survivors varies. This study aimed to elucidate key indicators of QoL among colorectal cancer survivors from the perspective of healthcare professionals, and to evaluate existing QoL questionnaires in relation to these indicators. Two studies were conducted: a Delphi study to identify key QoL indicators and a scoping review of questionnaires suitable for colorectal cancer survivors. Fifty-four healthcare professionals participated in the Delphi study's first round, with 25 in the second. The study identified two primary QoL domains (physical and psychological) and 17 subdomains deemed most critical. Additionally, a review of 12 questionnaires revealed two instruments assessing the most important general domains. The findings underscored a misalignment between existing assessment tools and healthcare professionals' clinical priorities in working with colorectal cancer survivors. To enhance support for survivors' QoL, efforts are needed to develop instruments that better align with the demands of routine QoL assessment in clinical practice.
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Affiliation(s)
- Urška Smrke
- Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia
| | - Sara Abalde-Cela
- RUBYnanomed LDA, Praça Conde de Agrolongo, 4700-314 Braga, Portugal
| | - Catherine Loly
- Gastroenterology Department, University Hospital of Liège, 4000 Liège, Belgium
| | - Jean-Paul Calbimonte
- Institute of Informatics, University of Applied Sciences and Arts Western Switzerland HES-SO, 3960 Sierre, Switzerland
- The Sense Innovation & Research Center, 1007 Lausanne, Switzerland
| | - Liliana R. Pires
- RUBYnanomed LDA, Praça Conde de Agrolongo, 4700-314 Braga, Portugal
| | - Simon Lin
- Science Department, Symptoma GmbH, 5020 Vienna, Austria
- Department of Internal Medicine, Paracelsus Medical University, 5020 Salzburg, Austria
| | - Alberto Sánchez
- Department of eHealth, Galician Research & Development Center in Advanced Telecommunications (GRADIANT), 26334 Vigo, Spain
| | - Sara Tement
- Department of Psychology, Faculty of Arts, University of Maribor, 2000 Maribor, Slovenia
| | - Izidor Mlakar
- Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia
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Güven DC, Thong MS, Arndt V. Survivorship outcomes in patients treated with immune checkpoint inhibitors: a scoping review. J Cancer Surviv 2024:10.1007/s11764-023-01507-w. [PMID: 38175366 DOI: 10.1007/s11764-023-01507-w] [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: 09/15/2023] [Accepted: 11/26/2023] [Indexed: 01/05/2024]
Abstract
BACKGROUND Immune checkpoint inhibitors (ICIs) have become a central part of cancer care. However, the survivorship outcomes in patients treated with ICIs are understudied. Therefore, we conducted a scoping review to evaluate the current status of the field and to establish research gaps regarding survivorship outcomes with ICIs in real-life cohorts. METHODS We used the Web of Science, PubMed, and Embase databases to systematically filter published studies with real-life cohorts from January 1, 2010, until October 19, 2022. Studies evaluating at least one survivorship outcome in ICI-treated patients were included. RESULTS A total of 39 papers were included. Quality of life (QoL) (n = 23), toxicity burden (n = 16), and psychosocial issues (n = 9) were the most frequently evaluated survivorship outcomes. Anti-PD-1/PD-L1 monotherapy and a response to treatment were associated with better QoL. In addition, the ICIs were associated with grade 3 or higher immune-related adverse events (irAEs) in 10-15% and late/long-term irAEs in 20-30% of the survivors. Regarding psychosocial problems, over 30% of survivors showed evidence of anxiety and depression, and 30-40% of survivors reported neurocognitive impairments. CONCLUSION The survivors treated with ICIs have impairments in most survivorship domains. Further research is needed to gather data on the understudied survivorship outcomes like late and long-term effects, fertility, financial toxicity, and return to work in survivors treated with ICIs. IMPLICATIONS FOR CANCER SURVIVORS Available evidence demonstrates that a significant portion of survivors treated with ICIs have a significant toxicity burden, lower QoL than the general population, and a high rate of psychosocial problems.
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Affiliation(s)
- Deniz Can Güven
- Department of Medical Oncology, Hacettepe University Cancer Institute, 06100 Sihhiye, Ankara, Turkey.
- Health Sciences University, Elazig City Hospital, Elazig, Turkey.
- Unit of Cancer Survivorship, Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Melissa Sy Thong
- Unit of Cancer Survivorship, Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Volker Arndt
- Unit of Cancer Survivorship, Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
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Manzo G, Pannatier Y, Duflot P, Kolh P, Chavez M, Bleret V, Calvaresi D, Jimenez-Del-Toro O, Schumacher M, Calbimonte JP. Breast cancer survival analysis agents for clinical decision support. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 231:107373. [PMID: 36720187 DOI: 10.1016/j.cmpb.2023.107373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 12/31/2022] [Accepted: 01/23/2023] [Indexed: 06/18/2023]
Abstract
Personalized support and assistance are essential for cancer survivors, given the physical and psychological consequences they have to suffer after all the treatments and conditions associated with this illness. Digital assistive technologies have proved to be effective in enhancing the quality of life of cancer survivors, for instance, through physical exercise monitoring and recommendation or emotional support and prediction. To maximize the efficacy of these techniques, it is challenging to develop accurate models of patient trajectories, which are typically fed with information acquired from retrospective datasets. This paper presents a Machine Learning-based survival model embedded in a clinical decision system architecture for predicting cancer survivors' trajectories. The proposed architecture of the system, named PERSIST, integrates the enrichment and pre-processing of clinical datasets coming from different sources and the development of clinical decision support modules. Moreover, the model includes detecting high-risk markers, which have been evaluated in terms of performance using both a third-party dataset of breast cancer patients and a retrospective dataset collected in the context of the PERSIST clinical study.
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Affiliation(s)
- Gaetano Manzo
- University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland; National Institutes of Health (NIH), Bethesda, MD, USA.
| | - Yvan Pannatier
- University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland
| | - Patrick Duflot
- CHU of Liege, Department of Information System Management, Belgium
| | - Philippe Kolh
- CHU of Liege, Department of Information System Management, Belgium
| | - Marcela Chavez
- CHU of Liege, Department of Information System Management, Belgium
| | | | - Davide Calvaresi
- University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland
| | | | - Michael Schumacher
- University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland
| | - Jean-Paul Calbimonte
- University of Applied Sciences and Arts Western Switzerland (HES-SO), Switzerland; The Sense Innovation and Research Center, Lausanne and Sion, Switzerland
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Wang A, Qian Z, Briggs L, Cole AP, Reis LO, Trinh QD. The Use of Chatbots in Oncological Care: A Narrative Review. Int J Gen Med 2023; 16:1591-1602. [PMID: 37152273 PMCID: PMC10162388 DOI: 10.2147/ijgm.s408208] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 04/18/2023] [Indexed: 05/09/2023] Open
Abstract
Background Few reports have investigated chatbots in patient care. We aimed to assess the current applications, limitations, and challenges in the literature on chatbots employed in oncological care. Methods We queried the PubMed database through April 2022 and included studies that investigated the use of chatbots in different phases of oncological care. The search used five different combinations of the specific terms "chatbot", "cancer", "oncology", and "conversational agent". Inclusion criteria were chatbot use in any aspect of oncological care-prevention, patient education, treatment, and surveillance. Results The initial search yielded 196 records, 21 of which met inclusion criteria. The identified chatbots mostly focused on breast and ovarian cancer (n=8), with the second most common being cervical cancer (n=3). Good patient satisfaction was reported among 14 of 21 chatbots. The most reported chatbot applications were cancer screening, prevention, risk stratification, treatment, monitoring, and management. Of 12 studies examining efficacy of care via chatbot, 9 demonstrated improvements compared to standard care. Conclusion Chatbots used for oncological care to date demonstrate high user satisfaction, and many have shown efficacy in improving patient-centered communication, accessibility to cancer-related information, and access to care. Currently, chatbots are primarily limited by the need for extensive user-testing and iterative improvement before widespread implementation.
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Affiliation(s)
- Alexander Wang
- Division of Urological Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Surgery and Public Health, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Zhiyu Qian
- Division of Urological Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Surgery and Public Health, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Logan Briggs
- Division of Urological Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Surgery and Public Health, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexander P Cole
- Division of Urological Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Surgery and Public Health, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Leonardo O Reis
- Division of Urological Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Surgery and Public Health, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- UroScience, School of Medical Sciences, University of Campinas, UNICAMP, and Immuno-Oncology Division, Pontifical Catholic University of Campinas, PUC-Campinas, Sao Paulo, Brazil
| | - Quoc-Dien Trinh
- Division of Urological Surgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Center for Surgery and Public Health, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Correspondence: Quoc-Dien Trinh, Surgery, Harvard Medical School, Division of Urological Surgery, Brigham and Women’s Hospital, 45 Francis St, ASB II-3, Boston, MA, 02115, USA, Tel +1 617 525-7350, Fax +1 617 525-6348, Email
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Sorayaie Azar A, Babaei Rikan S, Naemi A, Bagherzadeh Mohasefi J, Pirnejad H, Bagherzadeh Mohasefi M, Wiil UK. Application of machine learning techniques for predicting survival in ovarian cancer. BMC Med Inform Decis Mak 2022; 22:345. [PMID: 36585641 PMCID: PMC9801354 DOI: 10.1186/s12911-022-02087-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 12/15/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Ovarian cancer is the fifth leading cause of mortality among women in the United States. Ovarian cancer is also known as forgotten cancer or silent disease. The survival of ovarian cancer patients depends on several factors, including the treatment process and the prognosis. METHODS The ovarian cancer patients' dataset is compiled from the Surveillance, Epidemiology, and End Results (SEER) database. With the help of a clinician, the dataset is curated, and the most relevant features are selected. Pearson's second coefficient of skewness test is used to evaluate the skewness of the dataset. Pearson correlation coefficient is also used to investigate the associations between features. Statistical test is utilized to evaluate the significance of the features. Six Machine Learning (ML) models, including K-Nearest Neighbors , Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), and Extreme Gradient Boosting (XGBoost), are implemented for survival prediction in both classification and regression approaches. An interpretable method, Shapley Additive Explanations (SHAP), is applied to clarify the decision-making process and determine the importance of each feature in prediction. Additionally, DTs of the RF model are displayed to show how the model predicts the survival intervals. RESULTS Our results show that RF (Accuracy = 88.72%, AUC = 82.38%) and XGBoost (Root Mean Squad Error (RMSE)) = 20.61%, R2 = 0.4667) have the best performance for classification and regression approaches, respectively. Furthermore, using the SHAP method along with extracted DTs of the RF model, the most important features in the dataset are identified. Histologic type ICD-O-3, chemotherapy recode, year of diagnosis, age at diagnosis, tumor stage, and grade are the most important determinant factors in survival prediction. CONCLUSION To the best of our knowledge, our study is the first study that develops various ML models to predict ovarian cancer patients' survival on the SEER database in both classification and regression approaches. These ML algorithms also achieve more accurate results and outperform statistical methods. Furthermore, our study is the first study to use the SHAP method to increase confidence and transparency of the proposed models' prediction for clinicians. Moreover, our developed models, as an automated auxiliary tool, can help clinicians to have a better understanding of the estimated survival as well as important features that affect survival.
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Affiliation(s)
- Amir Sorayaie Azar
- grid.412763.50000 0004 0442 8645Department of Computer Engineering, Urmia University, Urmia, Iran
| | - Samin Babaei Rikan
- grid.412763.50000 0004 0442 8645Department of Computer Engineering, Urmia University, Urmia, Iran
| | - Amin Naemi
- grid.10825.3e0000 0001 0728 0170Center for Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
| | | | - Habibollah Pirnejad
- grid.412763.50000 0004 0442 8645Patient Safety Research Center, Clinical Research Institute, Urmia University of Medical Sciences, Urmia, Iran ,grid.6906.90000000092621349Erasmus School of Health Policy and Management (ESHPM), Erasmus University Rotterdam, Rotterdam, The Netherlands
| | | | - Uffe Kock Wiil
- grid.10825.3e0000 0001 0728 0170Center for Health Informatics and Technology, The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Odense, Denmark
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Arioz U, Smrke U, Plohl N, Mlakar I. Scoping Review on the Multimodal Classification of Depression and Experimental Study on Existing Multimodal Models. Diagnostics (Basel) 2022; 12:2683. [PMID: 36359525 PMCID: PMC9689708 DOI: 10.3390/diagnostics12112683] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2022] [Revised: 10/27/2022] [Accepted: 10/31/2022] [Indexed: 12/26/2023] Open
Abstract
Depression is a prevalent comorbidity in patients with severe physical disorders, such as cancer, stroke, and coronary diseases. Although it can significantly impact the course of the primary disease, the signs of depression are often underestimated and overlooked. The aim of this paper was to review algorithms for the automatic, uniform, and multimodal classification of signs of depression from human conversations and to evaluate their accuracy. For the scoping review, the PRISMA guidelines for scoping reviews were followed. In the scoping review, the search yielded 1095 papers, out of which 20 papers (8.26%) included more than two modalities, and 3 of those papers provided codes. Within the scope of this review, supported vector machine (SVM), random forest (RF), and long short-term memory network (LSTM; with gated and non-gated recurrent units) models, as well as different combinations of features, were identified as the most widely researched techniques. We tested the models using the DAIC-WOZ dataset (original training dataset) and using the SymptomMedia dataset to further assess their reliability and dependency on the nature of the training datasets. The best performance was obtained by the LSTM with gated recurrent units (F1-score of 0.64 for the DAIC-WOZ dataset). However, with a drop to an F1-score of 0.56 for the SymptomMedia dataset, the method also appears to be the most data-dependent.
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Affiliation(s)
- Umut Arioz
- Faculty of Electrical Engineering and Computer Science, The University of Maribor, 2000 Maribor, Slovenia
| | - Urška Smrke
- Faculty of Electrical Engineering and Computer Science, The University of Maribor, 2000 Maribor, Slovenia
| | - Nejc Plohl
- Department of Psychology, Faculty of Arts, The University of Maribor, 2000 Maribor, Slovenia
| | - Izidor Mlakar
- Faculty of Electrical Engineering and Computer Science, The University of Maribor, 2000 Maribor, Slovenia
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Pan LC, Wu XR, Lu Y, Zhang HQ, Zhou YL, Liu X, Liu SL, Yan QY. Artificial intelligence empowered Digital Health Technologies in Cancer Survivorship Care: a scoping review. Asia Pac J Oncol Nurs 2022; 9:100127. [PMID: 36176267 PMCID: PMC9513729 DOI: 10.1016/j.apjon.2022.100127] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 07/29/2022] [Indexed: 12/03/2022] Open
Abstract
Objective The objectives of this systematic review are to describe features and specific application scenarios for current cancer survivorship care services of Artificial intelligence (AI)-driven digital health technologies (DHTs) and to explore the acceptance and briefly evaluate its feasibility in the application process. Methods Search for literatures published from 2010 to 2022 on sites MEDLINE, IEEE-Xplor, PubMed, Embase, Cochrane Central Register of Controlled Trials and Scopus systematically. The types of literatures include original research, descriptive study, randomized controlled trial, pilot study, and feasible or acceptable study. The literatures above described current status and effectiveness of digital medical technologies based on AI and used in cancer survivorship care services. Additionally, we use QuADS quality assessment tool to evaluate the quality of literatures included in this review. Results 43 studies that met the inclusion criteria were analyzed and qualitatively synthesized. The current status and results related to the application of AI-driven DHTs in cancer survivorship care were reviewed. Most of these studies were designed specifically for breast cancer survivors’ care and focused on the areas of recurrence or secondary cancer prediction, clinical decision support, cancer survivability prediction, population or treatment stratified, anti-cancer treatment-induced adverse reaction prediction, and so on. Applying AI-based DHTs to cancer survivors actually has shown some positive outcomes, including increased motivation of patient-reported outcomes (PROs), reduce fatigue and pain levels, improved quality of life, and physical function. However, current research mostly explored the technology development and formation (testing) phases, with limited-scale population, and single-center trial. Therefore, it is not suitable to draw conclusions that the effectiveness of AI-based DHTs in supportive cancer care, as most of applications are still in the early stage of development and feasibility testing. Conclusions While digital therapies are promising in the care of cancer patients, more high-quality studies are still needed in the future to demonstrate the effectiveness of digital therapies in cancer care. Studies should explore how to develop uniform standards for measuring patient-related outcomes, ensure the scientific validity of research methods, and emphasize patient and health practitioner involvement in the development and use of technology.
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Affiliation(s)
- Lu-Chen Pan
- Department of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
| | - Xiao-Ru Wu
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Ying Lu
- Department of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Han-Qing Zhang
- Health Science Center, Yangtze University, Jinzhou 434023, China
| | - Yao-Ling Zhou
- Department of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Xue Liu
- School of Nursing, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China
| | - Sheng-Lin Liu
- Department of Medical Engineering, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Corresponding authors.
| | - Qiao-Yuan Yan
- Department of Nursing, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China
- Corresponding authors.
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Mlakar I, Lin S, Nateqi J, Gruarin S, Diéguez L, Piairo P, Pires LR, Tement S, Aleksandraviča I, Leja M, Arcimoviča K, Bleret V, Kaux JF, Kolh P, Maquet D, Gómez JG, Mata JG, Salgado M, Horvat M, Ravnik M, Flis V, Smrke U. Establishing an Expert Consensus on Key Indicators of the Quality of Life among Breast Cancer Survivors: A Modified Delphi Study. J Clin Med 2022; 11:2041. [PMID: 35407649 PMCID: PMC8999421 DOI: 10.3390/jcm11072041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 04/01/2022] [Accepted: 04/04/2022] [Indexed: 02/01/2023] Open
Abstract
(1) Background: The needs of cancer survivors are often not reflected in practice. One of the main barriers of the use of patient-reported outcomes is associated with data collection and the interpretation of patient-reported outcomes (PROs) due to a multitude of instruments and measuring approaches. The aim of the study was to establish an expert consensus on the relevance and key indicators of quality of life in the clinical practice of breast cancer survivors. (2) Methods: Potential indicators of the quality of life of breast cancer survivors were extracted from the established quality of life models, depicting survivors' perspectives. The specific domains and subdomains of quality of life were evaluated in a two-stage online Delphi process, including an international and multidisciplinary panel of experts. (3) Results: The first round of the Delphi process was completed by 57 and the second by 37 participants. A consensus was reached for the Physical and Psychological domains, and on eleven subdomains of quality of life. The results were further supported by the additional ranking of importance of the subdomains in the second round. (4) Conclusions: The current findings can serve to optimize the use of instruments and address the challenges related to data collection and interpretation as the facilitators of the adaption in routine practice.
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Affiliation(s)
- Izidor Mlakar
- Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia;
| | - Simon Lin
- Data Science Department, Symptoma, 1030 Vienna, Austria;
| | - Jama Nateqi
- Medical Department, Symptoma, 4864 Attersee, Austria; (J.N.); (S.G.)
| | - Stefanie Gruarin
- Medical Department, Symptoma, 4864 Attersee, Austria; (J.N.); (S.G.)
| | - Lorena Diéguez
- RUBYnanomed, 4700-314 Braga, Portugal; (L.D.); (P.P.); (L.R.P.)
| | - Paulina Piairo
- RUBYnanomed, 4700-314 Braga, Portugal; (L.D.); (P.P.); (L.R.P.)
| | | | - Sara Tement
- Faculty of Arts, Department of Psychology, University of Maribor, 2000 Maribor, Slovenia;
| | - Ilona Aleksandraviča
- Institute of Clinical and Preventive Medicine of the University of Latvia, LV-1586 Riga, Latvia; (I.A.); (M.L.)
| | - Mārcis Leja
- Institute of Clinical and Preventive Medicine of the University of Latvia, LV-1586 Riga, Latvia; (I.A.); (M.L.)
| | - Krista Arcimoviča
- Oncology Centre of Latvia, Riga East Clinical University Hospital, LV-1038 Riga, Latvia;
| | - Valérie Bleret
- Department of Senology, University Hospital of Liège, 4000 Liège, Belgium; (V.B.); (D.M.)
| | - Jean-François Kaux
- Physical and Rehabilitation Medicine, University Hospital of Liège, 4000 Liège, Belgium;
| | - Philippe Kolh
- Department of Information Systems Management, University Hospital of Liège, 4000 Liège, Belgium;
| | - Didier Maquet
- Department of Senology, University Hospital of Liège, 4000 Liège, Belgium; (V.B.); (D.M.)
| | - Jesús Garcia Gómez
- Oncology Department, University Hospital Complex of Ourense (SERGAS), 32005 Ourense, Spain; (J.G.G.); (J.G.M.)
| | - Jesus García Mata
- Oncology Department, University Hospital Complex of Ourense (SERGAS), 32005 Ourense, Spain; (J.G.G.); (J.G.M.)
| | - Mercedes Salgado
- Department of Medical Oncology, Galician Health Services (SERGAS), 15703 Santiago de Compostela, A Coruña, Spain;
| | - Matej Horvat
- Department of Oncology, University Medical Centre Maribor, 2000 Maribor, Slovenia; (M.H.); (M.R.); (V.F.)
| | - Maja Ravnik
- Department of Oncology, University Medical Centre Maribor, 2000 Maribor, Slovenia; (M.H.); (M.R.); (V.F.)
| | - Vojko Flis
- Department of Oncology, University Medical Centre Maribor, 2000 Maribor, Slovenia; (M.H.); (M.R.); (V.F.)
| | - Urška Smrke
- Faculty of Electrical Engineering and Computer Science, University of Maribor, 2000 Maribor, Slovenia;
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