|
1
|
Harutyunyan L, Manvelyan E, Karapetyan N, Bardakhchyan S, Jilavyan A, Tamamyan G, Avagyan A, Safaryan L, Zohrabyan D, Movsisyan N, Avinyan A, Galoyan A, Sargsyan M, Harutyunyan M, Nersoyan H, Stepanyan A, Galstyan A, Danielyan S, Muradyan A, Jilavyan G. A Survival Analysis of Patients with Recurrent Epithelial Ovarian Cancer Based on Relapse Type: A Multi-Institutional Retrospective Study in Armenia. Curr Oncol 2024; 31:1323-1334. [PMID: 38534933 PMCID: PMC10968888 DOI: 10.3390/curroncol31030100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 02/11/2024] [Accepted: 02/27/2024] [Indexed: 05/26/2024] Open
Abstract
BACKGROUND Annually, approximately 200 new ovarian cancer cases are diagnosed in Armenia, which is considered an upper-middle-income country. This study aimed to summarize the survival outcomes of patients with relapsed ovarian cancer in Armenia based on the type of recurrence, risk factors, and choice of systemic treatment. METHODS This retrospective case-control study included 228 patients with relapsed ovarian cancer from three different institutions. RESULTS The median age of the patients was 55. The median follow-up times from relapse and primary diagnosis were 21 and 48 months, respectively. The incidence of platinum-sensitive relapse was 81.6% (186), while platinum-resistant relapse was observed in only 18.4% (42) of patients. The median post-progression survival of the platinum-sensitive group compared to the platinum-resistant group was 54 vs. 25 months (p < 0.001), respectively, while the median survival after relapse was 25 vs. 13 months, respectively; three- and five-year post-progression survival rates in these groups were 31.2% vs. 23.8%, and 15.1% vs. 9.5%, respectively (p = 0.113). CONCLUSIONS Overall, despite new therapeutic approaches, ovarian cancer continues to be one of the deadly malignant diseases affecting women, especially in developing countries with a lack of resources, where chemotherapy remains the primary available systemic treatment for the majority of patients. Low survival rates demonstrate the urgent need for more research focused on this group of patients with poor outcomes.
Collapse
Affiliation(s)
- Lilit Harutyunyan
- Department of General Oncology, Yerevan State Medical University after M. Heratsi, 2 Koryun St., Yerevan 0025, Armenia; (N.K.); (A.A.); (N.M.); (A.M.); (G.J.)
- Oncology Clinic, Mikaelyan Institute of Surgery, Ezras Hasratian 9, Yerevan 0052, Armenia; (A.A.); (A.G.); (M.S.)
| | - Evelina Manvelyan
- Department of Reproductive Biology, University Hospitals Cleveland Medical Center, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA;
| | - Nune Karapetyan
- Department of General Oncology, Yerevan State Medical University after M. Heratsi, 2 Koryun St., Yerevan 0025, Armenia; (N.K.); (A.A.); (N.M.); (A.M.); (G.J.)
- Clinic of Adults’ Oncology and Chemotherapy at Yeolyan Hematology and Oncology Center, 7 Nersisyan St., Yerevan 0014, Armenia; (S.B.); (L.S.); (D.Z.); (M.H.)
- Immune Oncology Research Institute, 7 Nersisyan St., Yerevan 0014, Armenia;
- Yeolyan Hematology and Oncology Center, 7 Nersisyan St., Yerevan 0014, Armenia;
| | - Samvel Bardakhchyan
- Clinic of Adults’ Oncology and Chemotherapy at Yeolyan Hematology and Oncology Center, 7 Nersisyan St., Yerevan 0014, Armenia; (S.B.); (L.S.); (D.Z.); (M.H.)
- Immune Oncology Research Institute, 7 Nersisyan St., Yerevan 0014, Armenia;
- Yeolyan Hematology and Oncology Center, 7 Nersisyan St., Yerevan 0014, Armenia;
| | - Aram Jilavyan
- National Center of Oncology of Armenia, 76 Fanarjyan St., Yerevan 0052, Armenia; (A.J.); (H.N.); (A.S.); (A.G.)
- Department of Gynecologic Oncology, National Center of Oncology of Armenia, 76 Fanarjyan St., Yerevan 0052, Armenia
| | - Gevorg Tamamyan
- Immune Oncology Research Institute, 7 Nersisyan St., Yerevan 0014, Armenia;
- Yeolyan Hematology and Oncology Center, 7 Nersisyan St., Yerevan 0014, Armenia;
- Pediatric Cancer and Blood Disorders Center of Armenia, 7 Nersisyan St., Yerevan 0014, Armenia
- Pediatric Oncology and Hematology Department, Yerevan State Medical University after M. Heratsi, 2 Koryun St., Yerevan 0025, Armenia
| | - Armen Avagyan
- Department of General Oncology, Yerevan State Medical University after M. Heratsi, 2 Koryun St., Yerevan 0025, Armenia; (N.K.); (A.A.); (N.M.); (A.M.); (G.J.)
- Oncology Clinic, Mikaelyan Institute of Surgery, Ezras Hasratian 9, Yerevan 0052, Armenia; (A.A.); (A.G.); (M.S.)
| | - Liana Safaryan
- Clinic of Adults’ Oncology and Chemotherapy at Yeolyan Hematology and Oncology Center, 7 Nersisyan St., Yerevan 0014, Armenia; (S.B.); (L.S.); (D.Z.); (M.H.)
- Yeolyan Hematology and Oncology Center, 7 Nersisyan St., Yerevan 0014, Armenia;
| | - Davit Zohrabyan
- Clinic of Adults’ Oncology and Chemotherapy at Yeolyan Hematology and Oncology Center, 7 Nersisyan St., Yerevan 0014, Armenia; (S.B.); (L.S.); (D.Z.); (M.H.)
- Yeolyan Hematology and Oncology Center, 7 Nersisyan St., Yerevan 0014, Armenia;
| | - Narine Movsisyan
- Department of General Oncology, Yerevan State Medical University after M. Heratsi, 2 Koryun St., Yerevan 0025, Armenia; (N.K.); (A.A.); (N.M.); (A.M.); (G.J.)
- Oncology Clinic, Mikaelyan Institute of Surgery, Ezras Hasratian 9, Yerevan 0052, Armenia; (A.A.); (A.G.); (M.S.)
- Anesthesiology and Intensive Care Department, Yerevan State Medical University after M. Heratsi, 2 Koryun St., Yerevan 0025, Armenia
- Armenian Association for the Study of Pain, 12 Kievyan Str. Apt. 20, Yerevan 0028, Armenia
| | - Anna Avinyan
- Oncology Clinic, Mikaelyan Institute of Surgery, Ezras Hasratian 9, Yerevan 0052, Armenia; (A.A.); (A.G.); (M.S.)
| | - Arevik Galoyan
- Oncology Clinic, Mikaelyan Institute of Surgery, Ezras Hasratian 9, Yerevan 0052, Armenia; (A.A.); (A.G.); (M.S.)
| | - Mariam Sargsyan
- Oncology Clinic, Mikaelyan Institute of Surgery, Ezras Hasratian 9, Yerevan 0052, Armenia; (A.A.); (A.G.); (M.S.)
- Immune Oncology Research Institute, 7 Nersisyan St., Yerevan 0014, Armenia;
| | - Martin Harutyunyan
- Clinic of Adults’ Oncology and Chemotherapy at Yeolyan Hematology and Oncology Center, 7 Nersisyan St., Yerevan 0014, Armenia; (S.B.); (L.S.); (D.Z.); (M.H.)
- Yeolyan Hematology and Oncology Center, 7 Nersisyan St., Yerevan 0014, Armenia;
| | - Hasmik Nersoyan
- National Center of Oncology of Armenia, 76 Fanarjyan St., Yerevan 0052, Armenia; (A.J.); (H.N.); (A.S.); (A.G.)
- Clinical Research and Cancer Registry Department, National Center of Oncology after V.A. Fanarjian, 76 Fanarjyan St., Yerevan 0052, Armenia
| | - Arevik Stepanyan
- National Center of Oncology of Armenia, 76 Fanarjyan St., Yerevan 0052, Armenia; (A.J.); (H.N.); (A.S.); (A.G.)
- Clinical Research and Cancer Registry Department, National Center of Oncology after V.A. Fanarjian, 76 Fanarjyan St., Yerevan 0052, Armenia
| | - Armenuhi Galstyan
- National Center of Oncology of Armenia, 76 Fanarjyan St., Yerevan 0052, Armenia; (A.J.); (H.N.); (A.S.); (A.G.)
- Diagnostic Service of the National Center of Oncology, 76 Fanarjyan St., Yerevan 0052, Armenia
| | - Samvel Danielyan
- Yeolyan Hematology and Oncology Center, 7 Nersisyan St., Yerevan 0014, Armenia;
| | - Armen Muradyan
- Department of General Oncology, Yerevan State Medical University after M. Heratsi, 2 Koryun St., Yerevan 0025, Armenia; (N.K.); (A.A.); (N.M.); (A.M.); (G.J.)
| | - Gagik Jilavyan
- Department of General Oncology, Yerevan State Medical University after M. Heratsi, 2 Koryun St., Yerevan 0025, Armenia; (N.K.); (A.A.); (N.M.); (A.M.); (G.J.)
- National Center of Oncology of Armenia, 76 Fanarjyan St., Yerevan 0052, Armenia; (A.J.); (H.N.); (A.S.); (A.G.)
- Department of Gynecologic Oncology, National Center of Oncology of Armenia, 76 Fanarjyan St., Yerevan 0052, Armenia
| |
Collapse
|
|
2
|
Thaker K, Chi Y, Birkhoff S, He D, Donovan H, Rosenblum L, Brusilovsky P, Hui V, Lee YJ. Exploring Resource-Sharing Behaviors for Finding Relevant Health Resources: Analysis of an Online Ovarian Cancer Community. JMIR Cancer 2022; 8:e33110. [PMID: 35258465 PMCID: PMC9044146 DOI: 10.2196/33110] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 01/28/2022] [Accepted: 02/19/2022] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND Online health communities (OHCs) provide patients and survivors of ovarian cancer (OvCa) and their caregivers with help beyond traditional support channels, such as health care providers and clinicians. OvCa OHCs promote connections and exchanges of information among users with similar experiences. Users often exchange information, which leads to the sharing of resources in the form of web links. Although OHCs are important platforms for health management, concerns exist regarding the quality and relevance of shared resources. Previous studies have examined different aspects of resource-sharing behaviors, such as the purpose of sharing, the type of shared resources, and peer user reactions to shared resources in OHCs to evaluate resource exchange scenarios. However, there is a paucity of research examining whether resource-sharing behaviors can ultimately determine the relevance of shared resources. OBJECTIVE This study aimed to examine the association between OHC resource-sharing behaviors and the relevance of shared resources. We analyzed three aspects of resource-sharing behaviors: types of shared resources, purposes of sharing resources, and OHC users' reactions to shared resources. METHODS Using a retrospective design, data were extracted from the National Ovarian Cancer Coalition discussion forum. The relevance of a resource was classified into three levels: relevant, partially relevant, and not relevant. Resource-sharing behaviors were identified through manual content analysis. A significance test was performed to determine the association between resource relevance and resource-sharing behaviors. RESULTS Approximately 48.3% (85/176) of the shared resources were identified as relevant, 29.5% (52/176) as partially relevant, and 22.2% (39/176) as irrelevant. The study established a significant association between the types of shared resources (χ218=33.2; P<.001) and resource relevance (through chi-square tests of independence). Among the types of shared resources, health consumer materials such as health news (P<.001) and health organizations (P=.02) exhibited significantly more relevant resources. Patient educational materials (P<.001) and patient-generated resources (P=.01) were more significantly associated with partially relevant and irrelevant resources, respectively. Expert health materials, including academic literature, were only shared a few times but had significantly (P<.001) more relevant resources. A significant association (χ210=22.9; P<.001) was also established between the purpose of resource sharing and overall resource relevance. Resources shared with the purpose of providing additional readings (P=.01) and pointing to resources (P=.03) had significantly more relevant resources, whereas subjects for discussion and staying connected did not include any relevant shared resources. CONCLUSIONS The associations found between resource-sharing behaviors and the relevance of these resources can help in collecting relevant resources, along with the corresponding information needs from OvCa OHCs, on a large scale through automation. The results from this study can be leveraged to prioritize the resources required by survivors of OvCa and their caregivers, as well as to automate the search for relevant shared resources in OvCa OHCs.
Collapse
Affiliation(s)
- Khushboo Thaker
- School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States
| | - Yu Chi
- School of Information Science, University of Kentucky, Lexington, KY, United States
| | - Susan Birkhoff
- School of Nursing, University of Pittsburgh, Pittsburgh, PA, United States
| | - Daqing He
- School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States
| | - Heidi Donovan
- School of Nursing, University of Pittsburgh, Pittsburgh, PA, United States
| | - Leah Rosenblum
- School of Nursing, University of Pittsburgh, Pittsburgh, PA, United States
| | - Peter Brusilovsky
- School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, United States
| | - Vivian Hui
- School of Nursing, University of Pittsburgh, Pittsburgh, PA, United States
| | - Young Ji Lee
- School of Nursing, University of Pittsburgh, Pittsburgh, PA, United States
| |
Collapse
|
|
3
|
Ganguli R, Franklin J, Yu X, Lin A, Heffernan DS. Machine learning methods to predict presence of residual cancer following hysterectomy. Sci Rep 2022; 12:2738. [PMID: 35177700 PMCID: PMC8854708 DOI: 10.1038/s41598-022-06585-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Accepted: 01/24/2022] [Indexed: 12/24/2022] Open
Abstract
Surgical management for gynecologic malignancies often involves hysterectomy, often constituting the most common gynecologic surgery worldwide. Despite maximal surgical and medical care, gynecologic malignancies have a high rate of recurrence following surgery. Current machine learning models use advanced pathology data that is often inaccessible within low-resource settings and are specific to singular cancer types. There is currently a need for machine learning models to predict non-clinically evident residual disease using only clinically available health data. Here we developed and tested multiple machine learning models to assess the risk of residual disease post-hysterectomy based on clinical and operative parameters. Data from 3656 hysterectomy patients from the NSQIP dataset over 14 years were used to develop models with a training set of 2925 patients and a validation set of 731 patients. Our models revealed the top postoperative predictors of residual disease were the initial presence of gross abdominal disease on the diaphragm, disease located on the bowel mesentery, located on the bowel serosa, and disease located within the adjacent pelvis prior to resection. There were no statistically significant differences in performances of the top three models. Extreme gradient Boosting, Random Forest, and Logistic Regression models had comparable AUC ROC (0.90) and accuracy metrics (87–88%). Using these models, physicians can identify gynecologic cancer patients post-hysterectomy that may benefit from additional treatment. For patients at high risk for disease recurrence despite adequate surgical intervention, machine learning models may lay the basis for potential prospective trials with prophylactic/adjuvant therapy for non-clinically evident residual disease, particularly in under-resourced settings.
Collapse
Affiliation(s)
- Reetam Ganguli
- Brown University, Providence, USA.,Department of Surgery, Rhode Island Hospital, Brown University, Providence, USA
| | - Jordan Franklin
- Department of Computer Sciences, Georgia Institute of Technology, Atlanta, USA
| | - Xiaotian Yu
- Department of Mathematics, University of Virginia, Charlottesville, USA
| | - Alice Lin
- Warren Alpert Medical School, Providence, USA.,Department of Surgery, Rhode Island Hospital, Brown University, Providence, USA
| | - Daithi S Heffernan
- Brown University, Providence, USA. .,Warren Alpert Medical School, Providence, USA. .,Department of Surgery, Rhode Island Hospital, Brown University, Providence, USA. .,Division of Trauma/Surgical Critical Care, Division of Surgical Research, Department of Surgery, Rhode Island Hospital, Brown University, Room 207, Aldrich Building, 593 Eddy Street, Providence, RI, 02903, USA.
| |
Collapse
|