دورية أكاديمية
Ovarian Cancer-Self Assessment: An Innovation for Early Detection and Risk Assessment of Ovarian Cancer.
| العنوان: | Ovarian Cancer-Self Assessment: An Innovation for Early Detection and Risk Assessment of Ovarian Cancer. |
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| المؤلفون: | Salima S; Department of Obstetrics and Gynecology, Faculty of Medicine, Universitas Padjajaran, Bandung, Indonesia., Rachmawati A; Department of Obstetrics and Gynecology, Faculty of Medicine, Universitas Padjajaran, Bandung, Indonesia., Harsono AB; Department of Obstetrics and Gynecology, Faculty of Medicine, Universitas Padjajaran, Bandung, Indonesia., Erfiandi F; Department of Obstetrics and Gynecology, Faculty of Medicine, Universitas Padjajaran, Bandung, Indonesia., Fauzi H; Biomedical Engineering, Faculty of Electrical Engineering, Telkom University, Bandung, Indonesia., Prasekti H; Department of Obstetrics and Gynecology, Faculty of Medicine, Universitas Padjajaran, Bandung, Indonesia., Nurita R; Department of Obstetrics and Gynecology, Faculty of Medicine, Universitas Padjajaran, Bandung, Indonesia. |
| المصدر: | Asian Pacific journal of cancer prevention : APJCP [Asian Pac J Cancer Prev] 2022 Aug 01; Vol. 23 (8), pp. 2643-2647. Date of Electronic Publication: 2022 Aug 01. |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: Asian Pacific Organization for Cancer Prevention Country of Publication: Thailand NLM ID: 101130625 Publication Model: Electronic Cited Medium: Internet ISSN: 2476-762X (Electronic) Linking ISSN: 15137368 NLM ISO Abbreviation: Asian Pac J Cancer Prev Subsets: MEDLINE |
| أسماء مطبوعة: | Original Publication: Bangkok : Asian Pacific Organization for Cancer Prevention, |
| مواضيع طبية MeSH: | Artificial Intelligence* , Ovarian Neoplasms*/diagnosis , Ovarian Neoplasms*/pathology , Ovarian Neoplasms*/surgery, Carcinoma, Ovarian Epithelial ; Cohort Studies ; Early Detection of Cancer ; Female ; Humans ; Risk Assessment |
| مستخلص: | Objective: The modality to detect ovarian cancer at an early stage is very limited. Early diagnosis determines the prognosis. This study aimed to develop a risk assessment tool for early detection of ovarian cancer using artificial intelligence. To accomplish this, the presence of ten signs and symptoms reported by patients with ovarian cancer was assessed. Methods: This study was carried out as a cohort study of patients diagnosed with suspected ovarian tumors undergoing cytoreduction operation at Hasan Sadikin Hospital, Bandung, from December 2019 to September 2020. Compared to ovarian cancer self-assessment through questionnaire, postoperative histopathology in patients with suspected ovarian tumors. The questionnaire proceeded by artificial intelligence is grouped into risk and no risk. Statistical analyses were done using Chi-Square and Exact Fisher Test. Result: In total, 115 patients included in this study. The differences were statistically significant in terms of the six variables (abdominal bloating, nausea/vomiting, decreased of appetite, fullness, menstrual disturbance, and weight loss) ovarian cancer self-assessment compared to postoperative histopathology with a tendency towards benign ovarian tumors (p<0.05), while there was no statistically significant difference in the four variables (abdominal enlargement, abdominal pain, urinating disturbance, and defecation disturbance) (p>0.05). According to the artificial intelligence grouping, fifty-five patients were at risk, and sixty patients were not at risk. The Fifty-five risk patients were related with postoperative histopathology diagnosis (with RR 0.682 and CI 95% 0.519-0.895). Conclusion: Risk assessments based on ovarian cancer self-assessment unfortunately were not comparable to postoperative histopathology as a single predictor. Ten variables in ovarian cancer artificial intelligence self-assessment for early detection needs improvement in adding another variable like tumor marker and ultrasonography assessment. |
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| فهرسة مساهمة: | Keywords: Keywords: artificial intelligence; Malignancy; Ovarian Cancer; Screening; self-assessment |
| تواريخ الأحداث: | Date Created: 20220829 Date Completed: 20220831 Latest Revision: 20221221 |
| رمز التحديث: | 20240628 |
| مُعرف محوري في PubMed: | PMC9741892 |
| DOI: | 10.31557/APJCP.2022.23.8.2643 |
| PMID: | 36037117 |
| قاعدة البيانات: | MEDLINE |
Full Text Finder | تدمد: | 2476-762X |
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| DOI: | 10.31557/APJCP.2022.23.8.2643 |