ArticleComputational and mathematical methods in medicine2022
Establishment and Verification of Logistic Regression Model for Qualitative Diagnosis of Ovarian Cancer Based on MRI and Ultrasound Signs.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed, 11 citations in OpenAlex.
- Risk assessment of subclinical mastitis in Holstein cows in Jiangsu Province of China.Frontiers in veterinary science · 2026Article
- Machine learning and radiomics for segmentation and classification of adnexal masses on ultrasound.NPJ precision oncology · 2024Article
- Unlocking the 'ova'-coming power: immunotherapy's role in shaping the future of ovarian cancer treatment.Medical oncology (Northwood, London, England) · 2024Review
- Retracted: Establishment and Verification of Logistic Regression Model for Qualitative Diagnosis of Ovarian Cancer Based on MRI and Ultrasound Signs.Computational and mathematical methods in medicine · 2023Article
- Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis.EClinicalMedicine · 2022Article
Corrections and comments
- Retraction · 2023-12-06Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo explore the establishment and verification of logistic regression model for qualitative diagnosis of ovarian cancer based on MRI and ultrasonic signs.
method207 patients with ovarian tumors in our hospital from April 2018 to April 2021 were selected, of which 138 were used as the training group for model creation and 69 as the validation group for model evaluation. The differences of MRI and ultrasound signs in patients with ovarian cancer and benign ovarian tumor in the training group were analyzed. The risk factors were screened by multifactor unconditional logistic regression analysis, and the regression equation was established. The self-verification was carried out by subject working characteristics (ROC), and the external verification was carried out by K-fold cross verification.
resultThere was no significant difference in age, body mass index, menstruation, dysmenorrhea, times of pregnancy, cumulative menstrual years, and marital status between the two groups (
conclusionIrregular shape, unclear boundary, obvious enhancement in MRI signs, cystic or solid tumor components and irregular shape, solid-dominated shape, thick septate shape, large nipple, and abundant blood supply in ultrasound signs are independent risk factors for ovarian cancer. After verification, the diagnostic model has good accuracy and stability, which provides basis for clinical decision-making.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.