Clinicopathological features and prognostic factors for survival and lymph node metastases in stage IB adenocarcinoma of the cervix

Wu, Dongchen; Zhang, Lihua; Wu, Yan-Guang; Wang, Yingchun; Beharee, Nitish; Wang, Jinhua; Wang, Ping

doi:10.31083/j.ejgo.2021.03.2300

Cited by 2 publications

(1 citation statement)

References 22 publications

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“…Feature selection obtains the most effective feature subset for classification and recognition of the many features ( Wang et al, 2010 ; Mo et al, 2020 ; Sheng et al, 2021 ; Wu et al, 2021 ). That is, it captures a set of “small but precise” classification features with a small probability of error.…”

Section: Methodsmentioning

confidence: 99%

AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs

Zhai

Zhang

et al. 2022

Front. Pharmacol.

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Antioxidant proteins can not only balance the oxidative stress in the body, but are also an important component of antioxidant drugs. Accurate identification of antioxidant proteins is essential to help humans fight diseases and develop new drugs. In this paper, we developed a friendly method AOPM to identify antioxidant proteins. 188D and the Composition of k-spaced Amino Acid Pairs were adopted as the feature extraction method. In addition, the Max-Relevance-Max-Distance algorithm (MRMD) and random forest were the feature selection and classifier, respectively. We used 5-folds cross-validation and independent test dataset to evaluate our model. On the test dataset, AOPM presented a higher performance compared with the state-of-the-art methods. The sensitivity, specificity, accuracy, Matthew’s Correlation Coefficient and an Area Under the Curve reached 87.3, 94.2, 92.0%, 0.815 and 0.972, respectively. In addition, AOPM still has excellent performance in predicting the catalytic enzymes of antioxidant drugs. This work proved the feasibility of virtual drug screening based on sequence information and provided new ideas and solutions for drug development.

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Section: Methodsmentioning

confidence: 99%

AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs

Zhai

Zhang

et al. 2022

Front. Pharmacol.

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Association between Silva pattern-based classification and endocervical adenocarcinoma: a systematic review and meta-analysis

Chen,

Han,

Wang

et al. 2024

Int J Gynecol Cancer

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ObjectiveTo determine the relationship between the Silva pattern-based classification system and endocervical adenocarcinoma.MethodsThe PubMed, Embase, Central Cochrane Library, and Web of Science databases were systematically searched for studies that investigated the correlation between the Silva classification system and the oncology prognosis or pathological features of endocervical adenocarcinoma, published in the period from January 2013 to March 2024.ResultsA total of 19 eligible studies including 3122 cases were included in this systematic review and meta-analysis. The combined death rate in the Silva A, Silva B, and Silva C patterns was 0% (95% CI 0.0% to 0.4%), 2.6% (95% CI 0.4% to 5.9%), and 14.0% (95% CI 9.4% to 19.2%), respectively; the combined recurrence rate in the Silva A, Silva B, and Silva C patterns was 0.1% (95% CI 0.0% to 1.2%), 5.1% (95% CI 1.6% to 10.0%), and 19.4% (95% CI 14.7% to 24.4%), respectively; the combined lymphovascular invasion rate in the Silva A, Silva B, and Silva C patterns was 0% (95% CI 0.0% to 0.5%), 21.0% (95% CI 16.9% to 25.4%), and 58.8% (95% CI 50.1% to 67.3%), respectively; and the combined International Federation of Gynecology and Obstetrics (FIGO) I rate in the Silva A, Silva B, and Silva C patterns was 99.3% (95% CI 97.6% to 100%), 93.7% (95% CI 86.4% to 98.7%), and 82.4% (95% CI 74.9% to 88.9%), respectively.ConclusionOur study found that Silva A was negatively correlated with death rate, while Silva C was positively correlated. There was no correlation regarding the death rate for Silva B. Based on these findings, it is suggested that the Silva pattern-based classification system can predict the prognosis of human papillomavirus (HPV)-related endocervical adenocarcinoma and assist in guiding patient treatment.

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Clinicopathological features and prognostic factors for survival and lymph node metastases in stage IB adenocarcinoma of the cervix

Cited by 2 publications

References 22 publications

AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs

AOPM: Application of Antioxidant Protein Classification Model in Predicting the Composition of Antioxidant Drugs

Association between Silva pattern-based classification and endocervical adenocarcinoma: a systematic review and meta-analysis

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