Mature Cystic Teratoma Of The Ovary Associated With Contralateral Mucinous Cystadenoma:a Case Report

Parveen, Shazia; Hakim, Seema; Fatima, Zebi

doi:10.15373/2249555x/sep2012/62

Cited by 3 publications

(3 citation statements)

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“…In our study 2 cases of mucinous cystadenomas are associated with a dermoid, also reported by others. 31,32 In our study they were mostly unilateral. They were not subtyped as endocervical and intestinal type.…”

Section: Soumini G Et Al Int J Reprod Contracept Obstet Gynecol 201mentioning

confidence: 50%

Scenario of ovarian mass lesions at a teaching hospital in Andhra Pradesh, India

Soumini

Sarella

Chaveli

et al. 2015

Int J Reprod Contracept Obstet Gynecol

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Section: Soumini G Et Al Int J Reprod Contracept Obstet Gynecol 201mentioning

confidence: 50%

Scenario of ovarian mass lesions at a teaching hospital in Andhra Pradesh, India

Soumini

Sarella

Chaveli

et al. 2015

Int J Reprod Contracept Obstet Gynecol

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“…Only serum creatinine and left ventricular ejection fraction (LVEF), according to Chicco and Jurman, may be used to predict the survival rate of patients with heart failure ( 37 ). To save the standard of care in the hospital system, Sohrabi et al used classification algorithms (i.e., DT, Artificial Neural Networks (ANN), SVM, and LR) and employed AUC and accuracy as assessment indicators ( 38 ). To predict a patient’s risk of developing heart failure, Lafta et al built a neural network classifier using the global weight of attribute contribution ( 39 ).…”

Section: Discussionmentioning

confidence: 99%

Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study

Yilmaz,

Yagin,

Colak

et al. 2024

Front. Med.

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IntroductionAcute heart failure (AHF) is a serious medical problem that necessitates hospitalization and often results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Unfortunately, there is not yet a fast and accurate laboratory test for identifying AHF. The purpose of this research is to apply the principles of explainable artificial intelligence (XAI) to the analysis of hematological indicators for the diagnosis of AHF.MethodsIn this retrospective analysis, 425 patients with AHF and 430 healthy individuals served as assessments. Patients’ demographic and hematological information was analyzed to diagnose AHF. Important risk variables for AHF diagnosis were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection. To test the efficacy of the suggested prediction model, Extreme Gradient Boosting (XGBoost), a 10-fold cross-validation procedure was implemented. The area under the receiver operating characteristic curve (AUC), F1 score, Brier score, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) were all computed to evaluate the model’s efficacy. Permutation-based analysis and SHAP were used to assess the importance and influence of the model’s incorporated risk factors.ResultsWhite blood cell (WBC), monocytes, neutrophils, neutrophil-lymphocyte ratio (NLR), red cell distribution width-standard deviation (RDW-SD), RDW-coefficient of variation (RDW-CV), and platelet distribution width (PDW) values were significantly higher than the healthy group (p < 0.05). On the other hand, erythrocyte, hemoglobin, basophil, lymphocyte, mean platelet volume (MPV), platelet, hematocrit, mean erythrocyte hemoglobin (MCH), and procalcitonin (PCT) values were found to be significantly lower in AHF patients compared to healthy controls (p < 0.05). When XGBoost was used in conjunction with LASSO to diagnose AHF, the resulting model had an AUC of 87.9%, an F1 score of 87.4%, a Brier score of 0.036, and an F1 score of 87.4%. PDW, age, RDW-SD, and PLT were identified as the most crucial risk factors in differentiating AHF.ConclusionThe results of this study showed that XAI combined with ML could successfully diagnose AHF. SHAP descriptions show that advanced age, low platelet count, high RDW-SD, and PDW are the primary hematological parameters for the diagnosis of AHF.

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“…[9] But the relationship between the pathogenesis of the two lesions occurring in same or contralateral ovary has not yet been answered.…”

Section: Discussionmentioning

confidence: 99%

Collision Tumor of Ovary Associated With Contralateral Benign Cystic Teratoma in Primigravida: A Case to Remember

Thakur¹,

Kishore²,

Bhardwaj³

et al. 2014

jemds

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Collision tumors involving ovaries are extremely rare. Here, we report this rare entity in a 23 years old primigravida presented with 12 weeks pregnancy and abdominal pain. Ultrasonography showed large right adnexal mass measuring 20 x 13 cm. having solid area with multiple cysts and bulky left ovary of 6 x 4.6 cm. Normal ovarian tissue was preserved after bilateral ovarian cystectomy. Grossly, right ovarian tissue revealed multiloculation and mucin filled cysts. Left ovarian cyst was filled with hairy pultaceous material. Histological diagnosis of collision tumor of right ovary comprising hemorrhagic mucinous cystadenoma and benign cystic teratoma along with dermoid cyst in left ovary was rendered. The pathogenesis of association of mature cystic teratoma with a mucinous cystadenoma has yet to be answered and the choice of management necessitates a weighing of risks based on characterization of the adnexal mass and gestational age.

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Mature Cystic Teratoma Of The Ovary Associated With Contralateral Mucinous Cystadenoma:a Case Report

Cited by 3 publications

References 1 publication

Scenario of ovarian mass lesions at a teaching hospital in Andhra Pradesh, India

Scenario of ovarian mass lesions at a teaching hospital in Andhra Pradesh, India

Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study

Collision Tumor of Ovary Associated With Contralateral Benign Cystic Teratoma in Primigravida: A Case to Remember

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