Why do some patients with stage 1A and 1B endometrial endometrioid carcinoma experience recurrence? A retrospective study in search of prognostic factors

Objectives: Endometrial endometrioid carcinoma (EEC) is the most encountered subtype of endometrial cancer (EC). Our study aimed to investigate the factors affecting recurrence in patients with stage 1A and 1B EEC. Material and methods: Our study included 284 patients diagnosed with the International Federation of Gynecology and Obstetrics stage 1A/1B EEC in our center from 2010 to 2018. The clinicopathological characteristics of the patients were obtained retrospectively from their electronic files. Results: The median age of the patients was 60 years (range 31–89). The median follow-up time of the patients was 63.6 months (range 3.3–185.6). Twenty-two (7.74%) patients relapsed during follow-up. Among the relapsed patients, 59.1% were at stage 1A ECC, and 40.9% were at stage 1B. In our study, the one-, three-, and five-year recurrence-free survival (RFS) rates were 98.9%, 95.4%, and 92.9%, respectively. In the multivariate analysis, grade and tumor size were found to be independent parameters of RFS in all stage 1 EEC patients. Furthermore, the Ki-67 index was found to affect RFS in stage 1A EEC patients, and tumor grade affected RFS in stage 1B EEC patients. In the time-dependent receiver operating characteristic curve analysis, the statistically significant cut-off values were determined for tumor size and Ki-67 index in stage 1 EEC patients. Conclusions: Stage 1-EEC patients in the higher risk group in terms of tumor size, Ki-67, and grade should be closely monitored for recurrence. Defining the prognostic factors for recurrence in stage 1 EEC patients may lead to changes in follow-up algorithms

The impact of Ki-67 index, squamous differentiation, and several clinicopathologic parameters on the recurrence of low and intermediate-risk endometrial cancer

Endometrial endometrioid carcinoma (EEC) represents approximately 75-80% of endometrial carcinoma cases. Three hundred and thirty-six patients with EEC followed-up in the authors’ medical center between 2010 and 2018 were included in our study. Two hundred and seventy-two low and intermediate EEC patients were identified using the European Society for Medical Oncology criteria and confirmed by histopathological examination. Recurrence was reported in 17 of these patients. The study group consisted of patients with relapse. A control group of 51 patients was formed at a ratio of 3:1 according to age, stage, and grade, similar to that in the study group. Of the 17 patients with recurrent disease, 13 patients (76.5%) were Stage 1A, and 4 patients (23.5%) were Stage 1B. No significant difference was found in age, stage, and grade between the case and control groups (p > 0.05). Body mass index, parity, tumor size, lower uterine segment involvement, SqD, and Ki-67 index with p<0.25 in the univariate logistic regression analysis were included in the multivariate analysis. Ki-67 was statistically significant in multivariate analysis (p = 0.018); however, there was no statistical significance in SqD and other parameters. Our data suggest that the Ki-67 index rather than SqD needs to be assessed for recurrence in patients with low- and intermediate-risk EEC

Binary classification of pumpkin (Cucurbita pepo L.) seeds based on quality features using machine learning algorithms

Mass, size, and shape attributes are important for the design of planters, breeding studies, and quality assessment. In recent years, machinery design and system development studies have taken these factors into consideration. The aim of this study is to explore classification models for four pumpkin seed varieties according to their physical characteristics by machine learning. Binary classification is important because it ensures that the quality characteristics of the seeds are very similar to each other. The pumpkin seed varieties of Develi, Sena Hanım, Türkmen, and Mertbey were discriminated in pairs. Five machine learning algorithms (Naïve Bayes, NB; support vector machine, SVM; random forest, RF; multilayer perceptron, MLP; and kNN, k-nearest neighbors) were applied to assess the classification performance. In all pairs, the pumpkin seed varieties of Develi and Mertbey were discriminated with the highest accuracies of 85.00% for the MLP model and 84.50% for the SVM model and 83.50% for the RF. In the MLP algorithm, TP rate reached to 0.790 for Develi and 0.910 for Mertbey, Precision to 0.898 for Develi and 0.813 for Mertbey, F-measure to 0.840 for Develi and 0.858 for Mertbey, PRC area to 0.894 for Develi and 0.896 for Mertbey, and ROC area to 0.907 for both varieties. Variety of pairs was followed by Sena Hanım and Türkmen (84.50%, MLP) and Türkmen and Mertbey (82.50%, SVM). For the selected input attributes, the highest mass (0.23 g), length (22.08 for Mertbey, 21.43 for Sena Hanım), and geometric mean diameter (8.79 mm) values were obtained from Sena Hanım variety, while shape index (3.40) from Mertbey variety. Multivariate statistical results showed that differences in attributes were significant (p < 0.01). Wilks’ lambda statistics found that the portion of the unexplained difference between groups was 46.60%. Develi and Sena Hanım varieties with the lowest Mahalanobis distance values had similar characteristics. Present results revealed that SVM and MLP may be used effectively and objectively for the classification of pumpkin seed varieties

Comparison of the efficacy of sunitinib and pazopanib in patients with advanced non-clear renal cell carcinoma

Non-clear cell renal cell carcinoma (non-ccRCC) is a highly heterogeneous disease group, accounting for approximately 25% of all RCC cases. Due to its rarity and especially heterogeneity, phase III trial data is limited and treatment options generally follow those of clear cell RCC. In the literature, there exists a number of studies with sunitinib, cabozantinib, and everolimus, but data on the efficacy of pazopanib are limited. Our aim in this study was to compare the efficacy of pazopanib and sunitinib, in a multicenter retrospective cohort of non-ccRCC patients. Our study included patients diagnosed with non-ccRCC who received pazopanib or sunitinib treatment as first-line therapy from 22 tertiary hospitals. We compared the progression-free survival (PFS), overall survival (OS), and response rates of pazopanib and sunitinib treatments. Additionally, we investigated prognostic factors in non-ccRCC. PFS and response rates of sunitinib and pazopanib were found to be similar, while a numerical difference was observed in OS. Being 65 years and older, being in the intermediate or poor risk group according to the International Metastatic Renal Cell Carcinoma Database Consortium, having liver metastases, presence of a sarcomatoid component, and having de novo metastatic disease were found to be significantly associated with shorter PFS. Pazopanib treatment appears to have similar efficacy in the treatment of non-ccRCC compared to sunitinib. Though randomized controlled trials are lacking and will probably be never be available, we suggest that pazopanib could be a preferred agent like sunitinib and cabozantinib