Evaluating clinical and radiomic features for predicting lung cancer recurrence pre- and post-tumor resection.

Among patients with early-stage non-small cell lung cancer (NSCLC) undergoing surgical resection, identifying who is at high-risk of recurrence can inform clinical guidelines with respect to more aggressive follow-up and/or adjuvant therapy. While predicting recurrence based on pre-surgical resection data is ideal, clinically important pathological features are only evaluated postoperatively. Therefore, we developed two supervised classification models to assess the importance of pre- and post-surgical features for predicting 5-year recurrence.

Technologies for Prediction of Preeclampsia.

Unlabelled: was to develop technologies for predicting the development of preeclampsia (PE) based on biomedical and molecular-genetic predictors and the calculation of individual risks for this pregnancy complication.

Materials And Methods: The study involved 457 pregnant women. Of them, 147 women had chronic arterial hypertension (CAH); 109 pregnant women had CAH and secondary preeclampsia (PE); 201 patients had PE.

Lung Nodule Sizes Are Encoded When Scaling CT Image for CNN's.

Noninvasive diagnosis of lung cancer in early stages is one task where radiomics helps. Clinical practice shows that the size of a nodule has high predictive power for malignancy. In the literature, convolutional neural networks (CNNs) have become widely used in medical image analysis.

[The polymorphism of detoxification genes system genes in very preterm infants.].

his article is devoted to the comparative analysis of polymorphism of genes of the detoxification system in premature newborns with birth weight less than 1500 g and gestational age less than 32 weeks, formed bronchopulmonary dysplasia (BPD) (n=50), and without signs of BPD (n=47). A positive association between the presence in the genotype of deletion variants of glutathioneS-transferases (GSTT1 and GSTМ1) and risk of developing BPD in preterm infants.