Objective To investigate the role of chest computed tomography (CT) examinations acquired early after initial onset of symptoms in predicting disease course in coronavirus disease 2019. Methods Two hundred sixty-two patients were categorized according to intensive care unit (ICU) admission, survival, length of hospital stay, and reverse transcriptase-polymerase chain reaction positivity. Mean time interval between the onset of symptoms and CT scan was 5.2 ± 2.3 days. Groups were compared using Student t test, Mann-Whitney U, and Fisher exact tests. Results In the ICU (+) and died groups, crazy paving (64% and 57.1%), bronchus distortion (68% and 66.7%), bronchiectasis-bronchiolectasis (80% and 76.2%), air trapping (52% and 52.4%) and mediastinal-hilar lymph node enlargement (52% and 52.4%) were significantly more encountered (P < 0,05). These findings were correlated with longer hospital stays (P < 0.05). There were no differences between reverse transcriptase-polymerase chain reaction-positive and -negative patients except bronchiectasis-bronchiolectasis. Conclusion Computed tomography examinations performed early after the onset of symptoms may help in predicting disease course and planning of resources, such as ICU beds.
Purpose: Graded Prognostic Assessment (GPA) is a new prognostic index for patients with brain metastases. Brain metastasis is a common site of metastasis in lung cancers. Lung cancer-specific GPA scoring system is used. We aimed to assess the prognostic and predictive significance of Graded Prognostic Assessment (GPA) score in non small-cell lung cancer patients with brain metastasis. Materials and Methods: This study was designed as a hospital-based retrospective observational case-series study. A total of 95 patients with brain metastatic NSCLC patients who were followed in two different oncology centers in Turkey between 2015 and 2021 have been included into this study. They were divided into 3 groups according to their GPA scores. Results: The median age of the patients was 62 (range 44-89) years The patients were divided into 3 groups according to their GPA scores. 24 (25.2 %) patients had ‘’0-1’’ GPA score, 54 (56,8 %) patients had ‘’1,5-2’’ GPA score and 17 (18 %) patients had ‘’2,5-3’’ GPA score. The median follow-up time was 11 months and 89 (93.7%) patients died during follow-up. Overall survival (OS) was 8 months. Patients in the low (0-1) GPA scores had worst overall survival than those with higher GPA scores (4.7, 12.6 and 18.5 months respectively and p=0,001). Conclusion: In this study, we have shown that GPA score is useful in evaluating the prognosis of NSCLC patients with brain metastasis
This study aimed to evaluate the potential of machine learning-based models for predicting carcinogenic human papillomavirus (HPV) oncogene types using radiomics features from magnetic resonance imaging (MRI). METHODSPre-treatment MRI images of patients with cervical cancer were collected retrospectively. An HPV DNA oncogene analysis was performed based on cervical biopsy specimens. Radiomics features were extracted from contrast-enhanced T1-weighted images (CE-T1) and T2-weighted images (T2WI). A third feature subset was created as a combined group by concatenating the CE-T1 and T2WI subsets. Feature selection was performed using Pearson's correlation coefficient and wrapper-based sequential-feature selection. Two models were built with each feature subset, using support vector machine (SVM) and logistic regression (LR) classifiers. The models were validated using a five-fold cross-validation technique and compared using Wilcoxon's signed rank and Friedman's tests. RESULTSForty-one patients were enrolled in the study (26 were positive for carcinogenic HPV oncogenes, and 15 were negative). A total of 851 features were extracted from each imaging sequence. After feature selection, 5, 17, and 20 features remained in the CE-T1, T2WI, and combined groups, respectively. The SVM models showed 83%, 95%, and 95% accuracy scores, and the LR models revealed 83%, 81%, and 92.5% accuracy scores in the CE-T1, T2WI, and combined groups, respectively. The SVM algorithm performed better than the LR algorithm in the T2WI feature subset (P = 0.005), and the feature sets in the T2WI and the combined group performed better than CE-T1 in the SVM model (P = 0.033 and 0.006, respectively). The combined group feature subset performed better than T2WI in the LR model (P = 0.023). CONCLUSIONMachine learning-based radiomics models based on pre-treatment MRI can detect carcinogenic HPV status with discriminative accuracy. KEYWORDSArtificial intelligence, human papillomavirus DNA tests, machine learning, radiology, uterine cervical neoplasms C ervical cancer is the fourth most common female cancer and the second most common in women aged 15-44. 1 The etiological factor in more than 95% of cervical cancer cases is human papillomavirus (HPV). [2][3][4] Fifteen of more than 200 oncogene types are identified as high risk, and type-16 and -18 HPV infections are the most common in women with cervical cancer. 5 In addition, several studies in the literature report that HPV DNA status is From the Clinic of Radiology (O.İ.
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