Nordentoftmaher5026

Immune checkpoint inhibitor (ICI) therapy has shown activity against melanoma brain metastases. Recently, promising results have also been reported for ICI combination therapy and ICI combined with radiotherapy. We aimed to evaluate radiologic response and adverse event rates of these therapeutic options by a systematic review and meta-analysis.

A systematic literature search of Ovid-MEDLINE and EMBASE was performed up to October 12, 2019 and included studies evaluating the intracranial objective response rates (ORRs) and/or disease control rates (DCRs) of ICI with or without radiotherapy for treating melanoma brain metastases. We also evaluated safety-associated outcomes.

Eleven studies with 14 cohorts (3 with ICI combination therapy; 5 with ICI combined with radiotherapy; 6 with ICI monotherapy) were included. ICI combination therapy pooled ORR, 53% (95% confidence interval [CI], 44-61%); DCR, 57% (95% CI, 49-66%) and ICI combined with radiotherapy (pooled ORR, 42% [95% CI, 31-54%]; DCR, 85% [95% CIcy than ICI monotherapy for treating melanoma brain metastasis. https://www.selleckchem.com/products/azd5363.html The grade 3 or 4 adverse event rate was highest with ICI combination therapy, and the CNS-related grade 3 or 4 event rate was similar. Prospective trials will be necessary to compare the efficacy of ICI combination therapy and ICI combined with radiotherapy.

To evaluate the radiological tumor response patterns and compare the response assessments based on immune-based therapeutics Response Evaluation Criteria in Solid Tumors (iRECIST) and RECIST 1.1 in metastatic clear-cell renal cell carcinoma (mccRCC) patients treated with programmed cell death-1 (PD-1) inhibitors.

All mccRCC patients treated with PD-1 inhibitors at Henan Cancer Hospital, China, between January 2018 and April 2019, were retrospectively studied. A total of 30 mccRCC patients (20 males and 10 females; mean age, 55.6 years; age range, 37-79 years) were analyzed. The target lesions were quantified on consecutive CT scans during therapy using iRECIST and RECIST 1.1. The tumor growth rate was calculated before and after therapy initiation. The response patterns were analyzed, and the differences in tumor response assessments of the two criteria were compared. The intra- and inter-observer variabilities of iRECIST and RECIST 1.1 were also analyzed.

The objective response rate throughout therapy y, and it may last for more than the recommended maximum of 8 weeks, indicating a limitation of the current strategy for immune response monitoring.

Our study confirmed that the iRECIST criteria are more capable of capturing immune-related atypical responses during immunotherapy, whereas conventional RECIST 1.1 may underestimate the benefit of PD-1 inhibitors. Pseudoprogression is not rare in mccRCC patients during PD-1 inhibitor therapy, and it may last for more than the recommended maximum of 8 weeks, indicating a limitation of the current strategy for immune response monitoring.

To compare the performance of simulated abbreviated breast MRI (AB-MRI) and full diagnostic (FD)-MRI in distinguishing between benign and malignant lesions detected by MRI and investigate the features of discrepant lesions of the two protocols.

An AB-MRI set with single first postcontrast images was retrospectively obtained from an FD-MRI cohort of 111 lesions (34 malignant, 77 benign) detected by contralateral breast MRI in 111 women (mean age, 49.8. ± 9.8; range, 28-75 years) with recently diagnosed breast cancer. Five blinded readers independently classified the likelihood of malignancy using Breast Imaging Reporting and Data System assessments. McNemar tests and area under the receiver operating characteristic curve (AUC) analyses were performed. The imaging and pathologic features of the discrepant lesions of the two protocols were analyzed.

The sensitivity of AB-MRI for lesion characterization tended to be lower than that of FD-MRI for all readers (58.8-82.4% vs. 79.4-100%), although the findings of only two readers were significantly different (

< 0.05). The specificity of AB-MRI for lesion characterization was higher than that of FD-MRI for 80% of readers (39.0-74.0% vs. 19.5-45.5%,

≤ 0.001). The AUC of AB-MRI was comparable to that of FD-MRI for all readers (

> 0.05). Fifteen percent (5/34) of the cancers were false-negatives on AB-MRI. More suspicious margins or internal enhancement on the delayed phase images were related to the discrepancies.

The overall performance of AB-MRI was similar to that of FD-MRI in distinguishing between benign and malignant lesions. AB-MRI showed lower sensitivity and higher specificity than FD-MRI, as 15% of the cancers were misclassified compared to FD-MRI.

The overall performance of AB-MRI was similar to that of FD-MRI in distinguishing between benign and malignant lesions. AB-MRI showed lower sensitivity and higher specificity than FD-MRI, as 15% of the cancers were misclassified compared to FD-MRI.

To evaluate the diagnostic performance of a deep learning algorithm for the automated detection of developmental dysplasia of the hip (DDH) on anteroposterior (AP) radiographs.

Of 2601 hip AP radiographs, 5076 cropped unilateral hip joint images were used to construct a dataset that was further divided into training (80%), validation (10%), or test sets (10%). Three radiologists were asked to label the hip images as normal or DDH. To investigate the diagnostic performance of the deep learning algorithm, we calculated the receiver operating characteristics (ROC), precision-recall curve (PRC) plots, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) and compared them with the performance of radiologists with different levels of experience.

The area under the ROC plot generated by the deep learning algorithm and radiologists was 0.988 and 0.988-0.919, respectively. The area under the PRC plot generated by the deep learning algorithm and radiologists was 0.973 and 0.618-0.958, respectively. The sensitivity, specificity, PPV, and NPV of the proposed deep learning algorithm were 98.0, 98.1, 84.5, and 99.8%, respectively. There was no significant difference in the diagnosis of DDH by the algorithm and the radiologist with experience in pediatric radiology (

= 0.180). However, the proposed model showed higher sensitivity, specificity, and PPV, compared to the radiologist without experience in pediatric radiology (

< 0.001).

The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.

The proposed deep learning algorithm provided an accurate diagnosis of DDH on hip radiographs, which was comparable to the diagnosis by an experienced radiologist.