3-16.0), the median OS was 45.4months (95% CI 37.4-NA), and the RR was 56.0% (95% CI 42.3-68.8). Adverse events of grade ≥ 3 that occurred in ≥ 5% of cases were neutropenia in 6 patients (12%), peripheral sensory neuropathy in 5 patients (10%), diarrhea in 4 patients (8%), hypertension in 4 patients (8%), anorexia in 3 patients (6%) and allergic reactions in 3 patients (6%).
First-line chemotherapy with re-introduction of OX more than 6months after adjuvant chemotherapy including OX can be used safely with expected efficacy for relapsed colon cancer patients.
First-line chemotherapy with re-introduction of OX more than 6 months after adjuvant chemotherapy including OX can be used safely with expected efficacy for relapsed colon cancer patients.
To evaluate the diagnostic performance of dual-energy computed tomography (DECT) with regard to its post-processing techniques, namely linear blending (LB), iodine maps (IM), and virtual monoenergetic (VM) reconstructions, in diagnosing acute pulmonary embolism (PE).
This meta-analysis was conducted according to PRISMA. A systematic search on MEDLINE and EMBASE was performed in December 2019, looking for articles reporting the diagnostic performance of DECT on a per-patient level. Diagnostic performance meta-analyses were conducted grouping study parts according to DECT post-processing methods. Correlations between radiation or contrast dose and publication year were appraised.
Seventeen studies entered the analysis. Only lobar and segmental acute PE were considered, subsegmental acute PE being excluded from analysis due to data heterogeneity or lack of data. Lenvatinib research buy LB alone was assessed in 6 study parts accounting for 348 patients, showing a pooled sensitivity of 0.87 and pooled specificity of 0.93. LB and IMis not superior to that reported in literature for single-energy CT (0.83 sensitivity and 0.96 specificity). • Dual-energy CT did not yield substantial advantages in the identification of patients with acute pulmonary embolism compared to single-energy techniques.
• Dual-energy CT displayed pooled sensitivity and specificity of 0.87 and 0.93 for linear blending alone, 0.89 and 0.90 for linear blending and iodine maps, and 0.90 and 0.90 for linear blending iodine maps, and virtual monoenergetic reconstructions. • The performance of dual-energy CT for patient management is not superior to that reported in literature for single-energy CT (0.83 sensitivity and 0.96 specificity). • Dual-energy CT did not yield substantial advantages in the identification of patients with acute pulmonary embolism compared to single-energy techniques.
To evaluate a deep learning-based model using model-generated segmentation masks to differentiate invasive pulmonary adenocarcinoma (IPA) from preinvasive lesions or minimally invasive adenocarcinoma (MIA) on CT, making comparisons with radiologist-derived measurements of solid portion size.
Four hundred eleven subsolid nodules (SSNs) (120 preinvasive lesions or MIAs and 291 IPAs) in 333 patients who underwent surgery between June 2010 and August 2016 were retrospectively included to develop the model (370 SSNs in 293 patients for training and 41 SSNs in 40 patients for tuning). Ninety SSNs of 2cm or smaller (45 preinvasive lesions or MIAs and 45 IPAs) resected in 2018 formed a validation set. Six radiologists measured the solid portion of each nodule. Performances of the model and radiologists were assessed using receiver operating characteristics curve analysis.
The deep learning model differentiated IPA from preinvasive lesions or MIA with areas under the curve (AUCs) of 0.914, 0.956, and 0.833 for t = 0.97). • SSNs with a solid portion measuring > 10mm on CT showed a high probability of being IPA (positive predictive value, 93.5-100.0%).
10 mm on CT showed a high probability of being IPA (positive predictive value, 93.5-100.0%).
To evaluate the effect of a commercial deep learning algorithm on the image quality of chest CT, focusing on the upper abdomen.
One hundred consecutive patients who simultaneously underwent contrast-enhanced chest and abdominal CT were collected. The radiation dose was optimized for each scan (mean CTDI
chest CT, 3.19 ± 1.53 mGy; abdominal CT, 7.10 ± 1.88 mGy). Three image sets were collected chest CT reconstructed with an adaptive statistical iterative reconstruction (ASiR-CHT; 50% blending), chest CT with a deep learning algorithm (DLIR-CHT), and abdominal CT with ASiR (ASiR-ABD; 40% blending). Afterwards, the images covering the upper abdomen were extracted, and image noise, the signal-to-noise ratio (SNR), and the contrast-to-noise ratio (CNR) were measured. For subjective evaluation, three radiologists independently assessed noise, spatial resolution, presence of artifacts, and overall image quality. Additionally, readers selected the most preferable reconstruction technique among three image sets econstructed contrast-enhanced chest CT reconstructed using a standard ASiR-reconstructed abdominal CT. • Reconstruction algorithm-induced distortion artifacts were more frequently observed on deep learning algorithm-reconstructed images, but diagnostic difficulty was reported in only 0.3% of cases.
• With less then 50% radiation dose, a deep learning algorithm applied to contrast-enhanced chest CT exhibited better image noise and signal-to-noise ratio than standard abdominal CT with the ASiR technique. • Pooled readers mostly preferred deep learning algorithm-reconstructed contrast-enhanced chest CT reconstructed using a standard ASiR-reconstructed abdominal CT. • Reconstruction algorithm-induced distortion artifacts were more frequently observed on deep learning algorithm-reconstructed images, but diagnostic difficulty was reported in only 0.3% of cases.Camurati-Engelmann disease (CED) is a rare, progressive diaphyseal dysplasia characterized as diaphyseal hyperostosis and sclerosis of the long bones. Corticosteroids, bisphosphonates, and losartan have been reported to be effective systemic medications used to reduce CED symptoms. There are no reports of osteoblastoma in patients with CED, and osteoblastoma in the distal radius is rare. We present a patient diagnosed with CED, based on radiological and histological examinations, at 11 years old. At 22 years old, she experienced severe pain in her right forearm and was treated with bisphosphonate, losartan, and prednisolone; however, the pain continued. An expansive and sclerotic lesion at the distal radius was observed on radiography. A follow-up plain radiograph indicated that the lesion was growing. Fluorodeoxyglucose positron emission tomography revealed solitary, intense radiotracer uptake, and a biopsy and surgical resection were performed due to suspected malignancy. Pathologic analysis showed anastomosing bony trabeculae rimmed by osteoblasts observed in a loose fibrovascular stroma.Lenvatinib research buy
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