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CAD is feasible to differentiate the breast cancer subtypes, automatical segmentation were feasible by Unet model and the extracted texture features from breast MR imaging with the clinical features can be used to help differentiating the molecular subtype. Moreover, in the clinical features, BPE and age characteristics have the best potential for subtype.

CAD is feasible to differentiate the breast cancer subtypes, automatical segmentation were feasible by Unet model and the extracted texture features from breast MR imaging with the clinical features can be used to help differentiating the molecular subtype. Moreover, in the clinical features, BPE and age characteristics have the best potential for subtype.Cholangiocarcinoma (CCA), a high mortality malignant carcinoma characterized by advanced disease and frequent recurrence, constitutes a major challenge for treatment and prognosis. AT-rich interaction domain 1A (ARID1A) variation is a distinct genetic entity in CCA, getting mounting concerns recently. Here, we comprehensively reviewed the clinical significance and molecular mechanisms of ARID1A alterations in CCA. Based on the independent data derived from 29 relevant studies, the variation rate of ARID1A in intrahepatic and extrahepatic CCA is reported at 6.9-68.2% and 5-55%, respectively. Most of the included studies (28/29, 96.6%) suggest that ARID1A serves as a tumor suppressor in CCA. ARID1A variation may be an important prognostic indicator to predict disease mortality, metastasis, and recurrence in patients with CCA. Multifactorial molecular mechanisms are involved in the relationship between ARID1A variations and the pathogenesis and pathophysiology of CCA, including disruption of the cell cycle, chromatin remodeling, oxidative stress damage, DNA hypermethylation, and the interaction of multiple genes being affected. This review describes that ARID1A variation might be a potential diagnostic and prognostic biomarker for CCA. Future diagnoses and treatments targeting ARID1A hint towards a precision medicine strategy in the management of CCA.In recent years, the clinical importance of immunotherapy has been demonstrated in the treatment of extensive-stage small-cell lung cancer (ES-SCLC). However, immune checkpoint inhibitors (ICIs) have been shown to cause immune-related adverse events (irAEs), including autoimmune encephalitis. Here, we describe th treatment of a patient with ES-SCLC who developed immune-related encephalitis. A 68-year-old Japanese woman with ES-SCLC treated with carboplatin plus etoposide plus durvalumab 20 days earlier was admitted to our hospital with a high fever and anorexia. Her symptoms gradually worsened over time, and she had a headache daily and showed reduced levels of consciousness. An electroencephalogram showed diffuse slow waves, and there was a slight increase in cell counts and an increase in protein levels in the cerebrospinal fluid. The patient was diagnosed with durvalumab-associated encephalitis. Her symptoms improved immediately after steroid pulse therapy. Following steroid pulse therapy, oral prednisolone (1 mg/kg) was administered, and then, the dose was gradually reduced. Subsequently, treatment with carboplatin plus etoposide without durvalumab was restarted. learn more In conclusion, this study shows the efficacy of steroid therapy in the treatment of durvalumab-induced encephalitis in ES-SCLC.

Clinical evidence suggests radiation induces changes in the brain microenvironment that affect subsequent response to treatment. This study investigates the effect of previous radiation, delivered six weeks prior to orthotopic tumor implantation, on subsequent tumor growth and therapeutic response to anti-PD-L1 therapy in an intracranial mouse model, termed the Radiation Induced Immunosuppressive Microenvironment (RI

M) model.

C57Bl/6 mice received focal (hemispheric) single-fraction, 30-Gy radiation using the Leksell GammaKnife

Perfexion™, a dose that does not produce frank/gross radiation necrosis. Non-irradiated GL261 glioblastoma tumor cells were implanted six weeks later into the irradiated hemisphere. Lesion volume was measured longitudinally by

MRI. In a separate experiment, tumors were implanted into either previously irradiated (30 Gy) or non-irradiated mouse brain, mice were treated with anti-PD-L1 antibody, and Kaplan-Meier survival curves were constructed. Mouse brains were assessed by c. IHC and FACS analyses demonstrate increased frequency of activated microglia, which correlates with loss of sensitivity to checkpoint immunotherapy. Given that standard-of-care for primary brain tumor following resection includes concurrent radiation and chemotherapy, these striking observations strongly motivate detailed assessment of the late effects of the RI2M on tumor growth and therapeutic efficacy.

To establish a machine-learning-derived nomogram based on radiomic features and clinical factors to predict post-surgical 2-year progression-free survival (PFS) in patients with lung adenocarcinoma.

Patients with >2 years post-surgical prognosis results of lung adenocarcinoma were included in Hospital-1 for model training (n = 100) and internal validation (n = 50), and in Hospital-2 for external testing (n = 50). A total of 1,672 radiomic features were extracted from 3D segmented CT images. The Rad-score was established using random survival forest by accumulating and weighting the top-20 imaging features contributive to PFS. A nomogram for predicting PFS was established, which comprised the Rad-score and clinical factors highly relevant to PFS.

In the training, internal validation, and external test groups, 69/100 (69%), 37/50 (74%) and 36/50 (72%) patients were progression-free at two years, respectively. According to the Rad-score, the integral of area under the curve (iAUC) for discriminating high and low risk of progression was 0.92 (95%CI 0.77-1.0), 0.70 (0.41-0.98) and 0.90 (0.65-1.0), respectively. The C-index of Rad-score was 0.781 and 0.860 in the training and external test groups, higher than 0.707 and 0.606 for TNM stage, respectively. The nomogram integrating Rad-score and clinical factors (lung nodule type, cM stage and histological type) achieved a C-index of 0.845 and 0.837 to predict 2-year PFS, respectively, significantly higher than by only radiomic features (all p < 0.01).

The nomogram comprising CT-derived radiomic features and risk factors showed a high performance in predicting post-surgical 2-year PFS of patients with lung adenocarcinoma, which may help personalize the treatment decisions.

The nomogram comprising CT-derived radiomic features and risk factors showed a high performance in predicting post-surgical 2-year PFS of patients with lung adenocarcinoma, which may help personalize the treatment decisions.