The oncologist
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T-lymphoblastic lymphoma (T-LBL) is a highly aggressive neoplasm of lymphoblasts of T-cell origin. Although promising improvements have been recently achieved, one third of patients experience relapse or refractory T-LBL. Therefore, optimal strategies for identifying high-risk patients are urgently needed. ⋯ Optimal strategies for identifying high-risk patients with T-lymphoblastic lymphoma (T-LBL) are urgently needed. In the largest adult T-LBL cohort to date, simple, inexpensive, widely available parameters were applied and revealed that patients with lymphocyte-monocyte ratio (LMR) ≤2.8, neutrophil-lymphocyte ratio (NLR) ≥3.3, and platelet-lymphocyte ratio (PLR) ≥200 had both inferior progression-free survival and inferior overall survival (OS), in which the differences were much more remarkable in the international prognostic index score 0-2 subgroup. LMR, NLR, and PLR were integrated to generate a "complete blood count score" model, in which the 3-year OS was 84%, 53%, and 30% for low-, intermediate-, and high-risk patients, respectively.
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How to best support patients with neuroendocrine tumors (NETs) remains unclear. Improving quality of care requires an understanding of symptom trajectories. Objective validated assessments of symptoms burden over the course of disease are lacking. This study examined patterns and risk factors of symptom burden in NETs, using patient-reported outcomes. ⋯ This study used population-level, prospectively collected, validated, patient-reported outcome measures to appraise the symptoms burden and trajectory of patients with neuroendocrine tumors (NETs) after diagnosis. It is the largest and most detailed analysis of patient-reported symptoms for NETs. Patients with NETs present a high burden of symptoms at diagnosis that persists up to 5 years later, highlighting unmet needs. Early and comprehensive symptom screening and management programs are needed. This information should serve to devise pathways and policies to better support patients, evaluate supportive interventions, and assess the effectiveness of symptom management at the provider, institutional, and system levels.
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The efficacy of adjuvant targeted therapy for operable lung cancer is still under debate. Comprehensive genetic profiling is needed for detecting co-mutations in resected epidermal growth factor receptor (EGFR)-mutated lung adenocarcinoma (ADC), which may interfere the efficacy of adjuvant tyrosine kinase inhibitor (TKI) treatment. ⋯ The efficacy of adjuvant epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) therapy for lung cancer harboring EGFR mutation after surgical resection is still under debate. Next-generation sequencing of 416 cancer-relevant genes in 139 resected lung cancers revealed the co-mutational landscape with background EGFR mutation. Notably, the study identified potential EGFR TKI-resistant mutations in 34.71% of patients with a drug-sensitizing EGFR mutation and who were naive in terms of targeted therapy. A comprehensive mutation profiling of these resected tumors could facilitate in determining the applicability and efficacy of adjuvant EGFR TKI therapeutic strategy for these patients.
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Meta Analysis
A Systematic Review and Network Meta-Analysis of Regorafenib and TAS-102 in Refractory Metastatic Colorectal Cancer.
Regorafenib at different dosing strategies and TAS-102 are treatment options for refractory metastatic colorectal cancer (mCRC). We aimed to evaluate the comparative effectiveness evidence supporting these different strategies. ⋯ Regorafenib at a dose of 160 mg and TAS-102 appear to have similar efficacy in patients with refractory metastatic colorectal cancer. Regorafenib with a dose escalation strategy is superior to best-supportive care. Given its tolerability and the observed trend in survival benefit compared with regorafenib 160, dose escalation strategy of regorafenib (80+) may be the preferred option in this setting.
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Multicenter Study
Toward an Expert Level of Lung Cancer Detection and Classification Using a Deep Convolutional Neural Network.
Computed tomography (CT) is essential for pulmonary nodule detection in diagnosing lung cancer. As deep learning algorithms have recently been regarded as a promising technique in medical fields, we attempt to integrate a well-trained deep learning algorithm to detect and classify pulmonary nodules derived from clinical CT images. ⋯ The three-dimensional convolutional neural network described in this article demonstrated both high sensitivity and high specificity in classifying pulmonary nodules regardless of diameters as well as superiority compared with manual assessment. Although it still warrants further improvement and validation in larger screening cohorts, its clinical application could definitely facilitate and assist doctors in clinical practice.