PURPOSE Standard first-line therapy for EGFR-mutant advanced non–small-cell lung cancer (NSCLC) is an epidermal growth factor receptor (EGFR)–directed oral tyrosine kinase inhibitor. Adding pemetrexed and carboplatin chemotherapy to an oral tyrosine kinase inhibitor may improve outcomes. PATIENTS AND METHODS This was a phase III randomized trial in patients with advanced NSCLC harboring an EGFR-sensitizing mutation and a performance status of 0 to 2 who were planned to receive first-line palliative therapy. Random assignment was 1:1 to gefitinib 250 mg orally per day (Gef) or gefitinib 250 mg orally per day plus pemetrexed 500 mg/m2 and carboplatin area under curve 5 intravenously every 3 weeks for four cycles, followed by maintenance pemetrexed (gefitinib plus chemotherapy [Gef+C]). The primary end point was progression-free survival (PFS); secondary end points included overall survival (OS), response rate, and toxicity. RESULTS Between 2016 and 2018, 350 patients were randomly assigned to Gef (n = 176) and Gef+C (n = 174). Twenty-one percent of patients had a performance status of 2, and 18% of patients had brain metastases. Median follow-up time was 17 months (range, 7 to 30 months). Radiologic response rates were 75% and 63% in the Gef+C and Gef arms, respectively ( P = .01). Estimated median PFS was significantly longer with Gef+C than Gef (16 months [95% CI, 13.5 to 18.5 months] v 8 months [95% CI, 7.0 to 9.0 months], respectively; hazard ratio for disease progression or death, 0.51 [95% CI, 0.39 to 0.66]; P < .001). Estimated median OS was significantly longer with Gef+C than Gef (not reached v 17 months [95% CI, 13.5 to 20.5 months]; hazard ratio for death, 0.45 [95% CI, 0.31 to 0.65]; P < .001). Clinically relevant grade 3 or greater toxicities occurred in 51% and 25% of patients in the Gef+C and Gef arms, respectively ( P < .001). CONCLUSION Adding pemetrexed and carboplatin chemotherapy to gefitinib significantly prolonged PFS and OS but increased toxicity in patients with NSCLC.
In recent years, there has been increasing interest in the influence of body composition on oncological patient outcomes. Visceral obesity, sarcopenia and sarcopenic obesity have been identified as adverse factors in cancer patients. Imaging quantification of body composition such as lean muscle mass and fat distribution is a potentially valuable tool. This review describes the following imaging techniques that may be used to assess body composition: dual-energy X-ray absorptiometry (DXA), computed tomography (CT) and magnetic resonance imaging (MRI). CT and MRI are acquired as part of oncological patient care, thus providing an opportunity to integrate body composition assessment into the standard clinical pathway and allowing supportive care to be commenced as appropriate to improve outcome.Main Messages• Sarcopenia, sarcopenic obesity and visceral obesity are adverse prognostic factors in cancer patients.• CT and MRI are the current gold standard in body composition evaluation.• Body composition may affect chemotherapy tolerance and toxicities.
BACKGROUND:Because the addition of nimotuzumab to chemoradiation in patients with locally advanced head and neck cancer improved outcomes in a phase 2 study, the authors conducted a phase 3 study to confirm these findings. METHODS: This openlabel, investigator-initiated, phase 3, randomized trial was conducted from 2012 to 2018. Adult patients with locally advanced head and neck cancer who were fit for radical chemoradiation were randomized 1:1 to receive either radical radiotherapy (66-70 grays) with concurrent weekly cisplatin (30 mg/m 2 ) (CRT) or the same schedule of CRT with weekly nimotuzumab (200 mg) (NCRT). The primary endpoint was progression-free survival (PFS); key secondary endpoints were disease-free survival (DFS), duration of locoregional control (LRC), and overall survival (OS). An intent-to-treat analysis also was performed. RESULTS: In total, 536 patients were allocated equally to both treatment arms. The median follow-up was 39.13 months. The addition of nimotuzumab improved PFS (hazard ratio [HR], 0.69; 95% CI, 0.53-0.89; P = .004), LRC (HR, 0.67; 95% CI, 0.50-0.89; P = .006), and DFS (HR, 0.71; 95% CI, 0.55-0.92; P = .008) and had a trend toward improved OS (HR, 0.84; 95% CI, 0.65-1.08; P = .163). Grade 3 through 5 adverse events were similar between the 2 arms, except for a higher incidence of mucositis in the NCRT arm (66.7% vs 55.8%; P = .01). CONCLUSIONS: The addition of nimotuzumab to concurrent weekly CRT improves PFS, LRC, and DFS. This combination provides a novel alternative therapeutic option to a 3-weekly schedule of 100 mg/m 2 cisplatin in patients with locally advanced head and neck cancer who are treated with radical-intent CRT. Cancer 2019;125:3184-3197.
The predictive accuracy of MRI-based nomograms was excellent for SHH and encouraging for Group 4 medulloblastoma. Further work is needed for Group 3 and WNT-pathway medulloblastoma.
Glioblastoma is a WHO grade IV brain tumor, which leads to poor overall survival (OS) of patients. For precise surgical and treatment planning, OS prediction of glioblastoma (GBM) patients is highly desired by clinicians and oncologists. Radiomic research attempts at predicting disease prognosis, thus providing beneficial information for personalized treatment from a variety of imaging features extracted from multiple MR images. In this study, first-order, intensity-based volume and shape-based and textural radiomic features are extracted from fluid-attenuated inversion recovery (FLAIR) and T1ce MRI data. The region of interest is further decomposed with stationary wavelet transform with low-pass and high-pass filtering. Further, radiomic features are extracted on these decomposed images, which helped in acquiring the directional information. The efficiency of the proposed algorithm is evaluated on Brain Tumor Segmentation (BraTS) challenge training, validation, and test datasets. The proposed approach achieved 0.695, 0.571, and 0.558 on BraTS training, validation, and test datasets. The proposed approach secured the third position in BraTS 2018 challenge for the OS prediction task.
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