Impact of Physical Activity on Cancer-Specific and Overall Survival of Patients with Colorectal Cancer

Background. Physical activity (PA) reduces incidence of colorectal cancer (CRC). Its influence on cancer-specific (CSS) and overall survival (OS) is controversial. Methods. We performed a literature-based meta-analysis (MA) of observational studies, using keywords “colorectal cancer, physical activity, and survival” in PubMed and EMBASE. No dedicated MA was found in the Cochrane Library. References were cross-checked. Pre- and postdiagnosis PA levels were assessed by MET. Usually, “high” PA was higher than 17 MET hour/week. Hazard ratios (HRs) for OS and CSS were calculated, with their 95% confidence interval. We used more conservative adjusted HRs, since variables of adjustment were similar between studies. When higher PA was associated with improved survival, HRs for detrimental events were set to <1. We used EasyMA software and fixed effect model whenever possible. Results. Seven studies (8056 participants) were included, representing 3762 men and 4256 women, 5210 colon and 1745 rectum cancers. Mean age was 67 years. HR CSS for postdiagnosis PA (higher PA versus lower) was 0.61 (0.44–0.86). The corresponding HR OS was 0.62 (0.54–0.71). HR CSS for prediagnosis PA was 0.75 (0.62–0.91). The corresponding HR OS was 0.74 (0.62–0.89). Conclusion. Higher PA predicted a better CSS. Sustained PA should be advised for CRC. OS also improved (reduced cardiovascular risk)

18F-FDG PET-Derived Textural Indices Reflect Tissue-Specific Uptake Pattern in Non-Small Cell Lung Cancer

International audiencePurposeTexture indices (TI) calculated from 18F-FDG PET tumor images show promise for predicting response to therapy and survival. Their calculation involves a resampling of standardized uptake values (SUV) within the tumor. This resampling can be performed differently and significantly impacts the TI values. Our aim was to investigate how the resampling approach affects the ability of TI to reflect tissue-specific pattern of metabolic activity.Methods18F-FDG PET were acquired for 48 naïve-treatment patients with non-small cell lung cancer and for a uniform phantom. We studied 7 TI, SUVmax and metabolic volume (MV) in the phantom, tumors and healthy tissue using the usual relative resampling (RR) method and an absolute resampling (AR) method. The differences in TI values between tissue types and cancer subtypes were investigated using Wilcoxon’s tests.ResultsMost RR-based TI were highly correlated with MV for tumors less than 60 mL (Spearman correlation coefficient r between 0.74 and 1), while this correlation was reduced for AR-based TI (r between 0.06 and 0.27 except for RLNU where r = 0.91). Most AR-based TI were significantly different between tumor and healthy tissues (pvalues <0.01 for all 7 TI) and between cancer subtypes (pvalues<0.05 for 6 TI). Healthy tissue and adenocarcinomas exhibited more homogeneous texture than tumor tissue and squamous cell carcinomas respectively.ConclusionTI computed using an AR method vary as a function of the tissue type and cancer subtype more than the TI involving the usual RR method. AR-based TI might be useful for tumor characterization

Monitoring anti-PD-1-based immunotherapy in non-small cell lung cancer with FDG PET: introduction of iPERCIST

Abstract Background Immunotherapy represents a new therapeutic approach in non-small cell lung carcinoma (NSCLC) with the potential for prolonged benefits. Because of the systemic nature and heterogeneity of tumoral diseases, as well as the immune restoration process induced by immunotherapy, the assessment of therapeutic efficacy is challenging, and the role of FDG PET is not well established. We evaluated the potential of FDG PET to monitor NSCLC patients treated with a checkpoint inhibitor. Results This was a retrospective analysis of 28 NSCLC patients treated with nivolumab, a programmed cell death 1 (PD-1) blocker. All patients underwent a PET scan before treatment (SCAN-1) and another scan 2 months later (SCAN-2). Disease progression was assessed by immune PET Response Criteria in Solid Tumors (iPERCIST), which was adapted from PERCIST; and the immune Response Evaluation Criteria in Solid Tumors (iRECIST). iPERCIST is a dual-time-point evaluation of “unconfirmed progressive metabolic disease” (UPMD) status at SCAN-2. UPMD at SCAN-2 was re-evaluated after 4 weeks with SCAN-3 to confirm PMD. Patients with complete/partial metabolic response (CMR or PMR) or stable metabolic disease (SMD) at SCAN-2 or -3 were considered responders. Patients with UPMD confirmed at SCAN-3 were considered non-responders. The Kaplan-Meier method was used to estimate survival. At SCAN-2, we found 9/28 cases of PMR, 4/28 cases of SMD, 2/28 cases of CMR, and 13/28 cases of UPMD. Four of the 13 UPMD patients were classified as responders at SCAN-3 (PMR n = 1, SMD n = 3). The remaining nine UPMD patients were classified as non-responders due to clinical degradation, and treatment was stopped. The median follow-up was 16.7 months [3.6–32.2]. Responders continued treatment for a mean of 10.7 months [3.8–26.3]. Overall survival was longer for responders than that for non-responders (19.9 vs. 3.6 months, log rank p = 0.0003). The 1-year survival rates were 94% for responders and 11% for non-responders. A comparison with iRECIST showed reclassification in 39% (11/28) of patients with relevant additional prognostic information. Conclusions iPERCIST dual-time-point evaluation might be a powerful tool for evaluating anti-PD-1-based immunotherapy, with the ability to identify patients who can benefit most from treatment. The prognostic value of iPERCIST criteria should be confirmed in large prospective multicentric studies

First-line immune-checkpoint inhibitor plus chemotherapy versus chemotherapy alone for extensive-stage small-cell lung cancer: a meta-analysis

Introduction: Platin-based chemotherapy (CT) has long been the first-line standard-of-care for patients with extensive-stage small-cell lung cancer (ES–SCLC). Adding immune-checkpoint inhibitor(s) to CT (ICI+CT) in this setting is an option of interest, although its benefit is apparently modest. Methods: This meta-analysis was conducted on randomized trials comparing first-line ICI+CT versus CT alone for ES–SCLC. Outcomes included overall survival (OS), progression-free survival (PFS), objective response rate (ORR), response at 12 months and adverse events (AEs). Subgroup analyses were computed according to the immunotherapy used, performance status (PS), age, platinum salt, liver metastases and brain metastases at diagnosis. Results: The literature search identified one randomized phase II (ECOG-ACRIN-5161) and four phase III trials (CASPIAN, IMPOWER-133, KEYNOTE-604 and Reck et al. 2016) that included 2775 patients (66% males, 95% smokers, median age: 64 years, PS = 0 or 1). ICI+CT was significantly associated (hazard ratio [95% confidence interval]) with prolonged OS [0.82 (0.75–0.89); p < 0.00001] and PFS [0.81 (0.75–0.87); p < 0.00001], with OS benefits for anti-PD-L1 [0.73 (0.63–0.85); p < 0.0001] or anti-PD-1 [0.76 (0.63–0.93); p < 0.006] but not for anti-CTLA-4 [0.90 (0.80–1.01), p = 0.07]. ORRs for ICI+CT or CT alone were comparable [odds ratio 1.12 (0.97–1.00); p = 0.12], but responses at 12 months favored ICI+CT [4.16 (2.81–6.17), p < 0.00001]. Serious grade-3/4 AEs were more frequent with ICI+CT [odds ratio 1.18 (1.02–1.37); p = 0.03]. Compared with CT, no ICI+CT benefit was found for ES–SCLC with brain metastases at diagnosis [HR 1.14 (0.87–1.50); p = 0.34]. Conclusions: First-line ICI+CT appears to be superior to CT alone for ES–SCLC except for patients with brain metastases at diagnosis

Examples of segmentation.

<p>Delineation of spheres in the phantom data (A). Segmentation of tumors in a NSCLC patient (B) and relocation of the tumor volume in the liver of the patient.</p

Successful sequential tyrosine kinase inhibitors to overcome a rare compound of EGFR exon 18–18 and EGFR amplification: A case report

Background New mutational detection techniques like next-generation sequencing have resulted in an increased number of cases with uncommon mutation and compound mutations [3%–14% of all epidermal growth factor receptor (EGFR) mutations]. In rare exon 18 mutations (3%–6%), G719X and E709X represent the majority, but CMut associating these exon 18 points mutations are even rarer, making the understanding of the impact of epidermal growth factor receptor tyrosine kinase inhibitors still limited. Three generations of EGFR tyrosine kinase inhibitors (TKIs) are available to target EGFR mutations, but according to the types of mutations, the sensitivity to TKI is different. Afatinib, osimertinib, and neratinib have showed some effectiveness in single exon 18, but no report has precisely described their efficiency and acquired mechanism of resistance in a CMut of exon 18–18 (G719A and E709A). Case presentation We report a case of a 26-year-old woman with bilateral advanced adenocarcinoma of the lung harboring a compound mutation associating G719A and E709A in exon 18, who developed an EGFR amplification as resistance mechanism to osimertinib. She presented a significant clinical and morphological response under sequential TKIs treatment (afatinib, osimertinib, and then neratinib). Conclusion A non-small cell lung cancer (NSCLC) with rare compound mutation exon 18–exon 18 (G719A and E709A) and EGFR amplification can be overcome with adapted sequential second- and third-generation TKIs. This report has potential implications in guiding decisions for the treatment of these rare EGFR mutations

Prognostic implications of volume-based measurements on FDG PET/CT in stage III non-small-cell lung cancer after induction chemotherapy

International audienceWe sought to determine whether metabolic volume-based measurements on FDG PET/CT scans could provide additional information for predicting outcome in patients with stage III non-small-cell lung cancer (NSCLC) treated with induction chemotherapy

AR-based TI as a function of MV and SUVmax.

<p>Plots of homogeneity (A-B), entropy (C-D) and HGZE (E-F) as a function of the number of voxels (A, C, E) or as a function of SUVmax (B, D, F) for the phantom (blue), lung tumors (red) and healthy tissue (green) with the AR20 method for TI calculation.</p

RR-based TI as function of MV and SUVmax.

<p>Plots of homogeneity (A-B), entropy (C-D) and HGZE (E-F) as a function of the number of voxels (A, C, E) or as a function of SUVmax (B, D, F) for the phantom (blue), lung tumors (red) and healthy tissue (green) with the RR method for TI calculation.</p