Distinguishing Benign from Malignant Pancreatic and Periampullary Lesions Using Combined Use of 1H-NMR Spectroscopy and Gas Chromatography–Mass Spectrometry

Previous work demonstrated that serum metabolomics can distinguish pancreatic cancer from benign disease. However, in the clinic, non-pancreatic periampullary cancers are difficult to distinguish from pancreatic cancer. Therefore, to test the clinical utility of this technology, we determined whether any pancreatic and periampullary adenocarcinoma could be distinguished from benign masses and biliary strictures. Sera from 157 patients with malignant and benign pancreatic and periampullary lesions were analyzed using proton nuclear magnetic resonance (1H-NMR) spectroscopy and gas chromatography–mass spectrometry (GC-MS). Multivariate projection modeling using SIMCA-P+ software in training datasets (n = 80) was used to generate the best models to differentiate disease states. Models were validated in test datasets (n = 77). The final 1H-NMR spectroscopy and GC-MS metabolomic profiles consisted of 14 and 18 compounds, with AUROC values of 0.74 (SE 0.06) and 0.62 (SE 0.08), respectively. The combination of 1H-NMR spectroscopy and GC-MS metabolites did not substantially improve this performance (AUROC 0.66, SE 0.08). In patients with adenocarcinoma, glutamate levels were consistently higher, while glutamine and alanine levels were consistently lower. Pancreatic and periampullary adenocarcinomas can be distinguished from benign lesions. To further enhance the discriminatory power of metabolomics in this setting, it will be important to identify the metabolomic changes that characterize each of the subclasses of this heterogeneous group of cancers

Serum metabolomics: development and validation of a new diagnostic test for pancreatic cancer

Bibliography: p. 165-177Includes copy of ethics approval. Original copy with original Partial Copyright Licence.Patent application paperwork with original Partial Copyright Licence

Steps involved in designing and creating the spiked-in data set

Article describes the design and steps involved in creating the spiked-in data set. A subset of the data are included in an Excel file.Ye

Spiked-in Data Set for BMC Notes paper

Dilution series of experimentsN

Timely access and quality of care in colorectal cancer: a population-based cohort study using administrative data

Abstract Background While efforts to improve cancer outcomes have typically focused on improving quality of care, recently, a growing emphasis has been placed on timely access to quality cancer care. This retrospective cohort study examines, at a population level, the relationship between quality and timeliness of colorectal cancer (CRC) care in a single Canadian province (Nova Scotia). Through the provincial cancer registry, we identified all residents diagnosed with invasive CRC between 2001 and 2005 that underwent a non-emergent resection. Using anonymized administrative databases that are individually linked at the patient level, we obtained clinicodemographic, diagnostic, and treatment event data. Selected charts were reviewed to ensure completeness of chemotherapy data. Performance on six quality indicators and the percentage of patients achieving wait-time benchmarks for diagnosis, surgery, and adjuvant therapy were calculated. The relationship between quality indicators and wait-time benchmarks was examined using logistic regression. Results Where an association was identified, patients who received ‘higher quality care’ had longer wait times. Individuals who received a complete preoperative colonoscopy were less likely to meet benchmarks for time from presentation to diagnosis and from diagnosis to surgery. Those who received an appropriate radiation oncology consultation were less likely to meet benchmarks for time from diagnosis to surgery and from surgery to adjuvant therapy. Conclusions As governments and other organizations move forward with strategies to reduce wait times, they must also focus on how to define and monitor quality care, and consider the relationship between these two dimensions of health care. Similarly, when developing quality improvement initiatives, the impact on resource utilization and potential to create longer waits for care must be considered

Intensity-Modulated Radiotherapy (IMRT) vs Helical Tomotherapy (HT) in Concurrent Chemoradiotherapy (CRT) for Patients with Anal Canal Carcinoma (ACC): an analysis of dose distribution and toxicities

Purpose Intensity-modulated radiotherapy (IMRT) and helical tomotherapy (HT) have been adopted for radiotherapy treatment of anal canal carcinoma (ACC) due to better conformality, dose homogeneity and normal-tissue sparing compared to 3D-CRT. To date, only one published study compares dosimetric parameters of IMRT vs HT in ACC, but there are no published data comparing toxicities. Our objectives were to compare dosimetry and toxicities between these modalities. Methods and materials This is a retrospective study of 35 ACC patients treated with radical chemoradiotherapy at two tertiary cancer institutions from 2008–2010. The use of IMRT vs HT was primarily based on center availability. The majority of patients received fluorouracil (5-FU) and 1–2 cycles of mitomycin C (MMC); 2 received 5-FU and cisplatin. Primary tumor and elective nodes were prescribed to ≥54Gy and ≥45Gy, respectively. Patients were grouped into two cohorts: IMRT vs HT. The primary endpoint was a dosimetric comparison between the cohorts; the secondary endpoint was comparison of toxicities. Results 18 patients were treated with IMRT and 17 with HT. Most IMRT patients received 5-FU and 1 MMC cycle, while most HT patients received 2 MMC cycles (p < 0.01), based on center policy. HT achieved more homogenous coverage of the primary tumor (HT homogeneity and uniformity index 0.14 and 1.02 vs 0.29 and 1.06 for IMRT, p = 0.01 and p < 0.01). Elective nodal coverage did not differ. IMRT achieved better bladder, femoral head and peritoneal space sparing (V30 and V40, p ≤ 0.01), and lower mean skin dose (p < 0.01). HT delivered lower bone marrow (V10, p < 0.01) and external genitalia dose (V20 and V30, p < 0.01). Grade 2+ hematological and non-hematological toxicities were similar. Febrile neutropenia and unscheduled treatment breaks did not differ (both p = 0.13), nor did 3-year overall and disease-free survival (p = 0.13, p = 0.68). Conclusions Chemoradiotherapy treatment of ACC using IMRT vs HT results in differences in dose homogenity and normal-tissue sparing, but no significant differences in toxicities.YesSponsored by the University of Calgary Open Access Author’s Fund

Performance of variable selection methods using stability-based selection

Background: Variable selection is frequently carried out during the analysis of many types of high-dimensional data, including those in metabolomics. This study compared the predictive performance of four variable selection methods using stability-based selection, a new secondary selection method that is implemented in the R package BioMark. Two of these methods were evaluated using the more well-known false discovery rate (FDR) as well. Results: Simulation studies varied factors relevant to biological data studies, with results based on the median values of 200 partial area under the receiver operating characteristic curve. There was no single top performing method across all factor settings, but the student t test based on stability selection or with FDR adjustment and the variable importance in projection (VIP) scores from partial least squares regression models obtained using a stability-based approach tended to perform well in most settings. Similar results were found with a real spiked-in metabolomics dataset. Group sample size, group effect size, number of significant variables and correlation structure were the most important factors whereas the percentage of significant variables was the least important. Conclusions: Researchers can improve prediction scores for their study data by choosing VIP scores based on stability variable selection over the other approaches when the number of variables is small to modest and by increasing the number of samples even moderately. When the number of variables is high and there is block correlation amongst the significant variables (i.e., true biomarkers), the FDR-adjusted student t test performed best. The R package BioMark is an easy-to-use open-source program for variable selection that had excellent performance characteristics for the purposes of this study.Medicine, Faculty ofNon UBCSurgery, Department ofReviewedFacult