Study protocol: fish oil supplement in prevention of oxaliplatin-induced peripheral neuropathy in adjuvant colorectal cancer patients - a randomized controlled trial. (OxaNeuro).

BACKGROUND: Oxaliplatin-induced peripheral neuropathy (OIPN) in general and painful OIPN in particular is a debilitating late effect that severely affects cancer survivors' quality of life and causes premature cessation of potentially lifesaving treatment. No preventive treatments and no effective treatment for chronic OIPN exist despite many attempts. One of several suggested mechanisms includes neuroinflammation as a contributing factor to OIPN. Fish oil containing long-chain n-3 polyunsaturated fatty acids (n-3 LCPUFAs) are precursors to specialized proresolving mediators that mediate the resolution of inflammation. Our primary hypothesis is that a high supplementation of n-3 LCPUFAs will lower the prevalence and severity of OIPN. METHODS: The OxaNeuro project is an investigator-initiated, multicenter, double-blinded, randomized, placebo-controlled clinical study. We will include 120 patients eligible to receive adjuvant oxaliplatin after colorectal cancer surgery. Patients will receive fish oil capsules containing n-3 LCPUFAs or corn oil daily for 8 months. The primary endpoint is the prevalence of OIPN at 8 months defined as relevant symptoms, including one of the following: abnormal nerve conduction screening, abnormal vibration threshold test, abnormal skin biopsy, or abnormal pinprick test. Additional endpoints include the intensity and severity of OIPN-related neuropathic pain, patient-reported OIPN symptoms, quality of life, mental health symptoms, body composition, and cognitive evaluation. Furthermore, we will evaluate inflammatory biomarkers in blood samples and skin biopsies, including the potential OIPN biomarker neurofilament light protein (NfL) which will be measured before each cycle of chemotherapy. DISCUSSION: If readily available fish oil supplementation alleviates OIPN prevalence and severity, it will significantly improve the lives of both cancer survivors and palliative cancer patients receiving oxaliplatin; it will improve their quality of life, optimize chemotherapeutic treatment plans by lowering the need for dose reduction or premature cessation, and potentially increase survival. TRIAL REGISTRATION: ClinicalTrial.gov identifier: NCT05404230 Protocol version: 1.2, April 25th. 2023.

Missingness mechanisms and generalizability of patient reported outcome measures in colorectal cancer survivors - assessing the reasonableness of the "missing completely at random" assumption.

BACKGROUND: Patient-Reported Outcome Measures (PROM) provide important information, however, missing PROM data threaten the interpretability and generalizability of findings by introducing potential bias. This study aims to provide insight into missingness mechanisms and inform future researchers on generalizability and possible methodological solutions to overcome missing PROM data problems during data collection and statistical analyses. METHODS: We identified 10,236 colorectal cancer survivors (CRCs) above 18y, diagnosed between 2014 and 2018 through the Danish Clinical Registries. We invited a random 20% (2,097) to participate in a national survey in May 2023. We distributed reminder e-mails at day 10 and day 20, and compared Initial Responders (response day 0-9), Subsequent Responders (response day 10-28) and Non-responders (no response after 28 days) in demographic and cancer-related characteristics and PROM-scores using linear regression. RESULTS: Of the 2,097 CRCs, 1,188 responded (57%). Of these, 142 (7%) were excluded leaving 1,955 eligible CRCs. 628 (32%) were categorized as initial responders, 418 (21%) as subsequent responders, and 909 (47%) as non-responders. Differences in demographic and cancer-related characteristics between the three groups were minor and PROM-scores only marginally differed between initial and subsequent responders. CONCLUSION: In this study of long-term colorectal cancer survivors, we showed that initial responders, subsequent responders, and non-responders exhibit comparable demographic and cancer-related characteristics. Among respondents, Patient-Reported Outcome Measures were also similar, indicating generalizability. Assuming Patient-Reported Outcome Measures of subsequent responders represent answers by the non-responders (would they be available), it may be reasonable to judge the missingness mechanism as Missing Completely At Random.

Identification of patients' smoking status using an explainable AI approach: a Danish electronic health records case study.

BACKGROUND: Smoking is a critical risk factor responsible for over eight million annual deaths worldwide. It is essential to obtain information on smoking habits to advance research and implement preventive measures such as screening of high-risk individuals. In most countries, including Denmark, smoking habits are not systematically recorded and at best documented within unstructured free-text segments of electronic health records (EHRs). This would require researchers and clinicians to manually navigate through extensive amounts of unstructured data, which is one of the main reasons that smoking habits are rarely integrated into larger studies. Our aim is to develop machine learning models to classify patients' smoking status from their EHRs. METHODS: This study proposes an efficient natural language processing (NLP) pipeline capable of classifying patients' smoking status and providing explanations for the decisions. The proposed NLP pipeline comprises four distinct components, which are; (1) considering preprocessing techniques to address abbreviations, punctuation, and other textual irregularities, (2) four cutting-edge feature extraction techniques, i.e. Embedding, BERT, Word2Vec, and Count Vectorizer, employed to extract the optimal features, (3) utilization of a Stacking-based Ensemble (SE) model and a Convolutional Long Short-Term Memory Neural Network (CNN-LSTM) for the identification of smoking status, and (4) application of a local interpretable model-agnostic explanation to explain the decisions rendered by the detection models. The EHRs of 23,132 patients with suspected lung cancer were collected from the Region of Southern Denmark during the period 1/1/2009-31/12/2018. A medical professional annotated the data into 'Smoker' and 'Non-Smoker' with further classifications as 'Active-Smoker', 'Former-Smoker', and 'Never-Smoker'. Subsequently, the annotated dataset was used for the development of binary and multiclass classification models. An extensive comparison was conducted of the detection performance across various model architectures. RESULTS: The results of experimental validation confirm the consistency among the models. However, for binary classification, BERT method with CNN-LSTM architecture outperformed other models by achieving precision, recall, and F1-scores between 97% and 99% for both Never-Smokers and Active-Smokers. In multiclass classification, the Embedding technique with CNN-LSTM architecture yielded the most favorable results in class-specific evaluations, with equal performance measures of 97% for Never-Smoker and measures in the range of 86 to 89% for Active-Smoker and 91-92% for Never-Smoker. CONCLUSION: Our proposed NLP pipeline achieved a high level of classification performance. In addition, we presented the explanation of the decision made by the best performing detection model. Future work will expand the model's capabilities to analyze longer notes and a broader range of categories to maximize its utility in further research and screening applications.

The prognostic value of tumor budding in a thoroughly characterized stage II colon cancer population in the context of a national screening program.

Tumor budding as a prognostic marker in colorectal cancer has not previously been investigated in a cohort of screened stage II colon cancer patients. We assessed the prognostic significance of tumor budding in a thoroughly characterized stage II colon cancer population comprising surgically resected patients in the Region of Southern Denmark from 2014 to 2016. Tumors were re-staged according to the 8th edition of UICC TNM Classification, undergoing detailed histopathological evaluation and tumor budding assessment following guidelines from the International Tumor Budding Consensus Conference. Prognostic evaluation utilized Kaplan-Meier curves, log-rank tests, and Cox proportional hazard models for time to recurrence (TTR), recurrence-free survival (RFS), and overall survival (OS). Out of 497 patients, 20% were diagnosed through the national colorectal cancer screening program. High-grade tumor budding (Bd3) was found in 19% of tumors and was associated with glandular subtype, perineural invasion, mismatch repair proficient tumors, and tumor recurrence (p < 0.001, p < 0.001, p = 0.045, and p = 0.007 respectively). In multivariable Cox regression, high-grade budding was a significant prognostic factor for TTR compared to low-grade (Bd3 HR 2.617; p = 0.007). An association between tumor budding groups and RFS was observed, and the difference was significant in univariable analysis for high-grade compared to low-grade tumor budding (Bd3 HR 1.461; p = 0.041). No significant differences were observed between tumor budding groups and OS. High-grade tumor budding is a predictor of recurrence in a screened population of patients with stage II colon cancer and should be considered a high-risk factor in a shared decision-making process when stratifying patients to adjuvant chemotherapy.

Methylated Cell-Free Tumor DNA in Sputum as a Tool for Diagnosing Lung Cancer-A Systematic Review and Meta-Analysis.

Lung cancer is the leading cause of cancer-related mortality worldwide. Early diagnosis is pivotal for the prognosis. There is a notable overlap between lung cancer and chronic bronchitis, and the potential use of methylated tumor DNA in sputum as a biomarker for lung cancer detection is appealing. This systematic review and meta-analysis followed the PRISMA 2020 statement. A comprehensive search was conducted in Embase, Medline, Web of Science, and the Cochrane Library, using these search strings: Lung cancer, sputum, and methylated tumor DNA. A total of 15 studies met the eligibility criteria. Studies predominantly utilized a case-control design, with sensitivity ranging from 10 to 93% and specificity from 8 to 100%. A meta-analysis of all genes across studies resulted in a summary sensitivity of 54.3% (95% CI 49.4-59.2%) and specificity of 79.7% (95% CI 75.0-83.7%). Notably, two less explored genes (TAC1, SOX17) demonstrated sensitivity levels surpassing 85%. The study's findings highlight substantial variations in the sensitivity and specificity of methylated tumor DNA in sputum for lung cancer detection. Challenges in reproducibility could stem from differences in tumor site, sample acquisition, extraction methods, and methylation measurement techniques. This meta-analysis provides a foundation for prioritizing high-performing genes, calling for a standardization and refinement of methodologies before potential application in clinical trials.

A Comparison of Cellular Immune Response and Immunological Biomarkers in Laparoscopic Surgery for Colorectal Cancer and Benign Disorders.

BACKGROUND: Surgical trauma causes immune impairment, but it is largely unknown whether surgery for cancer and benign diseases instigate comparable levels of immune inhibition. Here, we compared the impact of laparoscopic surgery on immunological biomarkers in patients with colorectal cancer (CRC) and ventral hernia (VH). METHODS: Natural Killer cell activity (NKA), leukocyte subsets, and soluble programmed death ligand 1 (sPD-L1) were measured in blood samples collected from CRC (n = 29) and VH (n = 9) patients preoperatively (PREOP) and on postoperative day (POD) 1, 3-6, 2 weeks and 3 months. NKA was evaluated by the NK Vue assay that uses the level of IFNγ as a surrogate marker of NKA. Normal NKA was defined as IFNγ > 250 pg/mL and low NKA was defined as IFNγ < 250 pg/mL. RESULTS: The CRC cohort was classified into either PREOPLOW having preoperative low NKA or PREOPHIGH having preoperative normal NKA. The median NKA of the PREOPLOW subset was only in the normal range in the POD3 months sample, whereas median NKA of the PREOPHIGH subset and the VH cohort were only low in the POD1 sample. While PREOPLOW differed from VH in the PREOP-, POD1-, and POD3-6 samples (P =.0006, P = .0181, and P = .0021), NKA in PREOPHIGH and VH differed in the POD1 samples (P = .0226). There were no apparent differences in the distribution of leukocyte subsets in the perioperative period between the cohorts. CONCLUSION: CRC patients with preoperative normal NKA and VH patients showed the same pattern of recovery in NKA, while the CRC subset with preoperative low NKA seemed to experience prolonged NK cell impairment. As low NKA is associated with recurrence, preoperative level of NKA may identify patients who will benefit from immune-enhancing therapy in the perioperative period.

Soluble programmed death ligand 1 as prognostic biomarker in non-small cell lung cancer patients receiving nivolumab, pembrolizumab or atezolizumab therapy.

Many studies have focused on the prognostic role of soluble programmed death ligand 1 (sPD-L1) in non-small cell lung cancer (NSCLC), but outcomes are ambiguous and further investigations are needed. We addressed the matter by studying sPD-L1 in baseline samples and in longitudinal samples taken prior to three subsequent cycles of anti-PD-1/anti-PD-L1 treatments. Eighty patients with NSCLC were enrolled. Median sPD-L1 level at baseline was 52 pg/mL [95% confidence interval (CI) 49-57]. In patients treated with pembrolizumab and nivolumab, the concentration of sPD-L1 remained rather stable throughout treatment. In contrast, sPD-L1 rose by 50-fold following the first cycle of atezolizumab therapy. We found the baseline level of sPD-L1 to be related to overall survival (OS) after two years of follow-up in simple Cox analysis (p = 0.006) and multiple Cox Regression, hazard ratio 1.02 (95% CI 1.00-1.03) (p = 0.033). There was no association between sPD-L1 and tissue PD-L1 expression, overall response rate, or progression free survival. In conclusion, sPD-L1 measured in baseline serum samples may be associated with OS in NSCLC patients receiving anti-PD1/anti-PD-L1 treatment. Importantly, the results signify that further research is warranted to explore the clinical utility of sPD-L1 in patients treated with anti-PD-L1.

Early integration of basic palliative care in cancer: scoping review of cross-sectorial models - components, facilitators, barriers.

BACKGROUND: Shared care between oncology specialists and general practice regarding the delivery of palliative care (PC) is necessary to meet the demands for a cohesive PC. The primary objective of this study is to investigate models of cross-sectorial integration between primary care and oncology specialists that have been developed to promote early and basic PC and factors influencing the process. METHOD: A scoping review was conducted using publications dated up until April 2023. Searches were conducted in MEDLINE, CINAHL, Embase, Web of Science and ProQuest Dissertations and Theses. Complementary searches were performed via reference lists and grey literature. Explicit early PC models aimed at patients with cancer aged ≥18 years with healthcare professionals from primary care and oncology constituted the inclusion criteria. The screening of the papers was performed independently by two reviewers. The reporting adheres to the extension for scoping reviews of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. RESULTS: The search provided 5630 articles of which six met the eligibility criteria, each describing a different model of early and cross-sectorial, integrated PC. 12 active components were identified. Education of staff as well as good communication and cooperation skills are essential factors to succeed with integrated, early PC. CONCLUSION: Integration of PC between general practice and oncology specialists has potential. The components of basic PC have been established. Factors known to influence the process are trust, communication and a common goal. Further research is required into strategies for approaching different levels of integration.

Lung cancer among outpatients with COPD: a 7-year cohort study.

Introduction: Lung cancer (LC) is the most common cause of cancer-related deaths worldwide, and its prognosis upon metastasis remains poor. Patients with COPD face a significantly elevated LC risk, up to six times greater than those with normal lung function. We aimed to investigate LC prevalence and stage distribution among COPD outpatients. Furthermore, we aimed to outline the COPD-related variables associated with referral for LC examination. Methods: We conducted a retrospective analysis encompassing the period from 1 January 2012 to 31 December 2018 on all outpatients with COPD and LC and individuals referred for LC examinations. Results: Among all COPD outpatients, 2231 patients (18%) were referred for LC examinations and 565 (4.6%) were diagnosed with LC. LC patients with COPD were more likely to be stage I-II, in contrast to the non-COPD LC population (46% versus 26%, p<0.001 for all). Patients referred for LC examinations exhibited higher use of COPD-related medications, reported more severe dyspnoea (69% versus 66% with Medical Research Council dyspnoea score >2) and experienced a greater frequency of exacerbations (30% versus 24% with two or more exacerbations). Conclusion: Our study revealed a notably high LC incidence among COPD outpatients. LC patients with COPD were diagnosed at earlier stages, and outpatients with more pronounced COPD symptoms were more inclined to undergo LC diagnostics. The overrepresentation of LC cases among COPD outpatients emphasises the importance of tailoring specific screening initiatives for this demographic.

A collection of multiregistry data on patients at high risk of lung cancer-a Danish retrospective cohort study of nearly 40,000 patients.

Background: Lung cancer (LC) is the leading cause of cancer related deaths, and several countries are implementing screening programs. Risk models have been introduced to refine the LC screening criteria, but the use of real-world data for this task demands a robust data infrastructure and quality. In this retrospective cohort study, we aim to address the different relevant risk factors in terms of data sources, descriptive statistics, completeness and quality. Methods: Data on comorbidity, prescription medication, smoking history, consultations, symptoms, familial predispositions, exposures, laboratory data among others were collected for all patients examined on a risk of LC over a 10-year period in the Region of Southern Denmark. Data were delivered from the regional data warehouse as well as the Danish Lung Cancer Registry. Associations between LC and non-LC groups were examined through Chi-squared test (categorical variables) and Wilcoxon signed-rank test (continuous variables that were non-parametric). These associations were investigated on both the original datasets and the subset of patients with complete data. Results: The number of examined individuals increased over the study period and more patients were diagnosed with LC in stage I-II, from 18% in 2009 to 31% in 2018. LC patients were more likely to be older, smoker, with a registered prescription of the included medication. They also exhibited differences in laboratory analysis indicating inflammation and hyponatremia. Weight loss, fatigue and pain were more prevalent in the LC group, while hemoptysis and fever were more common among the non-LC patients. Advanced-stage LC patients experienced a higher rate of symptoms compared to those in the low stages. Within the sub-cohort with complete dataset results, most observed trends persisted, although data on comorbidities were susceptibility to change. Conclusions: This study provides key insights into LC risk assessment using a robust dataset of patients examined for suspected LC. A consistent positive trend in early-stage LC diagnosis was observed throughout the study period. LC patients exhibited distinct smoking behaviors, medication patterns, variations in lab results, and specific symptoms. These discoveries have the potential to enhance discrimination in machine learning-based prediction models, particularly those capable of handling complex distributions. Serving as a detailed account of real-world data collection and processing, the study establishes a foundation for future development of prediction models aimed at facilitating the early referral of LC patients.