Influence of timeliness and receipt of first treatment on geographic variation in non-small cell lung cancer mortality

Does Multidisciplinary Team Management Improve Clinical Outcomes in NSCLC? A Systematic Review With Meta-Analysis

Introduction

The implementation of multidisciplinary teams (MDTs) has been found to be effective for improving outcomes in oncology. Nevertheless, there is still a dearth of robust literature on patients with NSCLC. The aim of this study was to conduct a systematic review regarding the impact of MDTs on patient with NSCLC outcomes.

Methods

Databases were systematically searched up to February 2023. Two reviewers independently performed study selection and data extraction. Risk of bias was evaluated using the Newcastle-Ottawa and certainty of evidence by the Grading of Recommendations Assessment, Development and Evaluation approach. Overall survival was the primary outcome. Secondary outcomes included mortality, length of survival, progression-free survival, time from diagnosis to treatment, complete staging, treatment received, and adherence to guidelines. A meta-analysis with a random-effect model was performed. Statistical analysis was performed with the R 3.6.2 package.

Results

A total of 22 studies were included in the systematic review. Ten outcomes were identified, favoring the MDT group over the non-MDT group. Pooled analysis revealed that patients managed by MDTs had better overall survival (three studies; 38,037 participants; hazard ratio 0.60, 95% confidence interval [CI]: 0.49-0.75, I2 = 78%), shorter treatment time compared with patients in the non-MDT group (six studies; 15,235 participants; mean difference = 12.20 d, 95% CI: 10.76-13.63, I2 = 63%), and higher proportion of complete staging (four studies; 14,925 participants; risk ratio = 1.36, 95% CI: 1.17-1.57, I2 = 89%).

Conclusions

This meta-analysis revealed that MDT-based patient care was associated with longer overall survival and better quality-of-care-related outcomes.

Machine Learning Techniques to Predict Timeliness of Care among Lung Cancer Patients

Delays in the assessment, management, and treatment of lung cancer patients may adversely impact prognosis and survival. This study is the first to use machine learning techniques to predict the quality and timeliness of care among lung cancer patients, utilising data from the Victorian Lung Cancer Registry (VLCR) between 2011 and 2022, in Victoria, Australia. Predictor variables included demographic, clinical, hospital, and geographical socio-economic indices. Machine learning methods such as random forests, k-nearest neighbour, neural networks, and support vector machines were implemented and evaluated using 20% out-of-sample cross validations via the area under the curve (AUC). Optimal model parameters were selected based on 10-fold cross validation. There were 11,602 patients included in the analysis. Evaluated quality indicators included, primarily, overall proportion achieving "time from referral date to diagnosis date ≤ 28 days" and proportion achieving "time from diagnosis date to first treatment date (any intent) ≤ 14 days". Results showed that the support vector machine learning methods performed well, followed by nearest neighbour, based on out-of-sample AUCs of 0.89 (in-sample = 0.99) and 0.85 (in-sample = 0.99) for the first indicator, respectively. These models can be implemented in the registry databases to help healthcare workers identify patients who may not meet these indicators prospectively and enable timely interventions.

Development of an Australia and New Zealand Lung Cancer Clinical Quality Registry: a protocol paper

INTRODUCTION

Lung cancer is the leading cause of cancer mortality, comprising the largest national cancer disease burden in Australia and New Zealand. Regional reports identify substantial evidence-practice gaps, unwarranted variation from best practice, and variation in processes and outcomes of care between treating centres. The Australia and New Zealand Lung Cancer Registry (ANZLCR) will be developed as a Clinical Quality Registry to monitor the safety, quality and effectiveness of lung cancer care in Australia and New Zealand.

METHODS AND ANALYSIS

Patient participants will include all adults >18 years of age with a new diagnosis of non-small-cell lung cancer (NSCLC), SCLC, thymoma or mesothelioma. The ANZLCR will register confirmed diagnoses using opt-out consent. Data will address key patient, disease, management processes and outcomes reported as clinical quality indicators. Electronic data collection facilitated by local data collectors and local, state and federal data linkage will enhance completeness and accuracy. Data will be stored and maintained in a secure web-based data platform overseen by registry management. Central governance with binational representation from consumers, patients and carers, governance, administration, health department, health policy bodies, university research and healthcare workers will provide project oversight.

ETHICS AND DISSEMINATION

The ANZLCR has received national ethics approval under the National Mutual Acceptance scheme. Data will be routinely reported to participating sites describing performance against measures of agreed best practice and nationally to stakeholders including federal, state and territory departments of health. Local, regional and (bi)national benchmarks, augmented with online dashboard indicator reporting will enable local targeting of quality improvement efforts.

Clinical impact of delays in the management of lung cancer patients in the last decade: systematic review

INTRODUCTION

Due to the importance of lung cancer early treatment because of its severity and extent worldwide a systematic literature review was conducted about the impact of delays in waiting times on the disease prognosis.

MATERIALS AND METHODS

We conducted a systematic search of observational studies (2010-2020) including adult patients diagnosed with lung cancer and reporting healthcare timelines and their clinical consequences.

RESULTS

We included 38 articles containing data on waiting times and prognosis; only 31 articles linked this forecast to a specific waiting time. We identified 41 healthcare time intervals and found medians of 6-121 days from diagnosis to treatment and 4-19.5 days from primary care to specialist visit: 37.5% of the intervals indicated better prognosis with longer waiting times.

CONCLUSIONS

All articles emphasized that waiting times must be reduced to achieve good management and prognosis of lung cancer. Further prospective studies are needed on the relationship between waiting times and prognosis of lung cancer.

Hospital-based multidisciplinary lung cancer care in Australia: a survey of the landscape in 2021

Introduction

Lung cancer is the leading cause of cancer death in Australia and has the highest cancer burden. Numerous reports describe variations in lung cancer care and outcomes across Australia. There are no data assessing compliance with treatment guidelines and little is known about lung cancer multidisciplinary team (MDT) infrastructure around Australia.

Methods

Clinicians from institutions treating lung cancer were invited to complete an online survey regarding the local infrastructure for lung cancer care and contemporary issues affecting lung cancer.

Results

Responses from 79 separate institutions were obtained representing 72% of all known institutions treating lung cancer in Australia. Most (93.6%) held a regular MDT meeting although recommended core membership was only achieved for 42/73 (57.5%) sites. There was no thoracic surgery representation in 17/73 (23.3%) of MDTs and surgery was less represented in regional and low case volume centres. Specialist nurses were present in just 37/79 (46.8%) of all sites. Access to diagnostic and treatment facilities was limited for some institutions. IT infrastructure was variable and most sites (69%) do not perform regular audits against guidelines. The COVID-19 pandemic has driven most sites to incorporate virtual MDT meetings, with variable impact around the country. Clinician support for a national data-driven approach to improving lung cancer care was unanimous.

Discussion

This survey demonstrates variations in infrastructure support, provision and membership of lung cancer MDTs, in particular thoracic surgery and specialist lung cancer nurses. This heterogeneity may contribute to some of the well-documented variations in lung cancer outcomes in Australia.

Epigenetic imprinting alterations as effective diagnostic biomarkers for early-stage lung cancer and small pulmonary nodules

Background

Early lung cancer detection remains a clinical challenge for standard diagnostic biopsies due to insufficient tumor morphological evidence. As epigenetic alterations precede morphological changes, expression alterations of certain imprinted genes could serve as actionable diagnostic biomarkers for malignant lung lesions.

Results

Using the previously established quantitative chromogenic imprinted gene in situ hybridization (QCIGISH) method, elevated aberrant allelic expression of imprinted genes GNAS, GRB10, SNRPN and HM13 was observed in lung cancers over benign lesions and normal controls, which were pathologically confirmed among histologically stained normal, paracancerous and malignant tissue sections. Based on the differential imprinting signatures, a diagnostic grading model was built on 246 formalin-fixed and paraffin-embedded (FFPE) surgically resected lung tissue specimens, tested against 30 lung cytology and small biopsy specimens, and blindly validated in an independent cohort of 155 patients. The QCIGISH diagnostic model demonstrated 99.1% sensitivity (95% CI 97.5–100.0%) and 92.1% specificity (95% CI 83.5–100.0%) in the blinded validation set. Of particular importance, QCIGISH achieved 97.1% sensitivity (95% CI 91.6–100.0%) for carcinoma in situ to stage IB cancers with 100% sensitivity and 91.7% specificity (95% CI 76.0–100.0%) noted for pulmonary nodules with diameters ≤ 2 cm.

Conclusions

Our findings demonstrated the diagnostic value of epigenetic imprinting alterations as highly accurate translational biomarkers for a more definitive diagnosis of suspicious lung lesions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-021-01203-5.

LncRNA SNHG19 Promotes the Development of Non-Small Cell Lung Cancer via Mediating miR-137/E2F7 Axis

Objective

Non-small cell lung cancer (NSCLC) is a common malignant tumor, which has high incidence and low the 5-year survival rate. Long non-coding RNAs (lncRNAs) play critical roles in carcinoma occurrence and metastasis. Herein, our aim was to investigate the effects of lncRNA SNHG19 in NSCLC progression.

Materials and Methods

Long non-coding RNA Small Nucleolar RNA Host Gene 19 (lncRNA SNHG19) expression level was measured by bioinformatics and qRT-PCR. Edu, Transwell, and scratch assays were performed to explore the role of si-SNHG19 or SNHG19 on NSCLC progression. Luciferase assay was used to verify the relationship between SNHG19/E2F7 and miR-137. The experiment of Xenograft was used for exploring the function of SNHG19 in vivo.

Results

SNHG19 was upregulated in cancer tissues, patients plasma and cell lines of NSCLC. Knockdown of SNHG19 inhibited cell proliferation, migration, and invasion. Luciferase assay confirmed that SNHG19 regulated E2F7 expression via interacting with miR-137. Overexpression of SNHG19 accelerated NSCLC tumor progression via miR-137/E2F7 axis both in vitro and in vivo.

Conclusions

Our results clarified the SNHG19 function for the first time, and SNHG19 promoted the progression of NSCLC, which was mediated by the miR-137/E2F7 axis. This study might provide new understanding and targets for NSCLC diagnosis and treatment.