The Lancet regional health. Southeast Asia最新文献

This paper outlines the process undertaken by Asian National Cancer Centers Alliance (ANCCA) members in working towards an Asian Code Against Cancer (ACAC). The process involves: (i) identification of the criteria for selecting the existing set of national recommendations for ACAC (ii) compilation of existing national codes or recommendations on cancer prevention (iii) reviewing the scientific evidence on cancer risk factors in Asia and (iv) establishment of one or more ACAC under the World Code Against Cancer Framework. A matrix of national codes or key recommendations against cancer in ANCCA member countries is presented. These include taking actions to prevent or control tobacco consumption, obesity, unhealthy diet, physical inactivity, alcohol consumption, exposure to occupational and environmental toxins; and to promote breastfeeding, vaccination against infectious agents and cancer screening. ANCCA will continue to serve as a supportive platform for collaboration, development, and advocacy of an ACAC jointly with the International Agency for Research on Cancer/World Health Organization (IARC/WHO).

Background

Cancer survival data from Population Based Cancer Registries (PBCR) reflect the average outcome of patients in the population, which is critical for cancer control efforts. Despite decreasing incidence rates, cervical cancer is the second most common female cancer in India, accounting for 10% of all female cancers. The objective of the study is to estimate the five-year survival of patients with cervical cancer diagnosed between 2012 and 2015 from the PBCRs in India.

Methods

A single primary incidence of cervical cancer cases of 11 PBCRs (2012–2015) was followed till June 30, 2021 (n = 5591). Active follow-ups were conducted through hospital visits, telephone calls, home or field visits, and public databases. Five-year Observed Survival (OS) and Age Standardised Relative Survival (ASRS) was calculated. OS was measured by age and clinical extent of disease for cervical cancers.

Findings

The five-year ASRS (95% CI) of cervical cancer was 51.7% (50.2%–53.3%). Ahmedabad urban (61.5%; 57.4%–65.4%) had a higher survival followed by Thiruvananthapuram (58.8%; 53.1%–64.3%) and Kollam (56.1%; 50.7%–61.3%). Tripura had the lowest overall survival rate (31.6%; 27.2%–36.1%). The five-year OS% for pooled PBCRs was 65.9%, 53.5%, and 18.0% for localised, regional, and distant metastasis, respectively.

Interpretation

We observed a wide variation in cervical cancer survival within India. The findings of this study would help the policymakers to identify and address inequities in the health system. We re-emphasise the importance of awareness, early detection, and increase the improvement of the health care system.

Funding

The National Cancer Registry Programme is funded through intra-mural funding by Indian Council of Medical Research, Department of Health Research, India, Ministry of Health & Family Welfare.

Background

Gallbladder cancer (GBC) is highly aggressive. Diagnosis of GBC is challenging as benign gallbladder lesions can have similar imaging features. We aim to develop and validate a deep learning (DL) model for the automatic detection of GBC at abdominal ultrasound (US) and compare its diagnostic performance with that of radiologists.

Methods

In this prospective study, a multiscale, second-order pooling-based DL classifier model was trained (training and validation cohorts) using the US data of patients with gallbladder lesions acquired between August 2019 and June 2021 at the Postgraduate Institute of Medical Education and research, a tertiary care hospital in North India. The performance of the DL model to detect GBC was evaluated in a temporally independent test cohort (July 2021–September 2022) and was compared with that of two radiologists.

Findings

The study included 233 patients in the training set (mean age, 48 ± (2SD) 23 years; 142 women), 59 patients in the validation set (mean age, 51.4 ± 19.2 years; 38 women), and 273 patients in the test set (mean age, 50.4 ± 22.1 years; 177 women). In the test set, the DL model had sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of 92.3% (95% CI, 88.1–95.6), 74.4% (95% CI, 65.3–79.9), and 0.887 (95% CI, 0.844–0.930), respectively for detecting GBC which was comparable to both the radiologists. The DL-based approach showed high sensitivity (89.8–93%) and AUC (0.810–0.890) for detecting GBC in the presence of stones, contracted gallbladders, lesion size <10 mm, and neck lesions, which was comparable to both the radiologists (p = 0.052–0.738 for sensitivity and p = 0.061–0.745 for AUC). The sensitivity for DL-based detection of mural thickening type of GBC was significantly greater than one of the radiologists (87.8% vs. 72.8%, p = 0.012), despite a reduced specificity.

Interpretation

The DL-based approach demonstrated diagnostic performance comparable to experienced radiologists in detecting GBC using US. However, multicentre studies are warranted to explore the potential of DL-based diagnosis of GBC fully.

Funding

None.

Background

Leptomeningeal carcinomatosis (LMC), the metastatic spread of cancer to the leptomeninges, is a rare complication and has a dismal prognosis. Due to limited data available on LMC from India, we conducted a country-wise audit of LMC across 15 centres in India.

Methods

The current study conducted in 2020, was a retrospective, multicentric audit of adult patients (aged ≥18 years) with diagnosis of LMC and who received treatment during 2010–2020. Baseline characteristics, details related to previous treatments, cancer sites, LMC diagnosis, treatment pattern and overall survival (OS) were collected. Descriptive statistics were performed, and Kaplan Meier analysis was performed for the estimation of OS.

Findings

Among the patients diagnosed with LMC (n = 84), diagnosis was confirmed in 52 patients (61.9%) and ‘probable’ in 32 (38.1%) patients. The three most common cause of malignancy were non-small cell lung cancer (NSCLC), breast cancer and gastrointestinal cancer with 45 (53.6%), 22 (26.1%) and 9 (10.7%) patients respectively. Intrathecal therapy was offered in 33 patients (39.3%). The most common intrathecal agent was methotrexate in 23 patients (27.4%). The median OS was 90 days (95% CI 48–128). Among tested variables, intrathecal therapy administration (hazard ratio [HR] = 0.36, 95% CI 0.19–0.68) and primary in lung (HR = 0.43, 95% CI 0.23–0.83) had a favourable impact on OS.

Interpretation

Prognosis with leptomeningeal carcinomatosis is poor with a significant burden of morbidity and mortality in India. This data aims to highlight the current outcomes and facilitate further research on LMC.

Funding

None.

Community Engagement (CE) for disease control and health has been tested for a long time across the globe for various health programmes. Realizing the need for true multisectoral action and CE and ownership for ending TB on an accelerated timeline, the Government of India launched a nationwide campaign for ‘TB Mukt Panchayat’ (meaning ‘TB free village council’ in Hindi language) on 24 March 2023, banking on the system of local self-governments in the country. Though it is an initiative with huge potential to contribute to India’s efforts to end the TB epidemic, it is not without a few shortcomings. We critically analyse the TB Mukt Panchayat initiative and suggest a few recommendations for the way forward.

Background

The prognosis of lung carcinoma has changed since the discovery of molecular targets and their specific drugs. Somatic Epidermal Growth Factor Receptor (EGFR) mutations have been reported in lung carcinoma, and these mutant proteins act as substrates for targeted therapies. However, in a resource-constrained country like India, panel-based next-generation sequencing cannot be made available to the population at large. Additional challenges such as adequacy of tissue in case of lung core biopsies and locating suitable tumour tissues as a result of innate intratumoral heterogeneity indicate the necessity of an AI-based end-to-end pipeline capable of automatically detecting and learning more effective lung nodule features from CT images and predicting the probability of the EGFR-mutant. This will help the oncologists and patients in resource-limited settings to achieve near-optimal care and appropriate therapy.

Methods

The EGFR gene sequencing and CT imaging data of 2277 patients with lung carcinoma were included from three cohorts in India and a White population cohort collected from TCIA. Another cohort LIDC-IDRI was used to train the AIPS-Nodule (AIPS-N) model for automatic detection and characterisation of lung nodules. We explored the value of combining the results of the AIPS-N with the clinical factors in the AIPS-Mutation (AIPS-M) model for predicting EGFR genotype, and it was evaluated by area under the curve (AUC).

Findings

AIPS-N achieved an average AP50 of 70.19% in detecting the location of nodules within the lung region of interest during validation and predicted the score of five lung nodule properties. The AIPS-M machine learning (ML) and deep learning (DL) models achieved AUCs ranging from 0.587 to 0.910.

Interpretation

The AIPS suggests that CT imaging combined with a fully automated lung-nodule analysis AI system can predict EGFR genotype and identify patients with an EGFR mutation in a cost-effective and non-invasive manner.

Funding

This work was supported by a grant provided by Conquer Cancer Foundation of ASCO [2021IIG-5555960128] and Pfizer Products India Pvt. Ltd.