AstraZeneca, Cancer Coalition Philippines, Philippine Business for Social Progress Join Forces to Combat Lung Cancer

AstraZeneca, Cancer Coalition Philippines, Philippine Business for Social Progress Join Forces to Combat Lung Cancer

Early Detection Program Powered by AI Technology Targets Screening of 500,000 Filipinos in One Year

AstraZeneca Philippines, the Cancer Coalition of the Philippines (CCPH), and the Philippine Business for Social Progress (PBSP) have entered a Memorandum of Agreement (MOA) to amplify “Screen to Beat Lung Cancer,” a program aimed at early detection of lung cancer using artificial intelligence-assisted chest x-ray technology through the screening of at least 500,000 Filipino patients until next year.  

The collaboration aims to eliminate lung cancer as the number one cause of death among cancer types in the Philippines by shaping an end-to-end lung cancer ecosystem solution with a shift towards earlier diagnosis and equitable access to cancer care1.

Lung cancer’s high mortality rate and low survival rate, both mainly determined by the stage at diagnosis, is a challenge to Filipino patients having low access to early screening. In fact, 60% of lung cancer cases are already in the advanced setting, significantly decreasing the five-year survival to 2.9%2,3.

The earlier the disease gets diagnosed, the better the survival rate becomes, with stage III non-small cell lung cancer (NSCLC) having a five-year survival rate ranging from 13% to 36%; while in stage I NSCLC, it can be as high as 68% to 92%.4

“AstraZeneca is committed to creating value for society beyond the impact of our life-changing medicines through early detection, education, treatment, and post-treatment support. As lung cancer remains one of the deadliest cancer types, we are steadfast in providing a better chance at cure and equitable cancer care through our collaboration with PBSP and CCPH,“ said Lotis Ramin, AstraZeneca Philippines country president.

For its part, PBSP has been helping companies and organizations reduce morbidity and mortality, improve nutrition, and achieve universal health coverage among poor families. PBSP will co-lead with CCCPH for the development of relations and coordination with government stakeholders in advocating Screen to Beat Lung Cancer program.

CCPH will provide technical expertise in the development and implementation of the program specifically in patient management and referral, specialty care, and public awareness activities for prevention, early screening, and proper management of lung cancer.

The Screen to Beat Lung Cancer program is particularly relevant in the Philippines, where lung cancer is often misdiagnosed as tuberculosis (TB) due to their similar presentation such as coughing, chest pain, difficulty breathing, and common x-ray findings.5,6

This misdiagnosis can lead to delayed diagnosis and treatment of lung cancer, which can greatly reduce a patient’s chances of survival.7 By incorporating lung cancer screening into PBSP’s existing TB screening initiatives and using Artificial Intelligence (AI)-assisted chest x-ray technology, this program aims to improve the accuracy of diagnosis and save more lives through early detection.

This program will make use of Qure.AI’s qXR, which is an AI-powered diagnostic tool designed to assist in detecting chest x-ray abnormalities including pulmonary TB and lung nodules that could indicate the presence of lung cancer.8 Studies have demonstrated the high sensitivity and specificity of qXR in detecting lung nodules.9,10 The World Health Organization (WHO) has already recommended the use of qXR for preliminary TB screening.11 By pairing the qXR TB solution with lung cancer detection, there is an opportunity for synergy and increased value in diagnosing both diseases early.

Aligned with its global Lung Ambition Alliance goals, AstraZeneca Philippines will provide technical and financial assistance in the development and implementation of Qure.AI’s qXR, while PBSP will provide their existing TB screening modalities, including mobile clinics with digital x-ray machines, to incorporate lung cancer screening. CCCPH will further develop early lung cancer detection and patient awareness and education programs with AstraZeneca Philippines. 

The MOA signing ceremony held last May 3 in Mandaluyong City was attended by representatives from AstraZeneca, PBSP, and CCPH.

References

  1. Globocan 2020
  2. Lu T, Yang X, Huang Y et al. Cancer Mgt Res 2019; 11:943-953;
  3. Lung Center of the Philippines. 2008 https://lcp.gov.ph/images/Scientific_Proceedings/Monograph_on_Lung_Cancer_July14.pdf
  4. Goldstraw P, et al. J Thorac Oncol. 2016;11(1):39-51
  5. Singh VK, Chandra S, Kumar S, Pangtey G, Mohan A, Guleria R. A common medical error: lung cancer misdiagnosed as sputum negative tuberculosis. Asian Pac J Cancer Prev. 2009 Jul-Sep;10(3):335-8. PMID: 19640168.;
  6. Bhatt M, Kant S, Bhaskar R. Pulmonary tuberculosis as differential diagnosis of lung cancer. South Asian J Cancer. 2012 Jul;1(1):36-42. doi: 10.4103/2278-330X.96507. PMID: 24455507; PMCID: PMC3876596.;
  7. American Cancer Society. NSCLC survival rates by stage. https://www.cancer.org/cancer/non-small-cell-lung-cancer/detection-diagnosis-staging/survival-rates.html. Accessed April 2023
  8. Kaviani, P.et al. 2022. Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study. https://www.mdpi.com/2075-4418/12/9/2086
  9. Dhara, A.K., Mukhopadhyay, S. & Dutta, A. (2021). Evaluation of qXR, an artificial intelligence algorithm for chest radiography to diagnose pulmonary nodules. European Journal of Radiology, 136: 109581.
  10. Kundra, P., Jain, N., Bhargava, S.K., et al. (2020). Utility of Artificial Intelligence-Based Diagnostic Tool for Rapid Detection of Pulmonary Tuberculosis on Chest Radiography. Journal of Clinical Tuberculosis and Other Mycobacterial Diseases, 21: 100187.
  11. World Health Organization. (2019). WHO approves the use of AI to fight TB. Retrieved from https://www.who.int/news/item/11-06-2019-who-approves-the-use-of-ai-to-fight-tb



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