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, featured in Nature and spearheaded by Professors Logan Walsh and Daniela Quail of the Rosalind and Morris Goodman Cancer Institute (GCI), in partnership with Dr. Philippe Joubert from Universit茅 Laval, is among the top contenders for this important recognition.
The study, with the potential to significantly enhance treatment outcomes for lung cancer patients, utilizes artificial intelligence (AI) to predict the necessity of additional treatment after surgery, preventing cancer recurrence. This achievement was acknowledged by a panel of expert scientists who selected it as one of the top 10 discoveries of the year. The general public is encouraged to participate in the voting process on the .
Reflecting on the nomination, Mark Sorin, MD-Ph.D. candidate and one of three first co-authors of the publication, stated, "We are very proud of our selection by Qu茅bec Science. This nomination is an attestation to the significant potential of our research aimed at improving the survival of patients with lung cancer.鈥�
To address the challenge of cancer recurrence, the researchers investigated whether the microscopic make up of tumors could provide more insight into which tumors will return even after surgical removal. They used a novel technology called imaging mass cytometry to capture pictures of the different types and positionings of cancer and immune cells in lung cancer samples. To analyze this data, the researchers turned to artificial intelligence, which can quickly interpret images that would otherwise take years for a human to manually analyze. After learning from analyzing some of the tumor images, the AI was shown the remaining images and was able to predict which cancers came back after surgery with 95.6% accuracy.
Professor Logan Walsh shared what the success of this technology means for future research and clinical practices: 鈥淚n our study, we demonstrated the proof of principle that artificial intelligence can accurately identify distinct patterns within a tumor's architecture, providing valuable insights into the underlying biology influencing cancer progression and therapeutic responses. In addition, our AI based model exhibits promise as a potential biomarker, offering valuable information to guide clinical decision-making.鈥�
Even after surgical removal of a tumor, cancer recurrence remains a significant concern in many cases. While additional treatment can reduce the likelihood of recurrence, the toxic side effects of various cancer medications, such as chemotherapy, emphasize the importance of accurately determining which patients will benefit from further care. This study addresses this challenge by employing new imaging technologies to precisely predict which cancers are likely to return. This enables doctors to promptly administer the right treatment to the appropriate patients.
Reflecting on the impact of their discovery on patients and the healthcare system, Dr. Philippe Joubert remarked, "Our discovery aligns well with the new era of personalized medicine in pulmonary oncology, which focuses on adapting patient management according to individual tumor characteristics and improving the efficient allocation of healthcare resources, ensuring accessibility for patients in genuine need. This finding also represents a significant step in incorporating artificial intelligence into the potential development of companion tests."