AI for Computational Pathology in Cancer Drug Development

Artificial Intelligence, Cancer Drug: Artificial Intelligence, Cancer DrugThe Friends of Cancer Research (Friends) held a virtual meeting to announce a new research project aimed at supporting the use of digital pathology in oncology drug development. They released a white paper developed by a working group in advance of the meeting called “Supporting the Application of Computational Pathology in Oncology.” The report states that “digital pathology is an emerging application in oncology drug development and clinical care, which allows for whole-slide image creation for storage, viewing, analyses, and interpretation. Digitized images are used directly by pathologists for biomarker interpretation, cellular annotation, and diagnosis.” It also notes that these images can be used to support the development of computational pathology platforms that use artificial intelligence (AI) and machine learning (ML) to “analyze and measure specific image elements, such as subvisual morphological patterns and phenotypes; identify features; and generate reproducible and structured data.” Thus this technology enhances the identification of relevant biomarkers that lead to the best treatments in clinical trials and clinical practice. AI and ML platforms may establish novel biomarkers, aid in quantifying prognostic and predictive biomarkers, and expedite diagnosis and pathological scoring. The Friends white paper makes three key proposals for supporting responsible digital pathology in drug development: 1) report input and platform characteristics for optimized transparency; 2) establish a risk classification framework to inform evidentiary needs and performance criteria; and 3) establish common reference standards. The paper notes that formal guidance from regulatory bodies and interest groups will be needed as the field evolves. Advances in Technology Digital pathology is a highly active area of research and clinical development, which will be “the next transformative technology to enable better patient selection and clinical outcome,” said keynote speaker J. Carl Barrett, PhD, Adjunct Professor in the Departments of Pathology, Epidemiology, Toxicology, and Genetics and Molecular Biology at the University of North Carolina. He noted the use of digital pathology in cancer drug discovery can improve the understanding of cancer networks and assess tumor heterogeneity. “We need to move from reading to measuring,” Barrett noted. He said the COVID-19 pandemic gave a boost to the field of digital pathology because it fostered more reliance on remote work and telemedicine in health care. The promise of digital pathology is that it goes “beyond what the pathologist can see and beyond what the brain can compute,” Barrett stressed. The surge in computational pathology in cancer drug development is part of an overall trend. Barrett noted the FDA has recognized the growing interest in computational platforms with the release of a discussion paper, “Using Artificial Intelligence & Machine Learning in the Development of Drug & Biological Products” (https://www.fda.gov/media/167973/download). According to FDA statistics, in 2021 more than 100 drug and biologic applications submitted to the agency included AI/ML components. AI and ML offer “great promise in the early space of drug development” to see whether a drug is showing any effects, said Mike Montalto, PhD, Chief Scientific Officer at PathAI. “The data need to be compelling,” he added, noting standards in the field are definitely needed as these computational tools evolve. “We'll need to do the heavy lifting as an industry to see what those standards should be,” he said. There is a need for regulatory guidance from agencies, said Megan Doyle, JD, MPH, Director of Global Regulatory and R&D Policy at Amgen. She said guidance is needed on future fit-for-purpose algorithms, and drug sponsors will be required to demonstrate that digital pathology tools are fit-for-purpose in the trial at hand. “We need to feel the tool is robust and well-validated” to inspire trust, she emphasized. Doyle said reliable computational tools show the most promise for pre-screening and screening of patients, because multiple biomarkers could be evaluated from one slide. Computational pathology in drug development also offers the potential of improving clinical care, said Jochen Lennerz, MD, PhD, Medical Director of the Center for Integrated Diagnostics at Massachusetts General Hospital and Associate Professor of Pathology at Harvard Medical School. He posed the question of whether what is learned in drug development could help physicians in real-world clinical practice. It will be necessary to define a proposed AI tool carefully to be certain it can assess cancer risk, Lennerz said, adding that representative data need to be made available for validation. AI Challenges As with any evolving field, computational pathology presents challenges, said Brandon D. Gallas, PhD, Mathematician and Imaging Physicist at FDA's Center for Devices and Radiological Health. One of the biggest challenges in drug development is inertia, he noted. “Industry likes the path forward to be very clear.” To foster innovation in clinical trials, he said consortia will be needed. Montalto agreed. “Pharma needs to get more comfortable with the technology,” and the work of consortia can help to move the field forward. Gallas said any computational tool should be studied in the hands of the end user because that is where reader variability exists. The Friends white paper notes that standardization is needed to bring harmony to this newly evolving field. “Disparate platforms and methodologies for biomarker assessment may make comparing computational pathology platforms challenging unless harmonization efforts exist.” The paper added, “Currently there is not a simple mechanism for comparing the performance of the multiple available computational pathology platforms assessing the same biomarker.” There are also logistical challenges. The Friends white paper notes that there are regional differences in digital pathology capability. Also, it states, “many laboratories do not have digitization capabilities due to lack of infrastructure, training, adequate funding, or other barriers. Additionally, if digitization capabilities are available, most have only access to one scanner type, which may impact the ability to use various platforms if they are not developed in an agnostic way to the digitization workflow. “ Other challenges pointed out by the FDA include concerns about using algorithms that have a degree of opacity, which could lead to ethical or security concerns and pre-existing biases. Lacking transparency, “algorithmic discrimination” against one category of people could inadvertently occur. The FDA discussion paper poses a number of questions designed to spur dialogue and help the use of AI and ML evolve responsibly and ethically. These include: What are the considerations when using open-source AI software? What practices and documentation are being used to inform and record data source selection and inclusion or exclusion criteria? According to the FDA, media publicity on AI has generated concerns about proceeding carefully and with caution to earn trust. Ultimately, to earn trust computational tools will have to prove themselves with patients and the general public, as well as with the researchers who study them and the health professionals who use them. The FDA discussion paper states that “...communication and engagement with patients and the public regarding considerations for AI/ML in drug development are critical to ensure patient-centered approaches and policies.” Peggy Eastman is a contributing writer.