Talat Zehra
Assistant Professor, Jinnah Sindh Medical University, Karachi, Pakistan
Biggest challenge in pathology? The biggest challenge is the declining number of histopathologists while there is an increasing number of cancer cases around the globe. The situation is especially concerning in the developing world. At a time when personalized cancer treatment patients, driven by the use of comprehensive gene profiling, is becoming essential for targeted therapy, diagnosis and prognosis, this shortage of histopathologists is worrisome. Pathologists need to be at the forefront of this molecular revolution and lead this transformation.
Exciting developments and trends? Comprehensive gene profiling, molecular characterization of tumors, digital and computational pathology, and the use of AI are the emerging trends in pathology today. These developments have the potential to solve many of the existing challenges in pathology, revolutionizing diagnosis, treatment and patient care.
Controversial opinion? Pathologists should be at the forefront of not only diagnosing malignant tumors but also play a leading role in cancer prognostication and treatment. Pathologists should be the ones developing disease models, analyzing trends and predicting disease outcomes according to our own demographic regions.
Solving problems… After the COVID 19 pandemic, we all had to learn and adapt to new norms, including the adoption of digital tools in every field of life. The field of pathology did not lag behind. I started exploring new digital and computational pathology tools, but I encountered a bottleneck: the lack of a whole slide scanner in my setup.
I did not lose hope and started working with digital images captured by a camera connected to the microscope. Although the images were of small size, I focused on the area of interest and used them for telepathology and knowledge sharing. I was reasonably happy with this outcome but I still wanted to use computational pathology. As mentioned, the main bottleneck was the absence of whole slide images. Expense was another barrier. However, with the support of a number of organizations and computational pathologists who helped me despite all constraints, I applied deep learning algorithms on static images. We conducted various studies for research purposes, many of which have been published in local as well as international journals. I also explored different open-source tools and learned how pathologists can use digital and computational pathology tools in resource-limited settings.
The journey did not stop there and we are progressing with even better facilities now. What I learned from this experience of my life is to not give up on your efforts. Remain positive, consistent and humble, and success will be yours at the end of the day!
