The responses have been given by Dr. Manjiri Bakre, CEO & Founder, Oncostem Diagnostics
All around the world, people are facing unprecedented challenges and uncertainties as a result of the COVID-19 pandemic. As InnovatioCuris (IC), we are always on a lookout for healthcare innovations that are affordable and provide quality care. In the wake of this need of the hour, InnoHEALTH magazine scouted and interviewed some innovative startups to build an army of health transformers to mobilize and address this global health crisis.
Kritika Arora and Varsha Prasad interviewed Dr Manjiri Bakre, CEO & Founder, Oncostem Diagnostics on behalf of InnoHEALTH magazine.
By interviewing Oncostem Diagnostics, we aim to understand and review the role of AI as a decisive technology to analyze, prepare us for prevention, and fight with COVID-19 (Coronavirus) and other such pandemics.
Reasons behind the company name, any story behind it?
The company focuses on Oncology and was built to develop tests to predict cancer relapse, to help personalized cancer treatment planning. Taking “Onco”, from oncology and “Stem” inspired by cancer stem cells which are known to play a role in cancer relapse. Hence the name OncoStem.
What made you start the company? Tell us briefly about your journey?
OncoStem was founded in 2011, after being moved by the death of a close friend due to breast cancer. She died despite being diagnosed with early-stage disease, maybe due to a limited understanding of the tumour biology driving the disease. Companies in the West were developing tests that could analyze tumour biology and determine the risk of relapse in patients with early-stage breast cancer patients. These tests could help personalize a patient’s treatment plan based on that patient’s individual risk of relapse. Since these tests are very expensive, Dr Manjiri shared that she approached doctors in India and pitched her idea of developing a home-grown and affordable version of such a test. And that’s how the journey started.
How is your company using AI? Tell us about the specializations where it is being used and problems that you are solving?
OncoStem’s flagship product “CanAssist Breast” is a machine learning-based prognostic test that helps personalize treatment for early-stage breast cancer patients who are hormone receptor-positive (HR+) and HER2-negative. The standard treatment for HR+ HER2-negative breast cancer involves both chemotherapy and hormone therapy. The risk of relapse in early-stage (Stage I and II) HR+ HER2-negative breast cancer is very low (10-15%) even if patients are given hormone therapy alone. This implies a majority of patients (~85%) are being overtreated with chemotherapy, which has toxic side effects and lowers the quality of life of patients.
The company uses cutting-edge machine learning-based algorithms that predict the risk of recurrence for every patient. Machine learning is known to be a more advanced tool to develop prognostic tests where patterns of patient information need to be understood and analyzed. It maximizes diagnostic accuracy thereby improving patient outcomes. Machine learning-based methods also have flexible “transfer functions” which allow them to model complex processes such as tumor recurrence.
Explain us a typical day in office, how does an AI expert spend their day? Tell us some under the hood details?
At work typically, Dr Manjiri wear multiple hats in one day depending on a day, it can be a sales meeting first thing in the am, followed by scientific publication review, followed by issue in purchasing a reagent, to trouble shooting a Statistical analysis work or visit a hospital to pitch the product for commercial purposes or propose a study with a hospital, help design a brochure for a conference, meet investors, interview candidates to reading and discussing a legal document with our lawyers
Tell us the challenges you have faced and are facing in development/implementation of AI?
Conducting validation studies in India can be challenging because the clinical trial framework is not as well established as developed countries. Dr Manjiri shared that they must have approached 50-60 hospitals to work on developing and validating CanAssist Breast, which ultimately led to 10 hospitals signing up. There are many reasons for this – 1) one is that the doctor-patient ratio in India is well below the developed world. Each oncologist in India sees many more patients per day leaving them with little time to focus on research studies. 2) Lack of documented patient records and follow-up also makes it difficult to conduct studies that require patient history. Patients often move back to their hometowns after treatment and are lost to follow-up. Availability of data is crucial to AI/ML. A machine learning algorithm can only be as good as the input data. Robust data makes for a robust algorithm, otherwise its garbage in, garbage out 3) Lack of enforcement and encouragement of importance of research studies, clinical trials, detailed documentation from government and regulatory authorities. If they enforce and encourage these activities,says Dr Manjiri, they can develop far more world class diagnostics, drugs and medical devices than they are currently doing.
How can you overcome some of these challenges? Do you want to share any instances where in the past your team was able to overcome any challenges?
Through persistence and perseverance, Dr Manjiri found clinicians who believed in her cause. They were passionate about working on new technologies, who devoted time and effort to participate in our validation studies. Without them, this would not have been possible.Her early investors, Artiman Ventures, were also very helpful in making connections to clinicians in India and the US.
How reliable are these AI tools from a clinical perspective, tell us about the regulatory approvals, which stage are you in currently?
Specifically, in healthcare, ML has led to exciting new developments that could redefine cancer diagnosis and treatment in the years to come. ML can increase access to treatment in developing countries which don’t have enough cancer specialist doctors to deal with the rising incidence of cancer that can treat certain diseases, it can improve the sensitivity of detection, add more value in treatment decisions of cancer, and it can help personalize treatment so that each patient gets the treatment that’s best for them. In many cases, they can even add to workflow efficiency in hospitals. The possibilities are endless!
To share some examples, LYNA (LYmph Node Assistant) by Google detects spread of breast cancer metastasis early and can reduce the burden on Pathologists as well. A deep learning convolutional neural network or CNN – developed by a team from Germany, France, and the US can diagnose skin cancer more accurately than dermatologists. In a recently reported study, the software was able to accurately detect cancer in 95% of images of cancerous moles and benign spots, whereas a team of 58 dermatologists was accurate 87% of the time.
The move from lab to actual practice has happened already for some AI-based solutions such as the FDA-approved imaging tool called IDx-DR for diagnosing diabetic eye disease.
The test CanAssist Breast is CE marked and is performed in a lab which is ISO 13485 certified, NABL, and CAP-accredited.
Share the customer benefit with your solution, and the role AI will play?
The start-up provides oncologists with a well-validated and affordable solution that can be used to improve the quality of life of patients.Their machine learning-based algorithm calculates a risk score that is patient-specific. This score decides if patients can avoid chemotherapy. Chemotherapy has various side-effects, both short term and long term. In cases where its benefit does not outweigh the risks, it can lead to a serious decline in quality of life. The current treatment guidelines require oncologists to give patients optimal treatment based on their individual risk profile. But the tests developed by Western companies are very expensive and out of reach of most Indians. CanAssist Breast gives oncologists an evidence-based and affordable way to decide which patients can be spared the burden of chemotherapy.
CanAssist Breast is based on machine learning that is very well suited to building survival models in cancer because it improves patient outcomes by maximizing accuracy.
Are there any general issues associated with AI products and services?
Dr Manjiri said, technologies like AI indeed have tremendous potential and all stakeholders like the promising algorithms, accurate clinical and relevant in vivo data, clinicians, institutions have to align themselves to reap meaningful benefits from it. One must remember that excellent technical innovations in AI can not fix social/political problems. Also, the data input to AI must be in high volume and of clinically high quality/relevance. Fundamentally flawed data can not substitute for high volume. Currently, most of the AI applications are using the paradigm of ‘deductive reasoning’ and they need to move from there towards ‘inductive reasoning’.
She also shared that they have traveled a fair amount in the AI path to excellence but one must be cautious going further to embrace the brilliant promise it holds. What they need next is to move from theoretic benefit and evangelical sales to established use cases and robust, clinically-relevant data.
What efforts are you making to overcome associated disadvantages ?
The startup has established the accuracy and clinical utility of their test through years of rigorous validation. This has helped prove that their technology works and adds value in the clinician’s workflow. They are also working on additional studies, both locally and internationally, where long term data is available.
What differentiates you from your competitors?
According to the CEO and founder of the startup, their product, CanAssist Breast is the only test to be validated on Indian patients. Competitors have developed and validated in Western countries, on Caucasian patients. Caucasian breast cancer patients tend to be older and post-menopausal at diagnosis, whereas their counterparts in India tend to be younger and premenopausal. They also have other USPs:
- Only product to have data on Indian patients.
- Is affordable as compared to expensive Western tests.
- The turn around time or TAT is claimed to be the shortest so they report within 8-10 days compared to 3 weeks taken by some of the competing tests.
Where do you see your organisation in 10 years? Any brief message for our readers?
OncoStem is working to globally validate its “Made-in-India” test CanAssist Breast, by undertaking studies across multiple continents. Also working to build a pipeline of tests that can personalize treatment for other cancers like ovarian cancer.
Working with pharmaceutical companies to develop targeted drugs based on the learnings from CanAssist Breast, which can help treat those patients who don’t respond to conventional therapy. Drug development is a long process and they hope in the next 10 years they see major activity on this front.
Interviewed by Kritika Arora and Varsha Prasad
