Unlocking the Full Impact of Breakthrough Therapies in Breast Cancer Through Earlier Detection
By Léon Van Wouwe, Clinical Innovation Director, Volv Global New data from Roche’s Itovebi (inavolisib) combined with Pfizer’s Ibrance (palbociclib) and Faslodex (fulvestrant) has delivered a major milestone in breast cancer care, showing a remarkable 33% reduction in mortality for patients with HR-positive, HER2-negative, PIK3CA-mutated advanced breast cancer. This breakthrough offers significant hope in managing one of the most common and challenging breast cancer subtypes, especially benefiting patients who have progressed after hormone therapy. Yet, as we acknowledge this clinical achievement, a critical and pressing question arises: How many more lives could we significantly improve, or even save, if these patients were diagnosed earlier? The Challenge: Too Many Diagnoses Still Come Too Late While targeted therapies are revolutionising cancer care, their full therapeutic potential often remains unrealised due to the persistent and complex issue of late diagnosis. Particularly in HR+/HER2- breast cancer, the diagnostic journey is often protracted and challenging. Symptoms can be subtle and misleading, resistance to treatment evolves gradually and silently, and crucial genomic testing access may be limited by systemic barriers. For patients with PIK3CA mutations, every delay translates directly into worsened outcomes, significantly limiting their therapeutic options. These diagnostic delays result in considerable missed opportunities – not only for patients whose lives depend on timely intervention but also for pharmaceutical innovations whose efficacy diminishes as disease stages advance. The Opportunity: Detecting Risk Signals Earlier Using EMR and AI Emerging advancements in AI and sophisticated data analytics now offer a promising pathway to closing the critical gap in early breast cancer diagnosis. By harnessing patterns within electronic medical records (EMRs), it is possible to identify early, subtle risk indicators well before a formal breast cancer diagnosis. These early indicators can manifest as seemingly benign, recurrent symptoms, medication prescription patterns, comorbid conditions, or specific referral behaviours. When accurately captured and analysed, these subtle signals facilitate the predictive identification of at-risk patients, potentially years ahead of current clinical diagnosis timelines. Similar methodologies have already proven successful in rare, hard-to-diagnose cancers like Gastroenteropancreatic Neuroendocrine Tumours (GEP-NETs), where diagnostic delays have been significantly reduced by up to seven years through predictive analytics. Extending these methodologies to more prevalent cancers, such as HR+/HER2-/PIK3CA-mutated breast cancer, presents an immense opportunity to substantially extend therapeutic windows and improve patient prognoses. The Shift: From Precision Medicine to Precision Diagnosis Modern oncology has seen extraordinary advancements in precision therapies, but these advancements must be complemented by equivalent progress in precision diagnosis. The shift must start significantly earlier, ideally within primary care settings, as waiting until oncology referral often proves too late. With robust AI-driven healthcare infrastructure, we can empower clinicians, researchers, and pharmaceutical companies to: Identify patient segments at risk much earlier, enabling healthcare providers to initiate preventive and proactive care strategies at a point when interventions can be most impactful, thereby reducing the long-term burden on healthcare systems and improving individual patient outcomes. Enhance patient selection precision for clinical trials, ensuring that trials are better designed, more efficiently executed, and specifically targeted to patient populations most likely to benefit, thereby improving the speed and effectiveness of new treatment validations. Improve access and adoption of groundbreaking therapies, significantly expanding their clinical impact by accurately targeting therapy to patients who will derive the greatest benefit, which increases therapy effectiveness, reduces wastage, and maximises healthcare resources. Enable clinicians to navigate and address the most critical unmet needs in cancer care today by providing comprehensive, data-driven insights that facilitate timely diagnosis and personalised treatment decisions, ultimately closing critical gaps in patient care and improving survival rates. What’s Next? The oncology pipeline is brimming with innovative therapies, and the clinical and pharmaceutical sectors have never been more prepared to harness these transformative possibilities. However, maximising patient outcomes requires a concerted parallel investment: in early detection mechanisms, actionable data intelligence, and strategic collaborations. Leading pharmaceutical companies, healthcare providers, and technology innovators must collaborate to leverage AI-driven predictive analytics, transforming early detection from aspiration to operational reality. By accelerating this integration, we ensure more patients benefit fully from therapeutic breakthroughs like Itovebi and Ibrance. At Volv Global, we’re already working with forward-thinking teams to make this vision a reality — helping to identify the patients who need access to personalised therapies, earlier and more precisely than ever before. Let us shape a future where oncology care is defined not merely by the potency of treatments available but by the precision with which we ensure they reach patients exactly when they can make the greatest difference. About the author Léon van Wouwe has 20+ years’ global experience in clinical development and operations, uniting data science with pharma and research. He drives cross-functional collaboration to advance innovative treatments.
Progressive Pulmonary Fibrosis: Solving the Puzzle of Poor Disease Control
By Léon Van Wouwe, Clinical Innovation Director, Volv Global Introduction Progressive Pulmonary Fibrosis (PPF) is a severe clinical condition emerging from various fibrosing interstitial lung diseases (ILDs). PPF is characterised by ongoing fibrosis and relentless functional decline despite treatment efforts. Although increasingly recognised, patients continue to encounter significant challenges, including delayed diagnoses, limited therapeutic interventions, and inadequate monitoring. By enhancing patient identification and refining targeted interventions, we can significantly shift outcomes and improve lives. 1. Diagnostic Challenges: An Elusive Phenotype Diagnosing PPF remains complex. The condition can originate from multiple ILD subtypes, including idiopathic pulmonary fibrosis (IPF), hypersensitivity pneumonitis, and autoimmune-related lung diseases. Also, overlapping clinical presentations often mask the specific diagnosis, delaying referrals to specialist respiratory care. Patients frequently experience prolonged cycles within primary and general pulmonary care, with breathlessness worsening progressively before ILD suspicion is raised. Additionally, inconsistent access to multidisciplinary teams (MDTs) and variability in adherence to guideline-driven evaluations further extend diagnostic uncertainty, during which time fibrotic processes continue to progress. 2. Treatment Challenges: When Disease Control Falls Short Even following a correct diagnosis, optimal disease management for PPF often remains difficult to achieve. Immunomodulatory therapies frequently fall short of effectively halting fibrosis. Antifibrotic agents, despite availability in certain healthcare systems, are frequently underutilised due to complex regulatory environments, prohibitive costs, clinical uncertainty, or conservative clinical decision-making. Treatment inertia, driven by hesitation to escalate interventions despite documented disease progression, presents an additional barrier. Poorly defined follow-up protocols and inconsistent lung function monitoring further add to these challenges, leading to missed opportunities for timely therapeutic adjustments. 3. Mapping Challenges to Solutions Challenge Impact Example Solutions Diagnostic Delay Missed early intervention Education on ILD indicators, streamlined referral algorithms Overlap with ILDs Diagnostic ambiguity Wider MDT access, AI-powered pattern recognition tools Treatment Inertia Disease progression despite therapy Explicit escalation guidelines, antifibrotic initiation frameworks Poor Monitoring Unnoticed disease progression Automated lung function trend alerts, digital symptom diaries Case-Finding Deficits Hospitalisation-based detection AI-driven risk scoring and patient identification 4. Solutions: Precision Tools to Drive Better Care Advances in data science, particularly AI-driven healthcare intelligence, present groundbreaking opportunities, for both pharmaceutical companies and healthcare providers alike, helping them address strategic gaps and patient-care challenges in PPF. Specifically: Phenotypic Clustering and Trajectory Modelling: Enables early identification by doctors, of patients at high risk of progression by analysing complex real-world data to reveal subtle patterns indicative of disease pathways. Pharma companies can leverage these insights to optimise trial recruitment and personalise treatment plans, directly addressing gaps in trial precision and patient targeting. High-Risk Case Flagging: Accelerates referrals and intervention through AI-enhanced alert systems for healthcare practitioners, ensuring patients most likely to benefit from timely, targeted interventions are identified earlier, thereby maximising therapeutic effectiveness and resource allocation efficiency. Real-time Disease Activity Monitoring: Utilises advanced digital tools for continuous tracking of disease progression, alerting clinicians promptly to suboptimal disease control. This approach supports timely escalation and proactive disease management, addressing treatment inertia by offering clear, actionable data to healthcare providers. Real-World Evidence Generation: Facilitates comprehensive retrospective and prospective analyses of patient outcomes and treatment efficacies. This not only supports regulatory submissions and reimbursement decisions but also provides robust evidence for therapeutic efficacy and economic value, bridging critical gaps in clinical and economic understanding. By moving from reactive to predictive care, such tools can help transform outcomes for patients often lost in the complexity of ILD management. Conclusion: A New Collaborative Frontier Addressing the complexities of PPF demands a shift from reactive to proactive care models, supported by advanced predictive and analytical capabilities. For healthcare providers, adopting AI-driven insights into clinical practice means recognising subtle indicators of progression early and confidently escalating care. Pharmaceutical companies must also engage deeply with these precision tools, supporting their development and integration to ensure timely patient access to targeted therapies and personalised care plans. By fostering collaboration and integrating advanced AI tools into clinical pathways, we can substantially alter the trajectory for PPF patients, creating a new standard of care that prioritises early diagnosis, targeted intervention, and improved quality of life. About the author Léon van Wouwe has 20+ years’ global experience in clinical development and operations, uniting data science with pharma and research. He drives cross-functional collaboration to advance innovative treatments.
Unlocking the Power of EMR Data for Earlier Gastric Cancer Diagnosis and Risk Prediction
By Léon Van Wouwe, Clinical Innovation Director, Volv Global Gastric (stomach) cancer remains a formidable global health challenge, ranking as the fifth most common cancer and a leading cause of cancer-related mortality. [1] Alarmingly, its incidence is rising in patients under 50 years old, alongside other gastrointestinal malignancies. In 2022 alone, nearly one million new cases were diagnosed, leading to approximately 660,000 deaths worldwide. One particularly aggressive form, gastroesophageal junction (GEJ) cancer, spans the critical connection between the esophagus and the stomach, further complicating detection and treatment. As with all cancers, early diagnosis is critical for optimizing treatment outcomes. Yet, gastric cancer often remains undetected until later stages, limiting therapeutic options and survival rates. Could primary care electronic medical records (EMR) data hold the key to shifting this paradigm? The Power of EMR Data in Earlier Cancer Detection Our recent research on gastroenteropancreatic neuroendocrine tumours (GEP-NETs), a rare type of gut cancer, demonstrates how EMR-driven analytics can uncover hidden diagnostic delays. Leveraging UK primary care data, we identified that undiagnosed patients are, on average, 5 to 7 years younger than those already diagnosed—highlighting a significant opportunity to intervene earlier. [2] Addressing Gastric Cancer Recurrence and Outcome Prediction For gastric cancer, the stakes are even higher. Despite curative surgery and neoadjuvant/adjuvant chemotherapy, recurrence is common. One in four patients experiences disease recurrence within a year post-surgery, and survival beyond two years remains a challenge. The five-year survival rate remains dismally low, with fewer than half of patients alive at this milestone. [3, 4, 5] Beyond early detection, advanced risk prediction models leveraging EMR data could refine patient stratification and enhance personalized treatment decisions. By integrating EMR-driven insights, we can better predict recurrence risk and tailor therapeutic regimens accordingly. Notably, Imfinzi-based regimens have already demonstrated statistically significant and clinically meaningful improvements in event-free survival for resectable early-stage gastric and GEJ cancers, underscoring the potential of precision medicine approaches. [6] A Call to Action: Innovating in Gastric Cancer Drug Development The integration of EMR data into drug development and commercialization strategies presents an immense opportunity to revolutionize gastric cancer management. Pharmaceutical innovators and executives, are you ready to explore how real-world data can drive earlier diagnosis, improve risk modelling, and ultimately enhance patient outcomes? Let’s connect to discuss how advanced analytics and AI-driven EMR insights can shape the future of gastric cancer therapeutics. Looking forward to your thoughts in the comments or via direct conversation. This article was originally published on LinkedIn: Unlocking the Power of EMR Data for Earlier Gastric Cancer Diagnosis and Risk Prediction About the author Léon van Wouwe has 20+ years’ global experience in clinical development and operations, uniting data science with pharma and research. He drives cross-functional collaboration to advance innovative treatments. References World Health Organization . International Agency for Research on Cancer. Stomach Fact Sheet. Available at: https://gco.iarc.who.int/media/globocan/factsheets/cancers/7-stomach-fact-sheet.pdf. Accessed March 2025. Volv Global SA , project results. For information contact www.volv.global Li Y, et al. Postoperative recurrence of gastric cancer depends on whether the chemotherapy cycle was more than 9 cycles. Medicine. 2022;101(5):e28620. Ilic M, Ilic I. Epidemiology of stomach cancer. World J Gastroenterol. 2022;28(12):1187-1203. Al-Batran SE, et al. Perioperative chemotherapy with fluorouracil plus leucovorin, oxaliplatin, and docetaxel versus fluorouracil or capecitabine plus cisplatin and epirubicin for locally advanced, resectable gastric or gastro-oesophageal junction adenocarcinoma (FLOT4): a randomised, phase 2/3 trial. Lancet. 2019;393(10184):1948-1957. AstraZeneca : Imfinzi-based regimen demonstrated statistically significant and clinically meaningful improvement in event-free survival in resectable early-stage gastric and gastroesophageal junction cancers Header photo by mohamad azaam on Unsplash