Beyond Initial Treatment: Navigating Recurrence Risk and Clinical Decisions in Cancer Survivors
By Léon Van Wouwe, Clinical Innovation Director, Volv Global Emerging artificial intelligence solutions offer promising strategies to address critical challenges in recurrence risk assessment and decision-making post-curative cancer treatment. Clinical management of cancer survivors following curative treatment presents a complex set of challenges, particularly in accurately assessing recurrence risk and making informed decisions about the necessity of additional therapies. These clinical complexities are compounded by evolving surveillance strategies, substantial patient heterogeneity, and inherent limitations in current evidence bases and conventional risk assessment tools. Key Clinical and Diagnostic Challenges After curative treatment, pinpointing the precise moment when a patient transitions into being “at risk” for recurrence is often unclear. Clinicians face difficult decisions regarding the optimal timing to initiate surveillance and risk assessment, whether immediately upon confirmation of disease-free status, upon completion of treatment, or at another clinical milestone altogether. The chosen time scale for follow-up and survival analysis, be it from initial diagnosis, the start of treatment, or the confirmation of being disease-free, can significantly influence clinical interpretations and research outcomes. (1) Surveillance regimens currently vary widely depending on cancer type, individual patient risk factors, and continually evolving international guidelines. Although the overarching goal of surveillance is to detect recurrence at a stage where intervention could be most effective, the evidence supporting intensive surveillance remains mixed. Additionally, the benefits of earlier detection regarding survival rates and overall quality of life remain uncertain for many cancer types. Notably, recurrence may be discovered either through routine surveillance or symptomatic presentation. Often, there exists a critical window – characterised by increased healthcare consultations in the months before recurrence diagnosis – where earlier identification may indeed be feasible. (2) The assessment of recurrence risk is further complicated by several critical factors: Lack of population-level data: Most data on recurrence and disease-free survival originate from clinical trials, typically involving younger, healthier patients who might not reflect the broader community population. (4) Variability in guideline adherence: Differences in interpretation and application of risk assessment guidelines by healthcare professionals can lead to significant underestimations or misclassifications of recurrence risk. For instance, guideline-consistent and clinician-assessed risk levels often diverge significantly in breast cancer cohorts.(3) Patient heterogeneity: Factors including age, tumour stage, molecular subtype, treatment history, and comorbidities profoundly influence recurrence risk, as well as the appropriateness of further therapeutic interventions. (5) Limitations and Opportunities in Risk Assessment Tools Traditional risk assessments primarily depend on clinicopathologic features, biomarkers, and imaging technologies. While indispensable, these tools often lack the sensitivity required to detect minimal residual disease effectively and might not fully capture recurrence risk complexity, particularly within diverse patient groups. (2, 4) Emerging techniques, such as circulating tumour DNA (ctDNA), advances in PET imaging, and new genomic markers, among other methods, are increasingly incorporated into surveillance and risk stratification. However, their optimal application and overall impact on clinical outcomes remain under active investigation. (2) The integration of artificial intelligence (AI) and machine learning (ML) presents a substantial opportunity to advance clinical practice. Recent studies in other fields, such as ML-based models predicting progression from community-acquired pneumonia (CAP) to acute respiratory distress syndrome (ARDS), demonstrate that outcome risk prediction models can achieve remarkable accuracy and clinical utility. These innovative models exploit large, heterogeneous datasets and advanced analytics, offering the potential to identify high-risk patients significantly earlier and more precisely than conventional methods. Translating such sophisticated approaches into oncology could fundamentally enhance recurrence risk assessment by: Seamlessly integrating diverse clinical, genomic, and imaging data to deliver individualised predictions. Identifying phenotypic subgroups and previously unknown risk factors. Supporting more precise stratification of patients for decisions regarding adjuvant therapies. Decision-Making for Adjuvant Therapy Determining the initiation of adjuvant or additional therapies following curative treatment necessitates a careful balancing act. Clinicians must weigh the recurrence risk against potential adverse effects of overtreatment, such as treatment-related toxicity, financial burden, and impacts on patient quality of life. (4, 6, 8) Decision-making complexities are further magnified by: Patient values and preferences: Integrating patient perspectives is essential, given the broad variability in individual risk tolerance and treatment goals. (2, 9) Guideline variability: Divergences in clinical practice guidelines, driven by evolving evidence and methodologies, complicate the standardisation of care and the assurance of optimal outcomes. (5) Communication gaps: Many patients remain unaware of their personal recurrence risk or lack detailed discussions with healthcare providers, underscoring the necessity of improved risk communication and shared decision-making. (9) Summary Table: Clinical Management Challenges Challenge/Factor Current Limitation/Status Emerging Solution/Trend Example Scenario Defining “at risk” period Unclear clinical milestones for risk onset Standardised time points, AI-driven analytics Post-treatment surveillance timing Risk assessment tools Limited sensitivity/specificity, population bias Genomic profiling, ctDNA, ML/AI models Breast cancer recurrence risk Surveillance strategies Mixed evidence for intensive follow-up Individualised, risk-adapted surveillance Lung cancer regular scans Guideline adherence Divergence between clinician and guideline assessments Education, decision-support tools Breast cancer risk underestimation Patient heterogeneity Diverse risk profiles, comorbidities Personalised medicine, AI stratification Elderly or multimorbid patients Communication and shared decisions Gaps in risk discussion and preference integration Patient-centered risk communication Survivorship care planning Conclusion: The Potential of AI-Driven Outcome Prediction Assessing recurrence risk and making informed adjuvant therapy decisions post-curative cancer treatment remains fraught with clinical and operational difficulties. Traditional tools and guidelines, while vital, often fail to capture the full complexity of recurrence risk across diverse patient populations. AI and ML technologies, exemplified by successful outcome prediction models in conditions like CAP to ARDS, represent a transformative advancement for oncology. These methods can synthesise vast, heterogeneous datasets, delivering precise individualised risk predictions. Such innovation will support more accurate therapeutic decisions, reduce overtreatment, and enhance patient quality of life, ultimately fostering more confident, patient-centric care for cancer survivors. 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 American Cancer Society. (n.d.). Follow-up care after cancer treatment. Retrieved from https://www.cancer.org/cancer/survivorship/long-term-health-concerns/importance-of-follow-up-care.html American Society of Clinical Oncology (ASCO). (2024). Guideline concordance
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. Links: Roche: New data show Roche’s Itovebi significantly extended survival in a certain type of HR-positive advanced breast cancer Volv Global: We find more of the right patients Volv Global: We detect diseases at an earlier stage Volv Global: Beyond Initial Treatment: Navigating Recurrence Risk and Clinical Decisions in Cancer Survivors
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. Links: Volv Global: We find more of the right patients Volv Global: We predict patient outcomes Volv Global: We stratify patient cohorts
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