Early cancer detection through symptoms and signs

Early cancer detection is critical for improving survival rates, yet it requires a careful balance between minimising missed diagnoses and avoiding over- investigation. Primary care plays a central role in this process. This thesis aims to enhance understanding of the complexities involved in early cancer detection by analysing symptoms and signs, with the goal of contributing to the development of risk assessment and prediction tools to identify cancer at an early stage within primary care.

This thesis is based on five quantitative studies conducted within the Swedish healthcare system. Study I examined symptoms reported by referred patients via questionnaires at the Department of Pulmonary Medicine at Karolinska University Hospital. It investigated whether machine learning could predict which patients subsequently received a lung cancer diagnosis, stratified by smoking status. Studies II and III focused on comprehensive diagnostic data and coded symptoms from primary care to facilitate early detection of non-metastatic colorectal cancer, with Study II conducted in Region Stockholm and Study III in Region Västra Götaland. Studies IV and V used comprehensive clinical and laboratory data from the entire adult population of Stockholm County. Study IV presents a cohort description, while Study V examines the association and discriminatory capacity of newly developed anaemia as an indicator for cancer.

In Study I, the findings demonstrate that predictive models, using machine learning, exhibit good discriminatory ability for patients who either never smoked or were current smokers. In Study II, an existing Swedish risk assessment tool was validated by replicating it in a different region, with consistent results across regions. In Study III, a new predictive model was developed using machine learning to analyse all diagnostic data for identifying non-metastatic colorectal cancer. Study IV introduces the extensive STEADY-CAN (Stockholm early detection of cancer study) cohort, providing opportunities to analyse early cancer detection patterns. Study V investigates the association between newly developed anaemia and cancer risk within the STEADY-CAN cohort, revealing a significant impact on risk assessment.

These findings collectively address the value of improved risk stratification in primary care by leveraging existing data to better identify patients at elevated risk of having cancer. The results highlight key themes in early detection- predictive accuracy, risk stratification, clinical utility, and applicability across populations-and identify areas where current evidence has been limited or inconclusive. Future research should prioritise the validation of novel diagnostic approaches and the development of systems that support clinical decision- making, while upholding the principles of accessible, patient-centred care. Keywords: Early cancer detection, Primary care, Machine learning, Colorectal cancer, Lung cancer, Risk assessment tools, Anaemia, STEADY-CAN.

List of scientific papers

I. Nemlander E, Rosenblad A, Abedi E, Ekman S, Hasselström J, Eriksson LE, Carlsson AC. Lung cancer prediction using machine learning on data from a symptom e-questionnaire for never smokers, formers smokers and current smokers. PLoS One. 2022 Oct 21;17(10):e0276703. PMID: 36269746; PMCID: PMC9586380.
https://doi.org/10.1371/journal.pone.0276703

Erratum for: PLoS One. 2022 Oct 21;17(10):e0276703. PMID: 38060550; PMCID: PMC10703334. https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0295780

II. Nemlander E, Rosenblad A, Abedi E, Hasselström J, Sjövall A, Carlsson AC, Ewing M. Validation of a diagnostic prediction tool for colorectal cancer: a case-control replication study. Fam Pract. 2023 Jan 5:cmac147. PMID: 36611019. https://doi.org/10.1093/fampra/cmac147

III. Nemlander E, Ewing M, Abedi E, Hasselström J, Sjövall A, Carlsson AC, Rosenblad A. A machine learning tool for identifying non- metastatic colorectal cancer in primary care. European Journal of Cancer. 2023 Jan 11. https://doi.org/10.1016/j.ejca.2023.01.011

IV. Nemlander E, Abedi E, Ljungman P, Hasselström J, Carlsson AC, Rosenblad A. The Stockholm Early Detection of Cancer Study (STEADY-CAN): rationale, design, data collection, and baseline characteristics for 2.7 million participants. https://doi.org/10.1007/s10654-024-01192-8

V. Nemlander E, Rosenblad A, Abedi E, Hasselström J, Ljungman P, Carlsson AC. Newly developed anaemia predicts incident cancer and death within 18 months: Findings from 1.1 million patients in the Stockholm Early Detection of Cancer Study (STEADY-CAN) cohort. [Manuscript]