The Stockholm early detection of cancer study (STEADY-CAN): rationale, design, data collection, and baseline characteristics for 2.7 million participants.

The Stockholm Early Detection of Cancer Study (STEADY-CAN) cohort was established to investigate strategies for early cancer detection in a population-based context within Stockholm County, the capital region of Sweden. Utilising real-world data to explore cancer-related healthcare patterns and outcomes, the cohort links extensive clinical and laboratory data from both inpatient and outpatient care in the region. The dataset includes demographic information, detailed diagnostic codes, laboratory results, prescribed medications, and healthcare utilisation data.

Correction: Lung cancer prediction using machine learning on data from a symptom e-questionnaire for never smokers, formers smokers and current smokers.

[This corrects the article DOI: 10.1371/journal.pone.

A machine learning tool for identifying non-metastatic colorectal cancer in primary care.

Background: Primary health care (PHC) is often the first point of contact when diagnosing colorectal cancer (CRC). Human limitations in processing large amounts of information warrant the use of machine learning as a diagnostic prediction tool for CRC.

Aim: To develop a predictive model for identifying non-metastatic CRC (NMCRC) among PHC patients using diagnostic data analysed with machine learning.

Validation of a diagnostic prediction tool for colorectal cancer: a case-control replication study.

Background: Early detection of colorectal cancer (CRC) is crucial for survival. Primary care, the first point of contact in most cases, needs supportive risk assessment tools. We aimed to replicate the Swedish Colorectal Cancer Risk Assessment Tool (SCCRAT) for non-metastatic CRC in primary care and examine if risk factor patterns depend on sex and age.

Lung cancer prediction using machine learning on data from a symptom e-questionnaire for never smokers, formers smokers and current smokers.

Purpose: The aim of the present study was to investigate the predictive ability for lung cancer of symptoms reported in an adaptive e-questionnaire, separately for never smokers, former smokers, and current smokers.

Patients And Methods: Consecutive patients referred for suspected lung cancer were recruited between September 2014 and November 2015 from the lung clinic at the Karolinska University Hospital, Stockholm, Sweden. A total of 504 patients were later diagnosed with lung cancer (n = 310) or no cancer (n = 194).