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Simple Summary: Diagnosing most cancers at an earlier stage improves outcomes because treatments are more effective and often less invasive. This study looked at the health patterns of adults enrolled in Alberta's Tomorrow Project before they were diagnosed with cancer to identify factors related to cancers that are caught early or late. These identified factors were then tested in a similar group of adults from the British Columbia Generations Project who also subsequently developed cancer, to see how well they could predict stage at diagnosis. The confirmed health patterns can be used to improve cancer prevention programs and activities to find cancer earlier in people who are at most risk of being diagnosed with late-stage cancer. Risk prediction models for cancer stage at diagnosis may identify individuals at higher risk of late-stage cancer diagnoses. Partial proportional odds risk prediction models for cancer stage at diagnosis for males and females were developed using data from Alberta's Tomorrow Project (ATP). Prediction models were validated on the British Columbia Generations Project (BCGP) cohort using discrimination and calibration measures. Among ATP males, older age at diagnosis was associated with an earlier stage at diagnosis, while full- or part-time employment, prostate-specific antigen testing, and former/current smoking were associated with a later stage at diagnosis. Among ATP females, mammogram and sigmoidoscopy or colonoscopy were associated with an earlier stage at diagnosis, while older age at diagnosis, number of pregnancies, and hysterectomy were associated with a later stage at diagnosis. On external validation, discrimination results were poor for both males and females while calibration results indicated that the models did not over- or under-fit to derivation data or over- or under-predict risk. Multiple factors associated with cancer stage at diagnosis were identified among ATP participants. While the prediction model calibration was acceptable, discrimination was poor when applied to BCGP data. Updating our models with additional predictors may help improve predictive performance. [ABSTRACT FROM AUTHOR]

Simple Summary: Increasing knowledge of outcome prognostication, derived from various clinical and imaging-derived parameters, has the potential for pretreatment risk stratification, allowing clinicians to deliver more patient specific treatment tailored to individual risk. Our study indicates that a prognostic model, combining clinical parameter, PET and CT radiomics with sarcopenic status, derived from standard of care clinical 18F-FDG-PET/CT improves outcome prognostication among patients with advanced, metastatic esophageal and gastroesophageal cancer. We investigated, whether 18[18F]-FDG PET/CT-derived radiomics combined with sarcopenia measurements improves survival prognostication among patients with advanced, metastatic gastroesophageal cancer. In our study, 128 consecutive patients with advanced, metastatic esophageal and gastroesophageal cancer (n = 128; 26 females; 102 males; mean age 63.5 ± 11.7 years; age range: 29–91 years) undergoing 18[18F]-FDG PET/CT for staging between November 2008 and December 2019 were included. Segmentation of the primary tumor and radiomics analysis derived from PET and CT images was performed semi-automatically with a commonly used open-source software platform (LIFEX, Version 6.30, lifexsoft.org). Patients' nutritional status was determined by measuring the skeletal muscle index (SMI) at the level of L3 on the CT component. Univariable and multivariable analyses were performed to establish a survival prediction model including radiomics, clinical data, and SMI score. Univariable Cox proportional hazards model revealed ECOG (<0.001) and bone metastasis (p = 0.028) to be significant clinical parameters for overall survival (OS) and progression free survival (PFS). Age (p = 0.017) was an additional prognostic factor for OS. Multivariable analysis showed improved prognostication for overall and progression free survival when adding sarcopenic status, PET and CT radiomics to the model with clinical parameters only. PET and CT radiomics derived from hybrid 18[18F]-FDG PET/CT combined with sarcopenia measurements and clinical parameters may improve survival prediction among patients with advanced, metastatic gastroesophageal cancer. [ABSTRACT FROM AUTHOR]