KEY TAKEAWAYS
- The CANTO trial aimed to develop a model to identify groups of breast cancer pts at risk for long-term cancer-related behavioral symptoms.
- The study used a multivariable logistic regression model to identify clinical, behavioral, and treatment-related predictors of long-term cancer-related behavioral symptoms in breast cancer pts.
- The study found that predictive tools at diagnosis can help identify breast cancer pts at risk for severe long-term symptoms.
Fatigue, cognitive impairment, emotional distress, and sleep disturbance are common and difficult symptoms for breast cancer patients (pts). They often occur together, respond to similar treatments, and can be grouped as cancer-related behavioral symptoms (CRBS).
Researchers aimed to develop a model to identify groups of breast cancer pts at risk for long-term cancer-related behavioral symptoms.
The study collected longitudinal data at diagnosis and yearly post-diagnosis. The outcome of interest is the proportion of pts reporting a cluster of ≥3 severe CRBS (as measured by the EORTC QLQ-C30/HADS questionnaire) 4 years post-diagnosis. Clinical, behavioral, and treatment-related predictors of long-term CRBS were tested in a training cohort using a multivariable logistic regression model with bootstrapped Augmented Backwards Elimination and 10-fold internal cross-validation. The model was then externally validated in pts from a subsequent enrollment period.
In the training group (3,555 pts), 92.1% had stage I-II breast cancer, 52.6% received chemotherapy, and 81.9% received endocrine therapy. At diagnosis, 22.0% experienced severe fatigue (C30 score ≥ 40/100), 29.7% had severe cognitive impairment (C30 score < 75/100), 33.9% had clinically suggestive anxiety, and 6.5% had clinically suggestive depression (HADS score ≥ 11/21). About 35.2% had severe insomnia (C30 score > 50/100). At diagnosis, 19.0% reported severe cancer-related behavioral symptoms (CRBS), which increased to 21.0% four years after diagnosis. Predictors of CRBS clusters included younger age, prior psychiatric disorders, and higher baseline BSS (AUC 0.73 [95% CI 0.71-0.75]). These findings were also confirmed in the validation group (1533 pts).
The study found that predictive tools at diagnosis can help identify breast cancer pts at risk for severe long-term symptoms.
Source: https://ascopubs.org/doi/abs/10.1200/JCO.2023.41.16_suppl.12087
Clinical Trial: https://classic.clinicaltrials.gov/ct2/show/NCT01993498
Martina Pagliuca, Julie Havas, Florence Lerebours, Olivier Rigal, Thierry Petit, Sylvie Giacchetti, Florence Dalenc, Johanna Wassermann, Olivier Arsene, Anne-Laure Martin, Sibille Everhard, Ines Maria Vaz Duarte Luis, and Antonio Di Meglio. DOI: 10.1200/JCO.2023.41.16_suppl.12087 Journal of Clinical Oncology 41, no. 16_suppl (June 01, 2023) 12087-12087.
