Early breast cancer survival of black and white American women with equal diagnostic and therapeutic management

Associations amongst genes, molecules, cells, and organs in breast cancer metastasis

This paper is a cross fertilization of ideas about the importance of molecular aspects of breast cancer metastasis by basic scientists, a pathologist, and clinical oncologists at the Henry Ford Health symposium. We address four major topics: (i) the complex roles of lymphatic endothelial cells and the molecules that stimulate them to enhance lymph node and systemic metastasis and influence the anti-tumor immunity that might inhibit metastasis; (ii) the interaction of molecules and cells when breast cancer spreads to bone, and how bone metastases may themselves spread to internal viscera; (iii) how molecular expression and morphologic subtypes of breast cancer assist clinicians in determining which patients to treat with more or less aggressive therapies; (iv) how the outcomes of patients with oligometastases in breast cancer are different from those with multiple metastases and how that could justify the aggressive treatment of these patients with the hope of cure.

Machine learning prediction of pathological complete response and overall survival of breast cancer patients in an underserved inner-city population

BACKGROUND

Generalizability of predictive models for pathological complete response (pCR) and overall survival (OS) in breast cancer patients requires diverse datasets. This study employed four machine learning models to predict pCR and OS up to 7.5 years using data from a diverse and underserved inner-city population.

METHODS

Demographics, staging, tumor subtypes, income, insurance status, and data from radiology reports were obtained from 475 breast cancer patients on neoadjuvant chemotherapy in an inner-city health system (01/01/2012 to 12/31/2021). Logistic regression, Neural Network, Random Forest, and Gradient Boosted Regression models were used to predict outcomes (pCR and OS) with fivefold cross validation.

RESULTS

pCR was not associated with age, race, ethnicity, tumor staging, Nottingham grade, income, and insurance status (p > 0.05). ER-/HER2+ showed the highest pCR rate, followed by triple negative, ER+/HER2+, and ER+/HER2- (all p < 0.05), tumor size (p < 0.003) and background parenchymal enhancement (BPE) (p < 0.01). Machine learning models ranked ER+/HER2-, ER-/HER2+, tumor size, and BPE as top predictors of pCR (AUC = 0.74-0.76). OS was associated with race, pCR status, tumor subtype, and insurance status (p < 0.05), but not ethnicity and incomes (p > 0.05). Machine learning models ranked tumor stage, pCR, nodal stage, and triple-negative subtype as top predictors of OS (AUC = 0.83-0.85). When grouping race and ethnicity by tumor subtypes, neither OS nor pCR were different due to race and ethnicity for each tumor subtype (p > 0.05).

CONCLUSION

Tumor subtypes and imaging characteristics were top predictors of pCR in our inner-city population. Insurance status, race, tumor subtypes and pCR were associated with OS. Machine learning models accurately predicted pCR and OS.