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.

Deep learning applications to breast cancer detection by magnetic resonance imaging: a literature review.

Deep learning analysis of radiological images has the potential to improve diagnostic accuracy of breast cancer, ultimately leading to better patient outcomes. This paper systematically reviewed the current literature on deep learning detection of breast cancer based on magnetic resonance imaging (MRI). The literature search was performed from 2015 to Dec 31, 2022, using Pubmed. Other database included Semantic Scholar, ACM Digital Library, Google search, Google Scholar, and pre-print depositories (such as Research Square). Articles that were not deep learning (such as texture analysis) were excluded. PRISMA guidelines for reporting were used. We analyzed different deep learning algorithms, methods of analysis, experimental design, MRI image types, types of ground truths, sample sizes, numbers of benign and malignant lesions, and performance in the literature. We discussed lessons learned, challenges to broad deployment in clinical practice and suggested future research directions.

Three dimensional evaluation of cerebrovascular density and branching in chronic traumatic encephalopathy.

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease associated with exposure to repetitive head impacts (RHI) and characterized by perivascular accumulations of hyperphosphorylated tau protein (p-tau) at the depths of the cortical sulci. Studies of living athletes exposed to RHI, including concussive and nonconcussive impacts, have shown increased blood-brain barrier permeability, reduced cerebral blood flow, and alterations in vasoreactivity. Blood-brain barrier abnormalities have also been reported in individuals neuropathologically diagnosed with CTE. To further investigate the three-dimensional microvascular changes in individuals diagnosed with CTE and controls, we used SHIELD tissue processing and passive delipidation to optically clear and label blocks of postmortem human dorsolateral frontal cortex. We used fluorescent confocal microscopy to quantitate vascular branch density and fraction volume. We compared the findings in 41 male brain donors, age at death 31-89 years, mean age 64 years, including 12 donors with low CTE (McKee stage I-II), 13 with high CTE (McKee stage III-IV) to 16 age- and sex-matched non-CTE controls (7 with RHI exposure and 9 with no RHI exposure). The density of vessel branches in the gray matter sulcus was significantly greater in CTE cases than in controls. The ratios of sulcus versus gyrus vessel branch density and fraction volume were also greater in CTE than in controls and significantly above one for the CTE group. Hyperphosphorylated tau pathology density correlated with gray matter sulcus fraction volume. These findings point towards increased vascular coverage and branching in the dorsolateral frontal cortex (DLF) sulci in CTE, that correlates with p-tau pathology.

Incidence, characteristics, risk factors and outcomes of diabetic ketoacidosis in COVID-19 patients: Comparison with influenza and pre-pandemic data.

AIMS: This study characterized incidence, patient profiles, risk factors and outcomes of in-hospital diabetic ketoacidosis (DKA) in patients with COVID-19 compared with influenza and pre-pandemic data. METHODS: This study consisted of 13 383 hospitalized patients with COVID-19 (March 2020-July 2022), 19 165 hospitalized patients with influenza (January 2018-July 2022) and 35 000 randomly sampled hospitalized pre-pandemic patients (January 2017-December 2019) in Montefiore Health System, Bronx, NY, USA. Primary outcomes were incidence of in-hospital DKA, in-hospital mortality, and insulin use at 3 and 6 months post-infection. Risk factors for developing DKA were identified. RESULTS: The overall incidence of DKA in patients with COVID-19 and influenza, and pre-pandemic were 2.1%, 1.4% and 0.5%, respectively (p < .05 pairwise). Patients with COVID-19 with DKA had worse acute outcomes (p < .05) and higher incidence of new insulin treatment 3 and 6 months post-infection compared with patients with influenza with DKA (p < .05). The incidence of DKA in patients with COVID-19 was highest among patients with type 1 diabetes (12.8%), followed by patients with insulin-dependent type 2 diabetes (T2D; 5.2%), non-insulin dependent T2D (2.3%) and, lastly, patients without T2D (1.3%). Patients with COVID-19 with DKA had worse disease severity and higher mortality [odds ratio = 6.178 (4.428-8.590), p < .0001] compared with those without DKA. Type 1 diabetes, steroid therapy for COVID-19, COVID-19 status, black race and male gender were associated with increased risk of DKA. CONCLUSIONS: The incidence of DKA was higher in COVID-19 cohort compared to the influenza and pre-pandemic cohort. Patients with COVID-19 with DKA had worse outcomes compared with those without. Many COVID-19 survivors who developed DKA during hospitalization became insulin dependent. Identification of risk factors for DKA and new insulin-dependency could enable careful monitoring and timely intervention.

COVID-19 associated ketosis and diabetic ketoacidosis: A rapid review.

SARS-CoV-2 infection could disrupt the endocrine system directly or indirectly, which could result in endocrine dysfunction and glycaemic dysregulation, triggering transient or persistent diabetes mellitus. The literature on the complex relationship between COVID-19 and endocrine dysfunctions is still evolving and remains incompletely understood. Thus, we conducted a review on all literature to date involving COVID-19 associated ketosis or diabetic ketoacidosis (DKA). In total, 27 publications were included and analysed quantitatively and qualitatively. Studies included patients with DKA with existing or new onset diabetes. While the number of case and cohort studies was limited, DKA in the setting of COVID-19 seemed to increase risk of death, particularly in patients with new onset diabetes. Future studies with more specific variables and larger sample sizes are needed to draw better conclusions.

Imaging evaluation of lymphoma in pregnancy with review of clinical assessment and treatment options.

Lymphoma-related malignancies can be categorized as Hodgkin's lymphoma (HL) or non-Hodgkin's lymphoma (NHL) based on histologic characteristics. Although quite rare during pregnancy, HL and NHL are the fourth and fifth most common malignancies during the pregnancy period, respectively. Given the rarity of lymphoma among pregnant patients, radiologists are usually unfamiliar with the modifications required for staging and treatment of this population, even those who work at centers with busy obstetrical services. Therefore, this manuscript serves to not only review the abdominopelvic imaging features of lymphoma in pregnancy, but it also discusses topics including birthing parent and fetal lymphoma-related prognosis, both antenatal and postpartum, current concepts in the management of pregnancy-related lymphoma, as well as the current considerations regarding birthing parent onco-fertility.