Efficient and Rapid Microfluidics Production of Bio-Inspired Nanoparticles Derived from Silkworm for Enhanced Breast Cancer Treatment.

: In the quest for sustainable and biocompatible materials, silk fibroin (SF), derived from natural silk, has emerged as a promising candidate for nanoparticle production. This study aimed to fabricate silk fibroin particles (SFPs) using a novel swirl mixer previously presented by our group, evaluating their characteristics and suitability for drug delivery applications, including magnetic nanoparticles and dual-drug encapsulation with curcumin (CUR) and 5-fluorouracil (5-FU). : SFPs were fabricated via microfluidics-assisted desolvation using a swirl mixer, ensuring precise mixing kinetics.

Evaluation of cyclin-dependent kinase 4/6 inhibitor-induced serum creatinine elevations in patients with hormone receptor positive breast cancer.

Background: Cyclin-dependent kinase 4 and 6 inhibitors (CDK4/6i) have improved the efficacy of endocrine therapy in hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer (BC) and are now used in both early-stage and metastatic disease. Recent case reports suggest that pseudo-serum creatinine (Scr) elevations are likely a class effect of CDK4/6i.

Methods: This single-center retrospective analysis included patients aged ≥18 years who received at least one dose of palbociclib, ribociclib, or abemaciclib for the treatment of HR+/HER2- BC in the early or advanced setting.

Generative adversarial networks accurately reconstruct pan-cancer histology from pathologic, genomic, and radiographic latent features.

Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of molecular features. These approaches distill cancer histologic images into high-level features, which are used in predictions, but understanding the biologic meaning of such features remains challenging. We present and validate a custom generative adversarial network-HistoXGAN-capable of reconstructing representative histology using feature vectors produced by common feature extractors.

Predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer using a machine learning approach.

Background: For patients with breast cancer undergoing neoadjuvant chemotherapy (NACT), most of the existing prediction models of pathologic complete response (pCR) using clinicopathological features were based on standard statistical models like logistic regression, while models based on machine learning mostly utilized imaging data and/or gene expression data. This study aims to develop a robust and accessible machine learning model to predict pCR using clinicopathological features alone, which can be used to facilitate clinical decision-making in diverse settings.

Methods: The model was developed and validated within the National Cancer Data Base (NCDB, 2018-2020) and an external cohort at the University of Chicago (2010-2020).

ASO Author Reflections: Breast-Conserving Surgery After Neoadjuvant Systemic Therapy for Early-Stage Breast Cancer: Quantitative Biomarkers and Disparities in the Precision-Medicine Era.

In this era of precision medicine, incorporating quantitative measures of estrogen receptor (ER)/progesterone receptor (PR)/Ki-67 expressions and genomic assays could more precisely identify neoadjuvant systemic therapy with the highest likelihood of response and tumor downstaging. In our recent study, we quantified the likelihood of achieving breast-conserving surgery (BCS vs. mastectomy) after neoadjuvant chemotherapy or endocrine therapy as a function of demographics, quantitative ER/PR/Ki-67 expressions, 21-gene recurrence scores, or 70-gene risk scores in early-stage, hormone receptor (HR)-positive/human epidermal growth factor receptor 2 (HER2)-negative breast cancer.

Datopotamab-deruxtecan plus durvalumab in early-stage breast cancer: the sequential multiple assignment randomized I-SPY2.2 phase 2 trial.

Sequential adaptive trial designs can help accomplish the goals of personalized medicine, optimizing outcomes and avoiding unnecessary toxicity. Here we describe the results of incorporating a promising antibody-drug conjugate, datopotamab-deruxtecan (Dato-DXd) in combination with programmed cell death-ligand 1 inhibitor, durvalumab, as the first sequence of therapy in the I-SPY2.2 phase 2 neoadjuvant sequential multiple assignment randomization trial for high-risk stage 2/3 breast cancer.

Quantitative Biomarkers, Genomic Assays, and Demographics Associated with Breast-Conserving Surgery Following Neoadjuvant Therapy in Early-Stage, Hormone Receptor-Positive, HER-Negative Breast Cancer.

Background: Given increased neoadjuvant therapy use in early-stage, hormone receptor (HR)-positive/HER2-negative breast cancer, we sought to quantify likelihood of breast-conserving surgery (BCS) after neoadjuvant chemotherapy (NACT) or endocrine therapy (NET) as a function of ER%/PR%/Ki-67%, 21-gene recurrence scores (RS), or 70-gene risk groups.

Methods: We analyzed the 2010-2020 National Cancer Database. Surgery was categorized as "mastectomy/BCS.

Assessment of long-term psychosocial outcomes in N-methyl-D-aspartate receptor encephalitis - the SAPIENCE study protocol.

Background: N-methyl-D-aspartate-receptor (NMDAR) encephalitis is a rare neurological autoimmune disease with severe neuropsychiatric symptoms during the acute phase. Despite good functional neurological recovery, most patients continue to experience cognitive, psychiatric, psychological, and social impairments years after the acute phase. However, the precise nature and evolving patterns over time of these long-term consequences remain unclear, and their implications for the well-being and quality of life of predominantly young patients have yet to be thoroughly examined.

Developing a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning.

Background: Deployment and access to state-of-the-art precision medicine technologies remains a fundamental challenge in providing equitable global cancer care in low-resource settings. The expansion of digital pathology in recent years and its potential interface with diagnostic artificial intelligence algorithms provides an opportunity to democratize access to personalized medicine. Current digital pathology workstations, however, cost thousands to hundreds of thousands of dollars.

Assessing the Relationship Between Neighborhood Socioeconomic Disadvantage and Telemedicine Use Among Patients With Breast Cancer and Examining Differential Provisions of Oncology Services Between Telehealth and In-Person Visits: Quantitative Study.

Background: Since the COVID-19 pandemic began, we have seen rapid growth in telemedicine use. However, telehealth care and services are not equally distributed, and not all patients with breast cancer have equal access across US regions. There are notable gaps in existing literature regarding the influence of neighborhood-level socioeconomic status on telemedicine use in patients with breast cancer and oncology services offered through telehealth versus in-person visits.

Evaluation of large language models as a diagnostic aid for complex medical cases.

Background: The use of large language models (LLM) has recently gained popularity in diverse areas, including answering questions posted by patients as well as medical professionals.

Objective: To evaluate the performance and limitations of LLMs in providing the correct diagnosis for a complex clinical case.

Design: Seventy-five consecutive clinical cases were selected from the Massachusetts General Hospital Case Records, and differential diagnoses were generated by OpenAI's GPT3.

Development and validation of a clinical breast cancer tool for accurate prediction of recurrence.

Given high costs of Oncotype DX (ODX) testing, widely used in recurrence risk assessment for early-stage breast cancer, studies have predicted ODX using quantitative clinicopathologic variables. However, such models have incorporated only small cohorts. Using a cohort of patients from the National Cancer Database (NCDB, n = 53,346), we trained machine learning models to predict low-risk (0-25) or high-risk (26-100) ODX using quantitative estrogen receptor (ER)/progesterone receptor (PR)/Ki-67 status, quantitative ER/PR status alone, and no quantitative features.

Characterizing the Increase in Artificial Intelligence Content Detection in Oncology Scientific Abstracts From 2021 to 2023.

Purpose: Artificial intelligence (AI) models can generate scientific abstracts that are difficult to distinguish from the work of human authors. The use of AI in scientific writing and performance of AI detection tools are poorly characterized.

Methods: We extracted text from published scientific abstracts from the ASCO 2021-2023 Annual Meetings.

Generative Adversarial Networks Accurately Reconstruct Pan-Cancer Histology from Pathologic, Genomic, and Radiographic Latent Features.

Artificial intelligence models have been increasingly used in the analysis of tumor histology to perform tasks ranging from routine classification to identification of novel molecular features. These approaches distill cancer histologic images into high-level features which are used in predictions, but understanding the biologic meaning of such features remains challenging. We present and validate a custom generative adversarial network - HistoXGAN - capable of reconstructing representative histology using feature vectors produced by common feature extractors.

Understanding the extent to which PROMs and PREMs used with older people with severe frailty capture their multidimensional needs: A scoping review.

Background: Older people with severe frailty are nearing the end of life but their needs are often unknown and unmet. Systematic ways to capture and measure the needs of this group are required. Patient reported Outcome Measures (PROMs) & Patient reported Experience Measures (PREMs) are possible tools to assist this.

Validation of the RSClin risk calculator in the National Cancer Data Base.

Background: Guidelines recommend the use of genomic assays such as OncotypeDx to aid in decisions regarding the use of chemotherapy for hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer. The RSClin prognostic tool integrates OncotypeDx and clinicopathologic features to predict distant recurrence and chemotherapy benefit, but further validation is needed before broad clinical adoption.

Methods: This study included patients from the National Cancer Data Base (NCDB) who were diagnosed with stage I-III HR+/HER2- breast cancer from 2010 to 2020 and received adjuvant endocrine therapy with or without chemotherapy.

External Evaluation of a Mammography-based Deep Learning Model for Predicting Breast Cancer in an Ethnically Diverse Population.

Purpose: To externally evaluate a mammography-based deep learning (DL) model (Mirai) in a high-risk racially diverse population and compare its performance with other mammographic measures.

Materials And Methods: A total of 6435 screening mammograms in 2096 female patients (median age, 56.4 years ± 11.

Psychosocial care for cancer survivors: A global review of national cancer control plans.

Objective: National Cancer Control Plans (NCCPs) are high-level policy documents that prioritise actions to be taken to improve cancer control activities. As the number of cancer survivors grows globally, there is an urgent need to assess whether and how psychosocial care across the cancer care continuum is included in NCCPs. This review aimed to ascertain the extent to which NCCPs referenced psycho-oncology care for cancer survivors in the post-treatment phase.

Bilateral asymmetry of quantitative parenchymal kinetics at ultrafast DCE-MRI predict response to neoadjuvant chemotherapy in patients with HER2+ breast cancer.

Purpose: To assess whether measurement of the bilateral asymmetry of semiquantitative and quantitative perfusion parameters from ultrafast dynamic contrast-enhanced MRI (DCE-MRI), allows early prediction of pathologic response after neoadjuvant chemotherapy (NAC) in patients with HER2+ breast cancer.

Materials And Methods: Twenty-eight female patients with HER2+ breast cancer treated with NAC who underwent pre-NAC ultrafast DCE-MRI (3-9 s/phase) were enrolled for this study. Four semiquantitative and two quantitative parenchymal parameters were calculated for each patient.

The impact of survivorship care plans on adolescent and young adult head and neck cancer survivors and their primary care providers.

Purpose: A cross sectional study of adolescent and young adult (AYA) head and neck (H&N) cancer survivors was performed to assess late effects. Survivorship care plans (SCPs) were generated and evaluated by participants and their primary care providers (PCPs).

Methods: AYA H&N survivors who had been discharged over 5 years prior from our institution were assessed in recall consultation by a radiation oncologist.

Molecular profiling of a real-world breast cancer cohort with genetically inferred ancestries reveals actionable tumor biology differences between European ancestry and African ancestry patient populations.

Background: Endocrine-resistant HR+/HER2- breast cancer (BC) and triple-negative BC (TNBC) are of interest for molecularly informed treatment due to their aggressive natures and limited treatment profiles. Patients of African Ancestry (AA) experience higher rates of TNBC and mortality than European Ancestry (EA) patients, despite lower overall BC incidence. Here, we compare the molecular landscapes of AA and EA patients with HR+/HER2- BC and TNBC in a real-world cohort to promote equity in precision oncology by illuminating the heterogeneity of potentially druggable genomic and transcriptomic pathways.