From ductal carcinoma in situ to invasive breast cancer: the prognostic value of the extracellular microenvironment DOI Creative Commons
Taylor S. Hulahan, Peggi M. Angel

Journal of Experimental & Clinical Cancer Research, Journal Year: 2024, Volume and Issue: 43(1)

Published: Dec. 23, 2024

Abstract Ductal carcinoma in situ (DCIS) is a noninvasive breast disease that variably progresses to invasive cancer (IBC). Given the unpredictability of this progression, most DCIS patients are aggressively managed similar IBC patients. Undoubtedly, treatment paradigm places many at risk overtreatment and its significant consequences. Historically, prognostic modeling has included assessment clinicopathological features genomic markers. Although these provide valuable insights into tumor biology, they remain insufficient predict which will progress IBC. Contemporary work begun focus on microenvironment surrounding ductal cells for molecular patterns might progression. In review, extracellular alterations occurring with malignant transformation from detailed. Not only do changes collagen abundance, organization, localization mediate transition IBC, but also discrete post-translational regulation fibers understood promote invasion. Other matrix proteins, such as metalloproteases, decorin, tenascin C, have been characterized their role further demonstrate value matrix. Importantly, proteins influence immune fibroblasts toward pro-tumorigenic phenotypes. Thus, progressive play key invasion promise development.

Language: Английский

Prediction of biochemical prostate cancer recurrence from any Gleason score using robust tissue structure and clinically available information DOI Creative Commons

Laura Marin,

Daniel I. Zavaleta-Guzman,

Jessyca I. Gutierrez-Garcia

et al.

Discover Oncology, Journal Year: 2025, Volume and Issue: 16(1)

Published: Feb. 7, 2025

Language: Английский

Citations

0
Harnessing artificial intelligence for theragnostic applications: Current landscape and future directions DOI

Arundhati Pande,

Abhishek Kumar, Ashish Anjankar

et al.

Multidisciplinary Reviews, Journal Year: 2025, Volume and Issue: 8(7), P. 2025218 - 2025218

Published: Feb. 7, 2025

In the area of theragnostics, use artificial intelligence (AI) is supporting personalised medicine methods that merge therapeutic and diagnostic techniques, which causing sector to undergo a transition. An analysis historical backdrop, current condition, promise intelligence-enhanced theragnostic systems presented in this article. We investigate underlying ideas intelligence, such as machine learning, deep neural networks, well their applications variety medical fields, including cancer, pathology, imaging, cardiology, hypertension control, diabetes management. The ability integrate wide information, recognise trends, enable real-time decision-making patient monitoring all illustrate competency. It possible digital twins, make adaptive learning algorithms dynamic virtual models, might be used optimise treatment regimens anticipate course illness. Important prospects for advancement biomedical research therapy are by biochip technology driven intelligence. This includes gene chips, organ-on-a-chip systems, biosensors. However, there number obstacles must overcome before can effectively theragnostics. These include data security, privacy, algorithmic biases, legal frameworks, acceptability. vital, order realise full potential AI-driven address these constraints means extensive validation, diversified datasets, explainable clear communication. anticipated synergistic combination theragnostics will revolutionise precision continues advance. it more accurate diagnoses, achieve tailored therapeutics, better outcomes.

Language: Английский

Citations

0
Towards precision therapy in HER2-positive early-stage breast cancer DOI Open Access
Serena Di Cosimo, Paolo Verderio

The Breast, Journal Year: 2025, Volume and Issue: unknown, P. 104461 - 104461

Published: March 1, 2025

Language: Английский

Citations

0
Multimodal recurrence risk prediction model for HR+/HER2- early breast cancer following adjuvant chemo-endocrine therapy: integrating pathology image and clinicalpathological features DOI Creative Commons
Xiaoyan Wu, Yiman Li, Ji‐Long Chen

et al.

Breast Cancer Research, Journal Year: 2025, Volume and Issue: 27(1)

Published: March 28, 2025

Language: Английский

Citations

0
AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer DOI Creative Commons
Noorul Wahab, Michael S. Toss, Islam M. Miligy

et al.

npj Precision Oncology, Journal Year: 2023, Volume and Issue: 7(1)

Published: Nov. 15, 2023

Breast cancer (BC) grade is a well-established subjective prognostic indicator of tumour aggressiveness. Tumour heterogeneity and assessment result in high degree variability among observers BC grading. Here we propose an objective Haematoxylin & Eosin (H&E) image-based marker for early-stage luminal/Her2-negative BReAst CancEr that term as the BRACE marker. The proposed derived from AI based at detailed level using power deep learning. ability validated two well-annotated cohorts (Cohort-A/Nottingham: n = 2122 Cohort-B/Coventry: 311) on luminal/HER2-negative patients treated with endocrine therapy long-term follow-up. able to stratify both distant metastasis free survival (p 0.001, C-index: 0.73) specific < 0.0001, 0.84) showing comparable prediction accuracy Nottingham Prognostic Index Magee scores, which are manual histopathological assessment, identify luminal may be likely benefit adjuvant chemotherapy.

Language: Английский

Citations

7
Clinical evaluation of deep learning-based risk profiling in breast cancer histopathology and comparison to an established multigene assay DOI Creative Commons
Yinxi Wang, Wenwen Sun, Emelié Karlsson

et al.

Breast Cancer Research and Treatment, Journal Year: 2024, Volume and Issue: 206(1), P. 163 - 175

Published: April 9, 2024

To evaluate the Stratipath Breast tool for image-based risk profiling and compare it with an established prognostic multigene assay in a real-world case series of estrogen receptor (ER)-positive human epidermal growth factor 2 (HER2)-negative early breast cancer patients categorized as intermediate based on classic clinicopathological variables eligible chemotherapy.

Language: Английский

Citations

2
Development and validation of a clinical breast cancer tool for accurate prediction of recurrence DOI Creative Commons

Asim Dhungana,

Augustin Vannier, Fangyuan Zhao

et al.

npj Breast Cancer, Journal Year: 2024, Volume and Issue: 10(1)

Published: June 15, 2024

Abstract 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 incorporated only small cohorts. Using a cohort patients from the National Cancer Database (NCDB, n = 53,346), we trained machine learning to predict low-risk (0-25) or high-risk (26-100) estrogen receptor (ER)/progesterone (PR)/Ki-67 status, ER/PR status alone, and no features. Models were externally validated on diverse 970 (median follow-up 55 months) accuracy prediction recurrence. Comparing area under receiver operating characteristic curve (AUROC) held-out set NCDB, incorporating (AUROC 0.78, 95% CI 0.77–0.80) ER/PR/Ki-67 0.81, 0.80–0.83) outperformed non-quantitative model 0.70, 0.68–0.72). These results preserved validation cohort, where 0.87, 0.81–0.93, p 0.009) 0.86, 0.80–0.92, 0.031) significantly 0.80, 0.73–0.87). high-sensitivity rule-out threshold, non-quantitative, identified 35%, 30% 43% as cohort. Of these patients, fewer than 3% had at 5 years. may help identify who can forgo genomic testing initiate endocrine therapy alone. An online calculator is provided further study.

Language: Английский

Citations

2
Developing a low-cost, open-source, locally manufactured workstation and computational pipeline for automated histopathology evaluation using deep learning DOI Creative Commons

Divya Choudhury,

James M. Dolezal, Emma Dyer

et al.

EBioMedicine, Journal Year: 2024, Volume and Issue: 107, P. 105276 - 105276

Published: Aug. 27, 2024

Deployment and access to state-of-the-art precision medicine technologies remains a fundamental challenge in providing equitable global cancer care low-resource settings. The expansion of digital pathology recent years its potential interface with diagnostic artificial intelligence algorithms provides an opportunity democratize personalized medicine. Current workstations, however, cost thousands hundreds dollars. As incidence rises many low- middle-income countries, the validation implementation low-cost automated tools will be crucial helping healthcare providers manage growing burden cancer.

Language: Английский

Citations

2
Nanomedicine for cancer patient‐centered care DOI Creative Commons
Carlo Sorrentino, Stefania L. Ciummo, Cristiano Fieni

et al.

MedComm, Journal Year: 2024, Volume and Issue: 5(11)

Published: Oct. 20, 2024

Abstract Cancer is a leading cause of morbidity and mortality worldwide, an increase in incidence estimated the next future, due to population aging, which requires development highly tolerable low‐toxicity cancer treatment strategies. The use nanotechnology tailor treatments according genetic immunophenotypic characteristics patient's tumor, allow its targeted release, can meet this need, improving efficacy minimizing side effects. Nanomedicine‐based approach for diagnosis rapidly evolving field. Several nanoformulations are currently clinical trials, some have been approved marketed. However, their large‐scale production still hindered by in‐depth debate involving ethics, intellectual property, safety health concerns, technical issues, costs. Here, we survey key approaches, with specific reference organ‐on chip technology, cutting‐edge tools, such as CRISPR/Cas9 genome editing, through nanosystems needs personalized diagnostics therapy patients. An update provided on nanopharmaceuticals marketed those undergoing trials. Finally, discuss emerging avenues field challenges be overcome transfer nano‐based precision oncology into daily life.

Language: Английский

Citations

2
HistoMIL: A Python package for training multiple instance learning models on histopathology slides DOI Creative Commons

Pan Shi,

Maria Secrier

iScience, Journal Year: 2023, Volume and Issue: 26(10), P. 108073 - 108073

Published: Sept. 27, 2023

Language: Английский

Citations

5