Long term trends of breast cancer incidence according to proliferation status

Klæstad, Elise; Opdahl, Signe; Raj, Sunil Xavier; Bofin, Anna Mary; Valla, Marit

doi:10.1186/s12885-022-10438-1

Cited by 3 publications

(1 citation statement)

References 61 publications

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“…Previous studies have demonstrated that Ki67 expression may be related to the prognosis of various tumors [ 17 , 18 ]. Patients with PCa with high Ki67 expression (> 10%) exhibit poor tumor differentiation and are at a higher risk of metastasis and recurrence.…”

Section: Discussionmentioning

confidence: 99%

Deep learning–based radiomic nomograms for predicting Ki67 expression in prostate cancer

et al. 2023

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Background To explore the value of a multiparametric magnetic resonance imaging (MRI)-based deep learning model for the preoperative prediction of Ki67 expression in prostate cancer (PCa). Materials The data of 229 patients with PCa from two centers were retrospectively analyzed and divided into training, internal validation, and external validation sets. Deep learning features were extracted and selected from each patient’s prostate multiparametric MRI (diffusion-weighted imaging, T2-weighted imaging, and contrast-enhanced T1-weighted imaging sequences) data to establish a deep radiomic signature and construct models for the preoperative prediction of Ki67 expression. Independent predictive risk factors were identified and incorporated into a clinical model, and the clinical and deep learning models were combined to obtain a joint model. The predictive performance of multiple deep-learning models was then evaluated. Results Seven prediction models were constructed: one clinical model, three deep learning models (the DLRS-Resnet, DLRS-Inception, and DLRS-Densenet models), and three joint models (the Nomogram-Resnet, Nomogram-Inception, and Nomogram-Densenet models). The areas under the curve (AUCs) of the clinical model in the testing, internal validation, and external validation sets were 0.794, 0.711, and 0.75, respectively. The AUCs of the deep models and joint models ranged from 0.939 to 0.993. The DeLong test revealed that the predictive performance of the deep learning models and the joint models was superior to that of the clinical model (p < 0.01). The predictive performance of the DLRS-Resnet model was inferior to that of the Nomogram-Resnet model (p < 0.01), whereas the predictive performance of the remaining deep learning models and joint models did not differ significantly. Conclusion The multiple easy-to-use deep learning–based models for predicting Ki67 expression in PCa developed in this study can help physicians obtain more detailed prognostic data before a patient undergoes surgery.

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Section: Discussionmentioning

confidence: 99%

Deep learning–based radiomic nomograms for predicting Ki67 expression in prostate cancer

et al. 2023

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Deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram for predicting Ki-67 expression in rectal cancer

Wu,

Wang,

et al. 2024

Abdom Radiol

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Current Perspectives on Nanoparticle-based Targeted Drug Delivery Approaches in Breast Cancer Treatment

Khan¹,

Pandey²,

Maqsood³

et al. 2023

EMIDDT

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Breast cancer is avertible yet one of the most widespread carcinomas globally. Though periodic screening and monitoring have resulted in reduced incidences, the malignancy claims increased death rates across the globe. Due to the non-specific and aggressive nature of available conventional cancer therapeutics, there is a crucial need for better treatment paradigms. Recent advancements in nanotechnology have aided in this by utilizing nanocarriers in targeted drug delivery approaches. Optimized nanoparticles have been used to enhance the circulation time and target the efficacy of conventional therapeutic drugs. Passive targeting comprises surface modulation to avoid drug elimination via a standard body defense system. Active targeting includes chemical interaction with various genes, receptors, and antigens overexpressed during cancer progression. Therefore, the present review recapitulates drug delivery approaches and nanoparticle-based targeting that can potentially overcome the limitations of conventional drug therapies.

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Long term trends of breast cancer incidence according to proliferation status

Cited by 3 publications

References 61 publications

Deep learning–based radiomic nomograms for predicting Ki67 expression in prostate cancer

Deep learning–based radiomic nomograms for predicting Ki67 expression in prostate cancer

Deep learning-based multi-parametric magnetic resonance imaging (mp-MRI) nomogram for predicting Ki-67 expression in rectal cancer

Current Perspectives on Nanoparticle-based Targeted Drug Delivery Approaches in Breast Cancer Treatment

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