Machine learning based local recurrence prediction in colorectal cancer using polarized light imaging

Majumdar, Anamitra; Lad, Jigar; Tumanova, Kseniia; Serra, Stefano; Quereshy, Fayez; Khorasani, Mohammadali; Vitkin, Alex

doi:10.1117/1.jbo.29.5.052915

Cited by 2 publications

(1 citation statement)

References 58 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Colorectal cancer remains a non-negligible cause of cancer-related death ( 16 - 18 ). Surgical therapy is less effective in advanced colorectal cancer patients than those early patients ( 19 ).…”

Section: Discussionmentioning

confidence: 99%

Extracellular vesicles isolated from curcumin-medium weakened RKO cell proliferation and migration

Xu,

Liu

2024

Transl Cancer Res

View full text Add to dashboard Cite

Background Curcumin (Cur) is a natural phytochemical that is expected to become an indispensable drug for the treatment of colorectal cancer. A comprehensive understanding of the anti-tumor mechanism of Cur will provide a better reference for its clinical application. This study aimed to examine the effects of extracellular vesicles (EVs) isolated from Cur-medium on RKO colorectal cancer cell proliferation, apoptosis, and migration. Methods RKO cells were cultured in various concentrations of Cur-medium, and the EVs were isolated from the Cur-medium. The EVs were identified by transmission electron microscopy and western blotting. The effects of the EVs on RKO cell proliferation, apoptosis, and migration were analyzed, as was the expression of proliferating cell nuclear antigen (PCNA), Bax, vimentin, and E-cadherin. The expression of nuclear factor κB (NF-κB) p65 in the EVs was also detected. Results Our results showed that the EVs isolated from the Cur-medium weakened RKO cell proliferation and migration but had no effect on cell apoptosis. Cur suppressed the expression of NF-κB p65 in the EVs. Overall, this study revealed that Cur exerts anti-tumor effects by suppressing NF-κB p65 in EVs to weaken RKO cell proliferation and migration. Conclusions In conclusion, the packaging of Cur into EVs is expected to become an indispensable treatment of colorectal cancer in the future.

show abstract

Section: Discussionmentioning

confidence: 99%

Extracellular vesicles isolated from curcumin-medium weakened RKO cell proliferation and migration

Xu,

Liu

2024

Transl Cancer Res

View full text Add to dashboard Cite

show abstract

Machine learning for automated classification of lung collagen in a urethane-induced lung injury mouse model

Alnafisah,

Ranjan,

Sahu

et al. 2024

Biomed. Opt. Express

View full text Add to dashboard Cite

Dysregulation of lung tissue collagen level plays a vital role in understanding how lung diseases progress. However, traditional scoring methods rely on manual histopathological examination introducing subjectivity and inconsistency into the assessment process. These methods are further hampered by inter-observer variability, lack of quantification, and their time-consuming nature. To mitigate these drawbacks, we propose a machine learning-driven framework for automated scoring of lung collagen content. Our study begins with the collection of a lung slide image dataset from adult female mice using second harmonic generation (SHG) microscopy. In our proposed approach, first, we manually extracted features based on the 46 statistical parameters of fibrillar collagen. Subsequently, we pre-processed the images and utilized a pre-trained VGG16 model to uncover hidden features from pre-processed images. We then combined both image and statistical features to train various machine learning and deep neural network models for classification tasks. We employed advanced unsupervised techniques like K-means, principal component analysis (PCA), t-distributed stochastic neighbour embedding (t-SNE), and uniform manifold approximation and projection (UMAP) to conduct thorough image analysis for lung collagen content. Also, the evaluation of the trained models using the collagen data includes both binary and multi-label classification to predict lung cancer in a urethane-induced mouse model. Experimental validation of our proposed approach demonstrates promising results. We obtained an average accuracy of 83% and an area under the receiver operating characteristic curve (ROC AUC) values of 0.96 through the use of a support vector machine (SVM) model for binary categorization tasks. For multi-label classification tasks, to quantify the structural alteration of collagen, we attained an average accuracy of 73% and ROC AUC values of 1.0, 0.38, 0.95, and 0.86 for control, baseline, treatment_1, and treatment_2 groups, respectively. Our findings provide significant potential for enhancing diagnostic accuracy, understanding disease mechanisms, and improving clinical practice using machine learning and deep learning models.

show abstract

Machine learning based local recurrence prediction in colorectal cancer using polarized light imaging

Cited by 2 publications

References 58 publications

Extracellular vesicles isolated from curcumin-medium weakened RKO cell proliferation and migration

Extracellular vesicles isolated from curcumin-medium weakened RKO cell proliferation and migration

Machine learning for automated classification of lung collagen in a urethane-induced lung injury mouse model

Contact Info

Product

Resources

About