The impact of reporting tumor size in breast core needle biopsies on tumor stage: A retrospective review of five years of experience at a single institution

Alghamdi, Sarah; Oneto, Sabrina; Tuzzolo, Anthony; Mejia, Odille; Febres‐Aldana, Christopher A.; Poppiti, Robert; Vincentelli, Cristina

doi:10.1016/j.anndiagpath.2018.10.002

Cited by 1 publication

(1 citation statement)

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“…The objective of a biopsy is to identify the type and stage of the tumor and evaluate its potential invasiveness. This procedure is conducted by a medical professional, such as a surgeon, who carefully selects the site for tissue collection, often guided by imaging techniques like ultrasound or CT scans [2]. Once the sample is obtained, it is then subjected to laboratory analysis, including histological examination and molecular testing.…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Feasibility of Thermographic Images for Predicting Breast Tumor Stage Using DCNN

Khomsi,

El Fezazi,

Elouerghi

et al. 2024

IAPGOS

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Early-stage and advanced breast cancer represent distinct disease processes. Thus, identifying the stage of tumor is a crucial procedure for optimizing treatment efficiency. Breast thermography has demonstrated significant advancements in non-invasive tumor detection. However, the accurate determination of tumor stage based on temperature distribution represents a challenging task, primarily due to the scarcity of thermal images labeled with the stage of tumor. This work proposes a transfer learning approach based on Deep Convolutional Neural Network (DCNN) with thermal images for predicting breast tumor stage. Various tumor stage scenarios including early and advanced tumors are embedded in a 3D breast model using the Finite Element Method (FEM) available on COMSOL Multiphysics software. This allows the generation of the thermal image dataset for training the DCNN model. A detailed investigation of the hyperparameters tuning process has been conducted to select the optimal predictive model. Thus, various evaluation metrics, including accuracy, sensitivity, and specificity, are computed using the confusion matrix. The results demonstrate the DCNN model's ability to accurately predict breast tumor stage from thermographic images, with an accuracy of 98.2%, a sensitivity of 98.8%, and a specificity of 97.7%. This study indicates the promising potential of thermographic images in enhancing deep learning algorithms for the non-invasive prediction of breast tumor stage.

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Section: Introductionmentioning

confidence: 99%