Prediction of tumor size in patients with invasive ductal carcinoma using FT-IR spectroscopy combined with chemometrics: a preliminary study

Zhu, Zhaojie; Chen, Cheng; Yan, Ziwei; Chen, Fangfang; Yang, Bo; Huiting, Zhang; Han, Huijie

doi:10.1007/s00216-021-03258-y

Cited by 11 publications

(3 citation statements)

References 65 publications

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Section: Methodsmentioning

confidence: 99%

“…This paper uses receiver operating characteristic (ROC) curves to judge the performance of our constructed classification model and the area under the ROC curve (AUC) to verify the generalization ability of the model. 31 The confusion matrix consisted of four main aspects: true positive (TP), false positive (FP), true negative (TN), and false negative (FN). 32 Finally, the horizontal and vertical axes of the ROC curve are calculated, where the horizontal axis is the "false positive rate" (FPR), and the vertical axis is the "true case rate" (TPR), defined as Equations ( 25) and ( 26), respectively:…”

Section: Evaluation Indexmentioning

confidence: 99%

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CIABNet: Category imbalance attention block network for the classification of multi‐differentiated types of esophageal cancer

Chen

Cao

et al. 2022

Medical Physics

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Background: Esophageal cancer has become one of the important cancers that seriously threaten human life and health, and its incidence and mortality rate are still among the top malignant tumors. Histopathological image analysis is the gold standard for diagnosing different differentiation types of esophageal cancer. Purpose:The grading accuracy and interpretability of the auxiliary diagnostic model for esophageal cancer are seriously affected by small interclass differences, imbalanced data distribution, and poor model interpretability. Therefore, we focused on developing the category imbalance attention block network (CIABNet) model to try to solve the previous problems. Methods: First, the quantitative metrics and model visualization results are integrated to transfer knowledge from the source domain images to better identify the regions of interest (ROI) in the target domain of esophageal cancer. Second, in order to pay attention to the subtle interclass differences, we propose the concatenate fusion attention block, which can focus on the contextual local feature relationships and the changes of channel attention weights among different regions simultaneously. Third, we proposed a category imbalance attention module, which treats each esophageal cancer differentiation class fairly based on aggregating different intensity information at multiple scales and explores more representative regional features for each class, which effectively mitigates the negative impact of category imbalance. Finally, we use feature map visualization to focus on interpreting whether the ROIs are the same or similar between the model and pathologists, thus better improving the interpretability of the model. Results:The experimental results show that the CIABNet model outperforms other state-of -the-art models, which achieves the most advanced results in classifying the differentiation types of esophageal cancer with an average classification accuracy of 92.24%, an average precision of 93.52%, an average

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Section: Methodsmentioning

confidence: 99%

Section: Evaluation Indexmentioning

confidence: 99%

CIABNet: Category imbalance attention block network for the classification of multi‐differentiated types of esophageal cancer

Chen

Cao

et al. 2022

Medical Physics

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“…al. [4] used FT-IR spectroscopy combined with chemometrics to predict the size of tumor size in breast cancer. Three models were evaluated based on the pathological tumor size measured after surgery and included grid search support vector machine regression (GS-SVR), back propagation neural network optimized by genetic algorithm (GA-BP-ANN), and back propagation neural network optimized by particle swarm optimization (PSO-BP-ANN).…”

Section: Introductionmentioning

confidence: 99%

Tumor size estimation and 3D model viewing using Deep Learning

Venkataramana

Abhishek

et al. 2023

Preprint

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Cancer is the most deadly and dreaded disease ever encountered by mankind and tumor size plays a crucial role in determining the severity and treatment for the same. Therefore, it becomes imperative to estimate the dimensions of the associated tumor with paramount accuracy and precision so as to enable radiologists and doctors, in general, to effectively prescribe a treatment post-diagnosis. Current estimation approaches of tumor size involve the manual click and drag measurements by radiologists which are functional but prone to a lot of manual errors and redundancies. To improve the overall accuracy and efficiency of the process, the authors propose a Deep learning solution that uses DICOM scan images to determine the dimensions of the tumor. Furthermore, this solution provides a 3D representation of the tumor for clear perception and comprehension and also provides treatment suggestions that aid doctors throughout the treatment. The pipeline consists of two models namely, CNN model for detection performs with an accuracy of 97.6% and a ResUNet model to segment tumor out of the brain image with accuracy of 91.54%.

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Quantitative Analysis, Infrared

Cadet¹,

Guárdia

2022

Encyclopedia of Analytical Chemistry

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In this article, after reviewing the different mathematical methods used in quantification by IR, the main advantages and drawbacks and applications with or without previous sample treatment are presented. The bases of the use of quantitative IR in food analysis such as lipids (measurement of unsaturation degrees and lipids determination), carbohydrates, and proteins (secondary structures and quantitative analysis); original examples of the use of IR spectroscopy combined with enzymes; and modern applications in the clinical field are presented in detail, followed by examples of Fourier transform infrared (FTIR) analysis coupled with other analytical methods in different fields such as pharmaceuticals, petroleum, paints, and other industrial products and in the fields of health, environment, and trace compounds analysis in order to think about the limitations and perspectives of IR spectroscopy for quantitative analysis. It appears that the most significant developments in the field of quantitative analysis will probably come through the progress in chemometrics and instrumentation. More recently, approaches combining infrared spectroscopy and deep learning (DL) have shown great potential.

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Prediction of tumor size in patients with invasive ductal carcinoma using FT-IR spectroscopy combined with chemometrics: a preliminary study

Cited by 11 publications

References 65 publications

CIABNet: Category imbalance attention block network for the classification of multi‐differentiated types of esophageal cancer

CIABNet: Category imbalance attention block network for the classification of multi‐differentiated types of esophageal cancer

Tumor size estimation and 3D model viewing using Deep Learning

Quantitative Analysis, Infrared

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