Extraction of Radiomic Features from Breast DCE-MRI Responds to Pathological Changes in Patients During Neoadjuvant Chemotherapy Treatment

Moyya, Priscilla Dinkar; Mythili, A.; Ramaniharan, Anandh Kilpattu

doi:10.1109/ist48021.2019.9010068

Cited by 4 publications

(4 citation statements)

References 17 publications

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“…Interestingly, preliminary results from [32,33] reported that the radiomic features alone can differentiate the treatment response before and after the treatment. While a couple of studies have been conducted in NACT treatment response, they have been used either first follow-up NACT data or/and the end of the NACT data.…”

Section: Discussionmentioning

confidence: 99%

“…The scale is chosen as a multiple of 2 and the orientation is within the range of (0,2π) degrees [24]. Hence five scales λ = (2,4,8,16,32) and θ = (0°,30°,60°,90°,120°,135°,150°) are considered for this study. However, the tumor areas of different subjects vary with respect to the severity of the breast cancer, thus a smaller scale value is required to extract more detailed textures from the images.…”

Section: Gabor Filter Designmentioning

confidence: 99%

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Quantitative Analysis of Breast Cancer NACT Response on DCE-MRI Using Gabor Filter Derived Radiomic Features

Moyya

Mythili

2022

Int. J. Onl. Eng.

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In this work, an attempt has been made to quantify the treatment response due to Neoadjuvant Chemotherapy (NACT) on the publicly available QIN-Breast of TCIA database (N = 25) using Gabor filter derived radiomic features. The Gabor filter bank is constructed using 5 different scales and 7 different orientations. Different radiomic features were extracted from Gabor filtered Dynamic Contrast Enhanced Magnetic Resonance images (DCE-MRI) of patients having 3 different visits (Visit 1: before, Visit 2: after 1st cycle, and Visit 3: the last cycle of NACT). The extracted radiomic features were analyzed statistically and Area Under Receiver Operating Characteristic (AUROC) has been calculated. Results show that the Gabor derived radiomic features could differentiate the pathological differences among all three visits. Energy has shown a significant difference between all the three orientations particularly between Visits 2 & 3. However, Entropy from λ=2 and θ=300 between Visit 2 & 3, Skewness from λ=2 and θ=1200 between Visit 1 & 3 could differentiate the treatment response with high statistical significance of p=0.006 and 0.001 respectively. From the ROC analysis, the better predictors were Short Run Emphasis (SRE), Short Zone Emphasis (SZE), and Energy between Visit 1 & 3 by achieving an AUROC of 76.38%, 75.16%, and 71.10% respectively. Further, the results suggest that the radiomic features are capable of quantitatively compare the breast NACT prognosis that varies across multi-oriented Gabor filters.

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Section: Discussionmentioning

confidence: 99%

Section: Gabor Filter Designmentioning

confidence: 99%

Quantitative Analysis of Breast Cancer NACT Response on DCE-MRI Using Gabor Filter Derived Radiomic Features

Moyya

Mythili

2022

Int. J. Onl. Eng.

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“…Add multiscale The msSE residual structure of the channel recalibration model is shown in Figure 3 . Convolutional neural networks using multiscale features are often used in tasks such as target detection and recognition [ 15 , 19 – 21 ] and image semantic segmentation [ 22 – 24 ]. Using feature information at multiple scales can make the final result more accurate.…”

Section: Proposed Algorithm Descriptionmentioning

confidence: 99%

“…Computational Intelligence and Neuroscience recognition [15,[19][20][21] and image semantic segmentation [22][23][24]. Using feature information at multiple scales can make the final result more accurate.…”

Section: Proposed Algorithm Descriptionmentioning

confidence: 99%

Breast Cancer Pathological Image Classification Based on the Multiscale CNN Squeeze Model

Alqahtani

Mandawkar

Sharma

et al. 2022

Computational Intelligence and Neuroscience

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The use of an automatic histopathological image identification system is essential for expediting diagnoses and lowering mistake rates. Although it is of enormous clinical importance, computerized breast cancer multiclassification using histological pictures has rarely been investigated. A deep learning-based classification strategy is suggested to solve the challenge of automated categorization of breast cancer pathology pictures. The attention model that acts on the feature channel is the channel refinement model. The learned channel weight may be used to reduce superfluous features when implementing the feature channel. To increase classification accuracy, calibration is necessary. To increase the accuracy of channel recalibration findings, a multiscale channel recalibration model is provided, and the msSE-ResNet convolutional neural network is built. The multiscale properties flow through the network’s highest pooling layer. The channel weights obtained at different scales are delivered into line fusion and used as input to the next channel recalibration model, which may improve the results of channel recalibration. The experimental findings reveal that the spatial recalibration model fares poorly on the job of classifying breast cancer pathology pictures when applied to the semantic segmentation of brain MRI images. The public BreakHis dataset is used to conduct the experiment. The network performs benign/malignant breast pathology picture classiﬁcation collected at various magnifications with a classification accuracy of 88.87 percent, according to experimental data. The diseased images are also more resilient. Experiments on pathological pictures at various magnifications show that msSE-ResNet34 is capable of performing well when used to classify pathological images at various magnifications.

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Progesterone Receptor Status Analysis in Breast Cancer Patients using DCE- MR Images and Gabor Derived Anisotropy Index

Moyya

Mythili

Ramaniharan

2022

2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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Extraction of Radiomic Features from Breast DCE-MRI Responds to Pathological Changes in Patients During Neoadjuvant Chemotherapy Treatment

Cited by 4 publications

References 17 publications

Quantitative Analysis of Breast Cancer NACT Response on DCE-MRI Using Gabor Filter Derived Radiomic Features

Quantitative Analysis of Breast Cancer NACT Response on DCE-MRI Using Gabor Filter Derived Radiomic Features

Breast Cancer Pathological Image Classification Based on the Multiscale CNN Squeeze Model

Progesterone Receptor Status Analysis in Breast Cancer Patients using DCE- MR Images and Gabor Derived Anisotropy Index

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