Proving of a Mathematical Model of Cell Calculation Based on Apparent Diffusion Coefficient

Surov, Alexey; Garnov, Nikita

doi:10.1016/j.tranon.2017.08.001

Cited by 6 publications

(5 citation statements)

References 19 publications

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“…Typically, malignant tumors have lower values in comparison to benign lesions [ 28 ]. Moreover, it has been shown that ADC correlated inversely with cell density in several malignant and benign diseases, and, therefore, can be used as a surrogate cellularity marker [ 19 , 29 ]. Furthermore, ADC can also reflect proliferation activity of different lesions measured by Ki 67 index [ 20 ].…”

Section: Discussionmentioning

confidence: 99%

Apparent Diffusion Coefficient Is a Novel Imaging Biomarker of Myopathic Changes in Liver Cirrhosis

Surov

Paul

Schob

et al. 2018

JCM

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Diffusion weighted imaging can provide information regarding tissue composition and can quantitatively characterize different pathological changes by means of apparent diffusion coefficient (ADC). The study comprised of 114 patients with liver cirrhosis—22 women and 92 men with a mean age of 56.5 ± 9.0 years. In all patients, the Model for End Stage-Liver Disease (MELD) score was calculated. Furthermore, 12 healthy persons (5 women, 7 men), mean age, 42.1 ± 16.2 years, were investigated as a control group. In all cases, magnetic resonance imaging of the liver/trunk was performed using different 3T scanners and diffusion weighted images were obtained with a multi-shot SE-EPI sequence. In all cases, polygonal regions of interest were manually drawn on the ADC maps along the contours of the iliopsoas and paravertebral muscles. The comparison of ADC values in groups was performed by Mann-Whitney-U tests. The association between ADC and MELD score was calculated by Spearman’s rank correlation coefficient. ADC values of the skeletal musculature were statistically much higher in comparison to those in the control group: 1.85 ± 0.46 × 10−3 mm2 s−1 vs. 1.23 ± 0.12 × 10−3 mm2 s−1, p = 0.001. ADC values showed statistically significant correlation with the MELD score (r = 0.473, p = 0.0001). Furthermore, ADC values differed between the subgroups with different values of the MELD score. ADC values correlated slightly with lactate dehydrogenase (LDH) (r = 0.381, p = 0.0001) and tended to correlate with C-reactive protein (CRP) (r = 0.171, p = 0.07) and alanine aminotransferase (ALAT) (r = −0.167, p = 0.076). ADC can reflect muscle changes in liver cirrhosis and shows statistically significant correlation with the MELD score. Therefore, ADC can be used as an imaging biomarker of myopathic changes in liver cirrhosis.

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Section: Discussionmentioning

confidence: 99%

Apparent Diffusion Coefficient Is a Novel Imaging Biomarker of Myopathic Changes in Liver Cirrhosis

Surov

Paul

Schob

et al. 2018

JCM

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“…For example, in gliomas, the correlation coefficient was higher ( r =−0.66), whereas in lymphomas, it was −0.25 [4] . This seems to be related to the fact that ADC values are mainly influenced by cellularity, but also, other cellular structures such as [15] extracellular matrix can also cause diffusion restriction in tissues [6] , [13] , [14] .…”

Section: Discussionmentioning

confidence: 99%

“…As mentioned above, Atuewegu et al proposed two formulas for cellularity calculation based on ADC values (formula 1) and ADC and V e values (formula 2). Recently, results of cellularity calculation according to formula 1 were compared with histopathological data in different tumors [13] . It could be identified that the formula may be used for prediction of tumor cellularity in cerebral lymphomas and rectal cancer, but not in uterine cervical cancer, meningioma, and thyroid cancer [13] .…”

Section: Discussionmentioning

confidence: 99%

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Comparison of Two Mathematical Models of Cellularity Calculation

Meyer

Garnov

2018

Translational Oncology

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OBJECT: Nowadays, there is increasing evidence that functional magnetic resonance imaging (MRI) modalities, namely, diffusion-weighted imaging (DWI) and dynamic-contrast enhanced MRI (DCE MRI), can characterize tumor architecture like cellularity and vascularity. Previously, two formulas based on a logistic tumor growth model were proposed to predict tumor cellularity with DWI and DCE. The purpose of this study was to proof these formulas. METHODS: 16 patients with head and neck squamous cell carcinomas were included into the study. There were 2 women and 14 men with a mean age of 57.0 ± 7.5 years. In every case, tumor cellularity was calculated using the proposed formulas by Atuegwu et al. In every case, also tumor cell count was estimated on histopathological specimens as an average cell count per 2 to 5 high-power fields. RESULTS: There was no significant correlation between the calculated cellularity and histopathologically estimated cell count by using the formula based on apparent diffusion coefficient (ADC) values. A moderate positive correlation (r=0.515, P=.041) could be identified by using the formula including ADC and Ve values. CONCLUSIONS: The formula including ADC and Ve values is more sensitive to predict tumor cellularity than the formula including ADC values only.

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“…Our work is in contrast to one recent study that proposed a CNN-based system using multimodel MRI but does not include ADC maps [ 10 ]. ADC maps can be considered as an indication of cell density in tissues [ 19 ] and therefore can be used to search for cancer biomarkers, which usually involve high rates of cell proliferation. Similar to a recent study [ 20 ] that uses multiparametric MRI radiomics for prediction, we use a CNN-based structure instead of hand-crafted features—namely, we utilize a process of independent convolutions for ADC and DWI before fusing them using the dense fully connected layer.…”

Section: Introductionmentioning

confidence: 99%

Novel MRI-Based CAD System for Early Detection of Thyroid Cancer Using Multi-Input CNN

Ahmed

Khalifa

Khaled

et al. 2021

Sensors

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Early detection of thyroid nodules can greatly contribute to the prediction of cancer burdening and the steering of personalized management. We propose a novel multimodal MRI-based computer-aided diagnosis (CAD) system that differentiates malignant from benign thyroid nodules. The proposed CAD is based on a novel convolutional neural network (CNN)-based texture learning architecture. The main contribution of our system is three-fold. Firstly, our system is the first of its kind to combine T2-weighted MRI and apparent diffusion coefficient (ADC) maps using a CNN to model thyroid cancer. Secondly, it learns independent texture features for each input, giving it more advanced capabilities to simultaneously extract complex texture patterns from both modalities. Finally, the proposed system uses multiple channels for each input to combine multiple scans collected into the deep learning process using different values of the configurable diffusion gradient coefficient. Accordingly, the proposed system would enable the learning of more advanced radiomics with an additional advantage of visualizing the texture patterns after learning. We evaluated the proposed system using data collected from a cohort of 49 patients with pathologically proven thyroid nodules. The accuracy of the proposed system has also been compared against recent CNN models as well as multiple machine learning (ML) frameworks that use hand-crafted features. Our system achieved the highest performance among all compared methods with a diagnostic accuracy of 0.87, specificity of 0.97, and sensitivity of 0.69. The results suggest that texture features extracted using deep learning can contribute to the protocols of cancer diagnosis and treatment and can lead to the advancement of precision medicine.

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Proving of a Mathematical Model of Cell Calculation Based on Apparent Diffusion Coefficient

Cited by 6 publications

References 19 publications

Apparent Diffusion Coefficient Is a Novel Imaging Biomarker of Myopathic Changes in Liver Cirrhosis

Apparent Diffusion Coefficient Is a Novel Imaging Biomarker of Myopathic Changes in Liver Cirrhosis

Comparison of Two Mathematical Models of Cellularity Calculation

Novel MRI-Based CAD System for Early Detection of Thyroid Cancer Using Multi-Input CNN

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