tumor shape
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2021 ◽  
Vol 11 ◽  
Author(s):  
Huanhuan Wang ◽  
Ye Lu ◽  
Runkun Liu ◽  
Liang Wang ◽  
Qingguang Liu ◽  
...  

BackgroundMicrovascular invasion (MVI) is a significant predictive factor for early recurrence, metastasis, and poor prognosis of hepatocellular carcinoma. The aim of the present study is to identify preoperative factors for predicting MVI, in addition to develop and validate non-invasive nomogram for predicting MVI.MethodsA total of 381 patients with resected HCC were enrolled and divided into a training cohort (n = 267) and a validation cohort (n = 114). Serum VEGF-A level was examined by enzyme-linked immunosorbent assay (ELISA). Risk factors for MVI were assessed based on univariate and multivariate analyses in the training cohort. A nomogram incorporating independent risk predictors was established and validated.ResultThe serum VEGF-A levels in the MVI positive group (n = 198) and MVI negative group (n = 183) were 215.25 ± 105.68 pg/ml and 86.52 ± 62.45 pg/ml, respectively (P <0.05). Serum VEGF-A concentration ≥138.30 pg/ml was an independent risk factor of MVI (OR: 33.088; 95%CI: 12.871–85.057; P <0.001). Higher serum concentrations of AFP and VEGF-A, lower lymphocyte count, peritumoral enhancement, irregular tumor shape, and intratumoral artery were identified as significant predictors for MVI. The nomogram indicated excellent predictive performance with an AUROC of 0.948 (95% CI: 0.923–0.973) and 0.881 (95% CI: 0.820–0.942) in the training and validation cohorts, respectively. The nomogram showed a good model fit and calibration.ConclusionsHigher serum concentrations of AFP and VEGF-A, lower lymphocyte count, peritumoral enhancement, irregular tumor shape, and intratumoral artery are promising markers for MVI prediction in HCC. A reliable non-invasive nomogram which incorporated blood biomarkers and imaging risk factors was established and validated. The nomogram achieved desirable effectiveness in preoperatively predicting MVI in HCC patients.


2021 ◽  
Author(s):  
Amritpal Singh ◽  
Neeraj Kumar

Abstract In this work effects of tumor shape on magnetic nanoparticle hyperthermia (MNPH) are investigated and evaluated using four categories (spherical, oblate, prolate, and egg-shape) of tumor models having different morphologies. These tumors have equal volume; however, due to the differences in their shapes, they have different surface areas. The shape of tumors is quantified in terms of shape factor (ζ). Simulations for MNPH are done on the physical model constituting tumor tissue enclosed within the healthy tissue. Magnetic hyperthermia is applied (frequency 150 kHz, and magnetic field amplitude 20.5 kA/m) to all tumor models, for 1 hour, after injection of magnetic nanoparticles (MNPs) at the respective tumor centroids. The distribution of MNPs after injection is considered Gaussian. The governing model (Pennes' bioheat model) of heat transfer in biological media is solved with the finite volume-immersed boundary (FV-IB) method to simulate MNPH. Therapeutic effects are calculated using the Arrhenius tissue damage model, cumulative equivalent minutes at 43°C (CEM 43), and heterogeneity in temperature profiles of the tumors. Results show that the therapeutic effects due to MNPH depend significantly on the shape of a tumor. Tumors with higher shape factors receive less therapeutic effects in comparison to the tumors having lower shape factors. An empirical thermal damage model is also developed to assess the MNPH efficacy in real complex-shaped tumors.


2021 ◽  
Author(s):  
Mostafa Sefidgar ◽  
M Soltani ◽  
Kaamran Raahemifar ◽  
Hossein Bazmara ◽  
Seyed Mojtaba Mousavi Nayinian ◽  
...  

Background The computational methods provide condition for investigation related to the process of drug delivery, such as convection and diffusion of drug in extracellular matrices, drug extravasation from microvessels or to lymphatic vessels. The information of this process clarifies the mechanisms of drug delivery from the injection site to absorption by a solid tumor. In this study, an advanced numerical method is used to solve fluid flow and solute transport equations simultaneously to investigate the effect of tumor shape and size on drug delivery to solid tumor. Methods The advanced mathematical model used in our previous work is further developed by adding solute transport equation to the governing equations. After applying appropriate boundary and initial conditions on tumor and surrounding tissue geometry, the element-based finite volume method is used for solving governing equations of drug delivery in solid tumor. Also, the effects of size and shape of tumor and some of tissue transport parameters such as effective pressure and hydraulic conductivity on interstitial fluid flow and drug delivery are investigated. Results Sensitivity analysis shows that drug delivery in prolate shape is significantly better than other tumor shapes. Considering size effect, increasing tumor size decreases drug concentration in interstitial fluid. This study shows that dependency of drug concentration in interstitial fluid to osmotic and intravascular pressure is negligible. Conclusions This study shows that among diffusion and convection mechanisms of drug transport, diffusion is dominant in most different tumor shapes and sizes. In tumors in which the convection has considerable effect, the drug concentration is larger than that of other tumors at the same time post injection.


2021 ◽  
Author(s):  
Mostafa Sefidgar ◽  
M Soltani ◽  
Kaamran Raahemifar ◽  
Hossein Bazmara ◽  
Seyed Mojtaba Mousavi Nayinian ◽  
...  

Background The computational methods provide condition for investigation related to the process of drug delivery, such as convection and diffusion of drug in extracellular matrices, drug extravasation from microvessels or to lymphatic vessels. The information of this process clarifies the mechanisms of drug delivery from the injection site to absorption by a solid tumor. In this study, an advanced numerical method is used to solve fluid flow and solute transport equations simultaneously to investigate the effect of tumor shape and size on drug delivery to solid tumor. Methods The advanced mathematical model used in our previous work is further developed by adding solute transport equation to the governing equations. After applying appropriate boundary and initial conditions on tumor and surrounding tissue geometry, the element-based finite volume method is used for solving governing equations of drug delivery in solid tumor. Also, the effects of size and shape of tumor and some of tissue transport parameters such as effective pressure and hydraulic conductivity on interstitial fluid flow and drug delivery are investigated. Results Sensitivity analysis shows that drug delivery in prolate shape is significantly better than other tumor shapes. Considering size effect, increasing tumor size decreases drug concentration in interstitial fluid. This study shows that dependency of drug concentration in interstitial fluid to osmotic and intravascular pressure is negligible. Conclusions This study shows that among diffusion and convection mechanisms of drug transport, diffusion is dominant in most different tumor shapes and sizes. In tumors in which the convection has considerable effect, the drug concentration is larger than that of other tumors at the same time post injection.


PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0248541
Author(s):  
Olya Grove ◽  
Anders E. Berglund ◽  
Matthew B. Schabath ◽  
Hugo J. W. L. Aerts ◽  
Andre Dekker ◽  
...  

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244930
Author(s):  
Krzysztof Kaliszewski ◽  
Dorota Diakowska ◽  
Marta Rzeszutko ◽  
Łukasz Nowak ◽  
Michał Aporowicz ◽  
...  

Background Currently, less aggressive treatment or even active surveillance of papillary thyroid microcarcinoma (PTMC) is widely accepted and recommended as a therapeutic management option. However, there are some concerns about these approaches. We investigated whether there are any demographic, clinical and ultrasound characteristics of PTMC patients that are easy to obtain and clinically available before surgery to help clinicians make proper therapeutic decisions. Methods We performed a retrospective chart review of 5,021 patients with thyroid tumors surgically treated in one center in 2008–2018. Finally, 182 (3.62%) PTMC patients were selected (158 (86.8%) females and 24 (13.2%) males, mean age 48.8±15.4 years). We analyzed the disease-free survival (DFS) time of the PTMC patients according to demographic and histopathological parameters. Univariate and multivariate logistic regression analyses were used to assess the relationships of demographic, clinical and ultrasound characteristics with aggressive histopathological features. Results Age ≥55 years, hypoechogenicity, microcalcifications, irregular tumor shape, smooth margins and high vascularity significantly increased the risk for minimal extrathyroidal extension (minETE), lymph node metastasis (LNM), and capsular and vascular invasion (p<0.0001). Multivariate logistic regression analysis demonstrated a statistically significant risk of LNM (OR = 5.98, 95% CI: 2.32–15.38, p = 0.0002) and trends toward significantly higher rates of minETE and capsular and vascular invasion (OR = 2.24, 95% CI: 0.97–5.19, p = 0.056) in patients ≥55 years than in their younger counterparts. The DFS time was significantly shorter in patients ≥55 years (p = 0.015), patients with minETE and capsular and vascular invasion (p = 0.001 for all), patients with tumor size >5 mm (p = 0.021), and patients with LNM (p = 0.002). Conclusions The absence of microcalcifications, irregular tumor shape, blunt margins, hypoechogenicity and high vascularity in PTMC patients below 55 years and with tumor diameters below 5 mm may allow clinicians to select individuals with a low risk of local recurrence so that they can receive less aggressive management.


Digestion ◽  
2020 ◽  
pp. 1-8
Author(s):  
Noboru Yatagai ◽  
Hiroya Ueyama ◽  
Muneo Ikemura ◽  
Ryota Uchida ◽  
Hisanori Utsunomiya ◽  
...  

<b><i>Background:</i></b> Gastric adenocarcinoma of foveolar type (GA-FV) is a raspberry-shaped gastric cancer (RSGC) and garners much attention as <i>H. pylori</i> (<i>Hp</i>)-uninfected gastric cancer. However, the classification and clinicopathological and endoscopic features of RSGCs in <i>Hp</i>-uninfected patients are poorly defined. We designed a new histopathological classification of RSGC and compared them via endoscopic and clinicopathological characteristics. <b><i>Summary:</i></b> From 996 patients with early gastric cancers resected by endoscopy in our hospital, we studied 24 RSGC lesions from 21 (2.4%) <i>Hp</i>-uninfected patients. RSGCs were classified into 3 histological types as follows: GA-FV (<i>n</i> = 19), gastric adenocarcinoma of fundic gland type (GA-FG, <i>n</i> = 2), and gastric adenocarcinoma of fundic gland mucosa type (GA-FGM, <i>n</i> = 3). Most of the lesions were found at the greater curvature of the upper or middle third of the stomach. GA-FV lesions were homogeneously reddish and frequently accompanied with a whitish area around the tumor and an irregular microvascular (MV) pattern; these features were confirmed histopathologically by the presence of homogeneous neoplastic foveolar epithelium with foveolar hyperplasia around the tumors. GA-FG lesions might be heterogeneously reddish with a submucosal tumor shape and regular MV pattern; these were confirmed by the presence of covered or mixed nonneoplastic epithelium on deeper regions of tumors. GA-FGM lesions might be homogeneously reddish and occasionally had a submucosal tumor shape and irregular MV pattern; these were confirmed by the presence of homogeneous neoplastic foveolar epithelium on deeper regions of the tumors. <b><i>Key Messages:</i></b> RSGCs in <i>Hp</i>-uninfected patients are classified into 3 histopathological types. For accurate diagnosis of RSGCs, it may be necessary to fully understand endoscopic features of these lesions based on these histological characteristics and to take a precise biopsy.


2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii160-ii161
Author(s):  
Esra Sümer ◽  
Ece Tek ◽  
Meriç Şengöz ◽  
M Necmettiin Pamir ◽  
Alp Dinçer ◽  
...  

Abstract Gamma knife radiosurgery (GKRS) delivers an unevenly distributed radiation dose to a tumor, with a sharp falloff outside the target. Although the dose inhomogeneity within a tumor is strongly influenced by its shape, routine GKRS dose planning does not account for it. We hypothesized that shape irregularity measures were correlated with treatment planning indices, and might provide insight during treatment planning. The aims of this study were to quantify the shape irregularity measures in vestibular schwannomas, estimate their correlations with core radiosurgical planning measures, and define the most predictive shape feature for dose effectiveness. METHODS: Four dose plan indices, which were the selectivity index (SI), gradient index (GI), efficiency index (EI), and Paddick’s conformity index (PCI) were estimated from the GKRS plans of 234 vestibular schwannomas. All dose plans were prepared using Gamma Plan 10.0 and above and all treatments were delivered using a perfexion/ICON platform. Three-dimensional (3D) tumor models were rendered using 3D Slicer Software from segmented T1-weighted MR images. Sixteen irregularity measures were calculated for each tumor using Radiomics in MATLAB. Spearman correlation coefficients (r) were computed to find associations of the dose plan indices with the irregularity descriptors. The most predictive shape feature for dose efficiency was identified using the least absolute shrinkage and selection operator (Lasso). RESULTS: The shape irregularity measures were negatively correlated with SI, EI, and PCI, and positively correlated with GI. Volumetric index of sphericity (VioS) had the highest correlations with SI (r = 0.63, p= 3.27E-23), GI (r= -0.58, p= 1.10E-19), EI (r = 0.69, p= 0.00), and PCI(r= 0.68, p = 6.73E-28), and Lasso feature selection identified VioS as the most important feature for predicting all dose plan indices. CONCLUSION: VioS provides a numerical quantification of tumor shape irregularity, and it is highly correlated with the GKRS dose planning indices. *indicates co-senior authors


2020 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Nicholas A. George-Jones ◽  
Kai Wang ◽  
Jing Wang ◽  
Jacob B. Hunter

2020 ◽  
Author(s):  
Ivan S. Klyuzhin ◽  
Yixi Xu ◽  
Anthony Ortiz ◽  
Juan M. Lavista Ferres ◽  
Ghassan Hamarneh ◽  
...  

Purpose: To test the ability of convolutional neural networks (CNNs) to effectively capture the intensity, shape, and texture properties of tumors as defined by standardized radiomic features. Methods: Standard 2D and 3D CNN architectures with an increasing number of convolutional layers (up to 9) were trained to predict the values of 16 standardized radiomic features from synthetic images of tumors, and tested. In addition, several ImageNet-pretrained state-of-the-art networks were tested. The synthetic images replicated the quality of real PET images. A total of 4000 images were used for training, 500 for validation, and 500 for testing. Results: Radiomic features quantifying tumor size and intensity were predicted with high accuracy, while shape irregularity features had very high prediction errors and generalized poorly between training and test sets. For example, mean normalized prediction error of tumor diameter (mean intensity) with a 5-layer 2D CNN was 4.23 ± 0.25 (1.88 ± 0.07), while the error for tumor sphericity was 15.64 ± 0.93. Similarly-high error values were found with other shape irregularity and heterogeneity features, both with standard and state-of-the-art networks. Conclusions: Standard CNN architectures and ImageNet-pretrained advanced networks have a significantly lower capacity to capture tumor shape and heterogeneity properties compared to other features. Our findings imply that CNNs trained end-to-end for clinical outcome prediction and other tasks may under-utilize tumor shape and texture information. We hypothesize, that to improve CNN performance, these radiomic features can be computed explicitly and added as auxiliary variables to the dense layers in the networks, or as additional input channels.


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