scholarly journals Diagnostic Value of CT- and MRI-Based Texture Analysis and Imaging Findings for Grading Cartilaginous Tumors in Long Bones

2021 ◽  
Vol 11 ◽  
Author(s):  
Xue-Ying Deng ◽  
Hai-Yan Chen ◽  
Jie-Ni Yu ◽  
Xiu-Liang Zhu ◽  
Jie-Yu Chen ◽  
...  
Keyword(s):  
Texture Analysis ◽  
Feature Model ◽  
Imaging Feature ◽  
Low Grade ◽  
Long Bones ◽  
Imaging Features ◽  
High Grade ◽  
Accuracy Rate ◽  
Predictive Values ◽  
Cartilaginous Tumors

ObjectiveTo confirm the diagnostic performance of computed tomography (CT)-based texture analysis (CTTA) and magnetic resonance imaging (MRI)-based texture analysis for grading cartilaginous tumors in long bones and to compare these findings to radiological features.Materials and MethodsTwenty-nine patients with enchondromas, 20 with low-grade chondrosarcomas and 16 with high-grade chondrosarcomas were included retrospectively. Clinical and radiological information and 9 histogram features extracted from CT, T1WI, and T2WI were evaluated. Binary logistic regression analysis was performed to determine predictive factors for grading cartilaginous tumors and to establish diagnostic models. Another 26 patients were included to validate each model. Receiver operating characteristic (ROC) curves were generated, and accuracy rate, sensitivity, specificity and positive/negative predictive values (PPV/NPV) were calculated.ResultsOn imaging, endosteal scalloping, cortical destruction and calcification shape were predictive for grading cartilaginous tumors. For texture analysis, variance, mean, perc.01%, perc.10%, perc.99% and kurtosis were extracted after multivariate analysis. To differentiate benign cartilaginous tumors from low-grade chondrosarcomas, the imaging features model reached the highest accuracy rate (83.7%) and AUC (0.841), with a sensitivity of 75% and specificity of 93.1%. The CTTA feature model best distinguished low-grade and high-grade chondrosarcomas, with accuracies of 71.9%, and 80% in the training and validation groups, respectively; T1-TA and T2-TA could not distinguish them well. We found that the imaging feature model best differentiated benign and malignant cartilaginous tumors, with an accuracy rate of 89.2%, followed by the T1-TA feature model (80.4%).ConclusionsThe imaging feature model and CTTA- or MRI-based texture analysis have the potential to differentiate cartilaginous tumors in long bones by grade. MRI-based texture analysis failed to grade chondrosarcomas.

scholarly journals Should LSIL with ASC-H (LSIL-H) in cervical smears be an independent category? A study on SurePath™ specimens with review of literature

CytoJournal ◽  
2007 ◽  
Vol 4 ◽  
pp. 7 ◽  
Author(s):  
Vinod B Shidham ◽  
Nidhi Kumar ◽  
Raj Narayan ◽  
Gregory L Brotzman
Keyword(s):  
Current Evidence ◽  
Low Grade ◽  
High Grade ◽  
Squamous Intraepithelial Lesion ◽  
Predictive Values ◽  
Hpv Dna ◽  
Cervical Smears ◽  
Management Algorithm ◽  
The Status ◽  
Intraepithelial Lesion

Background Cervical smears exhibiting unequivocal features of ‘low grade squamous intraepithelial lesion’ (LSIL) are occasionally also admixed with some cells suspicious for, but not diagnostic of, ‘high grade squamous intraepithelial lesion’ (HSIL). Only a few studies, mostly reported as abstracts, have evaluated this concurrence. In this study, we evaluate the current evidence that favors a distinct category for “LSIL, cannot exclude HSIL” (LSIL-H), and suggest a management algorithm based on combinations of current ASCCP guidelines for related interpretations. Methods We studied SurePath™ preparations of cervical specimens from various institutions during one year period. Cytohisto correlation was performed in cases with cervical biopsies submitted to our institution. The status of HPV DNA testing was also noted in some LSIL-H cases with biopsy results. Results Out of 77,979 cases 1,970 interpreted as LSIL (1,523), LSIL-H (146), ‘atypical squamous cells, cannot exclude HSIL’ (ASC-H) (109), and HSIL (192) were selected. Concurrent biopsy results were available in 40% (Total 792 cases: 557 LSIL, 88 LSIL-H, 38 ASCH, and 109 HSIL). Biopsy results were grouped into A. negative for dysplasia (ND), B. low grade (HPV, CIN1, CIN1 with HPV), and C. high grade (CIN 2 and above). The positive predictive values for various biopsy results in relation to initial cytopathologic interpretation were: a. LSIL: (557 cases): ND 32% (179), low grade- 58% (323), high grade- 10% (55); b. LSIL-H: (88 cases): ND 24% (21), low grade- 43% (38), high grade- 33% (29); c. ASCH: (38 cases): ND 32% (12), low grade- 37% (14), high grade- 31% (12); d. HSIL (109 cases): ND 5% (6), low grade 26% (28), high grade 69% (75). The patterns of cervical biopsy results in cases reported as LSIL-H were compared with that observed in cases with LSIL, ASC-H, and HSIL. 94% (32 of 34) of LSIL-H were positive for high risk (HR) HPV, 1 was negative for HR HPV but positive for low risk (LR), and 1 LSIL-H was negative for HR and LR both. Conclusion LSIL-H overlapped with LSIL and ASC-H, but was distinct from HSIL. A management algorithm comparable to ASC-H and HSIL appears to be appropriate in LSIL-H cases.

scholarly journals Brainstem glioma: Prediction of histopathologic grade based on conventional MR imaging

2017 ◽  
Vol 31 (1) ◽  
pp. 10-17 ◽  
Author(s):  
Yashar Moharamzad ◽  
Morteza Sanei Taheri ◽  
Farhad Niaghi ◽  
Elham Shobeiri
Keyword(s):  
Logistic Regression ◽  
Mr Imaging ◽  
Contrast Enhancement ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Analysis ◽  
Low Grade ◽  
Imaging Features ◽  
High Grade ◽  
Post Contrast ◽  
Mri Features

Objective The objective of this article is to investigate the association between specific MR imaging findings and histopathologic grading (low-grade vs. high-grade) of brainstem gliomas (BSGs). Methods Sixty-two males and 34 females (mean (standard deviation, SD) age of 24.61 (17.20) years, range = 3 to 70 years) with histologically diagnosed BSG underwent conventional 1.5 T MR imaging, which included T1-weighted (T1W), T2W, and post-contrast T1W sequences. There were 39 children (mean age of 9.38 years) and 57 adults (mean age of 35 years). A binary logistic regression analysis was used to explore associations between MRI features and histopathological grade of the BSG. Results Binary logistic regression revealed that necrosis (adjusted odds ratio (OR) = 16.07; 95% confidence interval (CI) = 3.20 to 80.52; p = 0.001) and inhomogeneous contrast enhancement (adjusted OR = 8.04; 95% CI = 1.73 to 37.41; p = 0.008) as significant predictors of high-grade BSG. The equation (Nagelkerke R2 = 0.575) is Logit ( p high-grade BSG) = (2.77 × necrosis) + (2.08 × heterogeneous contrast enhancement) – 3.13. Sensitivity and specificity values were respectively 66.7% and 96.0% for necrosis and 85.7% and 65.9% for inhomogeneous contrast-enhancing lesions. In the pediatric age group, only inhomogeneous contrast enhancement (adjusted OR = 40; 95% CI = 3.95 to 445.73; p = 0.002) was a significant predictor for high-grade BSG. Conclusion Conventional MR imaging features such as necrosis and inhomogeneous contrast enhancement in adults and heterogeneous contrast enhancement in children suggest high-grade BSG.

scholarly journals Imaging features of adenosquamous carcinoma of the breast – A rare variant of metaplastic breast carcinoma

2021 ◽  
pp. 20210108
Author(s):  
Tiffany Marian Sae-Kho ◽  
Asha Bhatt ◽  
Malvika H. Solanki ◽  
Elizabeth B. Jeans ◽  
Kimberly S. Corbin ◽  
...  
Keyword(s):  
Adenosquamous Carcinoma ◽  
Ductal Carcinoma ◽  
Case Series ◽  
Screening Mammography ◽  
Metaplastic Carcinoma ◽  
Whole Body ◽  
Low Grade ◽  
Imaging Features ◽  
High Grade ◽  
Carcinoma Of The Breast

Adenosquamous carcinoma of the breast is a rare subtype of metaplastic carcinoma, which accounts for <1% of invasive breast malignancy. Metaplastic carcinoma is usually high grade and aggressive with typically reported benign imaging features when compared to invasive ductal carcinoma. However, the adenosquamous variant is a subtype with a more favorable prognosis. Within the literature, there is limited imaging description with case studies focusing on metaplastic carcinoma. Herein, we report seven cases of the adenosquamous subtype describing the imaging findings with correlation to clinical history and pathology. The majority of patients (n = 6) presented with palpable breast masses. One patient was identified through screening mammography. Mammographically (n = 6), tumors appeared as irregular masses. Sonographically (n = 7), tumors appeared as irregular masses ranging from solid to mixed solid/cystic masses. On MRI (n = 1), one tumor appeared as an irregular rim enhancing mass. FDG PET/CT (n = 2) and whole-body bone scan (n = 1) were also available for review. The majority of tumors were low-grade (n = 6) with only one high-grade tumor. This case series of seven patients demonstrated predominantly suspicious imaging features despite the majority being low-grade tumors.

Utility of first order MRI-Texture analysis parameters in the prediction of histologic grade and muscle invasion in urinary bladder cancer: a preliminary study

2021 ◽  
pp. 20201114
Author(s):  
Abdul Razik ◽  
Chandan J Das ◽  
Raju Sharma ◽  
Sundeep Malla ◽  
Sanjay Sharma ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Urinary Bladder ◽  
Texture Analysis ◽  
Urinary Bladder Cancer ◽  
Histologic Grade ◽  
Low Grade ◽  
High Grade ◽  
First Order ◽  
Muscle Invasive ◽  
Muscle Invasion

Objective: To explore the utility of first-order MRI-texture analysis (TA) parameters in predicting histologic grade and muscle invasion in urinary bladder cancer (UBC). Methods: After ethical clearance, 40 patients with UBC, who were imaged on a 3.0-Tesla scanner, were retrospectively included. Using the TexRADTM platform, two readers placed freehand ROI on the sections demonstrating the largest dimension of the tumor, evaluating only one tumor per patient. Interobserver reproducibility was assessed using the intraclass correlation coefficient (ICC). Mann–Whitney U test and ROC curve analysis were used to identify statistical significance and select parameters with high class separation capacity (AUC >0.8), respectively. Pearson’s test was used to identify redundancy in the results. Results: All texture parameters showed excellent ICC. The best parameters in differentiating high and low-grade tumors were mean/ mean of positive pixels (MPP) at SSF 0 (AUC: 0.897) and kurtosis at SSF 5 (AUC: 0.828) on the ADC images. In differentiating muscle invasive from non-muscle invasive tumors, mean/ MPP at SSF 0 on the ADC images showed AUC >0.8; however, this finding resulted from the confounding effect of high-grade histology on the ADC values of muscle invasive tumors. Conclusion: MRI-TA generated few parameters which were reproducible and useful in predicting histologic grade. No independent parameters predicted muscle invasion. Advances in knowledge: There is lacuna in the literature concerning the role of MRI-TA in the prediction of histologic grade and muscle invasion in UBC. Our study generated a few first-order parameters which were useful in predicting high-grade histology.

scholarly journals Point-of-care digital cytology with artificial intelligence for cervical cancer screening at a peripheral clinic in Kenya

2020 ◽  
Author(s):  
Oscar Holmstrom ◽  
Nina Linder ◽  
Harrison Kaingu ◽  
Ngali Mbuuko ◽  
Jumaa Mbete ◽  
...  
Keyword(s):  
Cervical Cancer ◽  
Pap Smear ◽  
Point Of Care ◽  
Screening Method ◽  
Machine Learning Algorithms ◽  
Learning System ◽  
Pap Smears ◽  
Low Grade ◽  
High Grade ◽  
Predictive Values

Cervical cancer is highly preventable but remains a common and deadly cancer in areas without screening programmes. Pap smear analysis is the most commonly used screening method but is labour-intensive, subjective and requires access to medical experts. We developed a diagnostic system in which microscopy samples are digitized at the point-of-care (POC) and analysed by a cloud-based deep-learning system (DLS) and evaluated the system for the detection of cervical cell atypia in Pap smears at a peripheral clinic in Kenya. A total of 740 conventional Pap smears were collected, digitized with a portable slide scanner and uploaded over mobile networks to a cloud server for training and validation of the system. In total, 16,133 manually-annotated image regions where used for training of the DLS. The DLS achieved a high average sensitivity (97.85%; 95% confidence interval (CI) 83.95-99.75%) and area under the curve (AUCs) (0.95) for the detection of cervical-cellular atypia, compared to the pathologist assessment of digital and physical slides. Specificity was higher for high-grade atypia (95.9%; 95% CI 94.9-97.6%) than for low-grade atypia (84.2%; 95% CI 79.9-87.9%). Negative predictive values were high (99.3-100%), and no samples classified as high grade by manual sample analysis had false-negative assessments by the DLS. The study shows that advanced digital microscopy diagnostics supported by machine learning algorithms is implementable in rural, resource-constrained areas, and can achieve a diagnostic accuracy close to the level of highly trained experts.

scholarly journals Utility of diffusion-weighted and contrast-enhanced magnetic resonance imaging in diagnosing and differentiating between high- and low-grade uterine endometrial stromal sarcoma

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Yen-Ling Huang ◽  
Shir-Hwa Ueng ◽  
Kueian Chen ◽  
Yu-Ting Huang ◽  
Hsin-Ying Lu ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Diagnostic Accuracy ◽  
Endometrial Stromal Sarcoma ◽  
Low Grade ◽  
Imaging Features ◽  
High Grade ◽  
Mr Images ◽  
Stromal Sarcoma ◽  
Diffusion Weighted ◽  
Contrast Enhanced

Abstract Background Endometrial stromal sarcoma (ESS) is a rare uterine malignancy that features different prognoses for its high- and low-grade subtypes. We investigated the diagnostic accuracy of magnetic resonance (MR) imaging in diagnosing and differentiating between high- and low-grade ESS. Methods We retrospectively reviewed the preoperative pelvic MR images of consecutive patients who received histologically confirmed diagnoses of high-grade ESS (n = 11) and low-grade ESS (n = 9) and T2-hyperintense leiomyoma (n = 16). Two radiologists independently evaluated imaging features in T1-, T2-, and diffusion-weighted and contrast-enhanced MR images. Statistical analysis included Mann-Whitney tests and Fisher’s exact test, with sensitivity, specificity and diagnostic accuracy of imaging features. Results High-grade ESS was associated with significantly more extensive necrosis and hemorrhage and distinct feather-like enhancement compared with low-grade ESS (P < .05 for all). The feather-like enhancement pattern yielded a diagnostic accuracy of 95%, sensitivity of 91%, and specificity of 100% in differentiating high-grade from low-grade ESS. This imaging characteristic was significantly superior to the necrosis (80%, P = .033) or hemorrhage (75%, P = .007). Both high- and low-grade ESS demonstrated T2 hypointense bands, marginal nodules, intratumoral nodules, and worm-like intra-myometrial nodules, and their tumor apparent diffusion coefficient (ADC) values were significantly lower than those of T2-hyperintense leiomyomas (P < .001). Conclusions Diffusion-weighted MR imaging is useful in diagnosing ESS against T2-hyperintense leiomyomas, and contrast enhancement aids in further differentiating between high- and low-grade ESS.

scholarly journals Three-dimensional CT texture analysis of anatomic liver segments can differentiate between low-grade and high-grade fibrosis

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Bettina Katalin Budai ◽  
Ambrus Tóth ◽  
Petra Borsos ◽  
Veronica Grace Frank ◽  
Sonaz Shariati ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Liver Disease ◽  
Texture Analysis ◽  
Hierarchical Cluster Analysis ◽  
Three Dimensional ◽  
Hierarchical Cluster ◽  
Low Grade ◽  
High Grade ◽  
Prediction Rate ◽  
Ct Texture Analysis

Abstract Background CT texture analysis (CTTA) has been successfully used to assess tissue heterogeneity in multiple diseases. The purpose of this work is to demonstrate the value of three-dimensional CTTA in the evaluation of diffuse liver disease. We aimed to develop CTTA based prediction models, which can be used for staging of fibrosis in different anatomic liver segments irrespective of variations in scanning parameters. Methods We retrospectively collected CT scans of thirty-two chronic hepatitis patients with liver fibrosis. The CT examinations were performed on either a 16- or a 64-slice scanner. Altogether 354 anatomic liver segments were manually highlighted on portal venous phase images, and 1117 three-dimensional texture parameters were calculated from each segment. The segments were divided between groups of low-grade and high-grade fibrosis using shear-wave elastography. The highly-correlated features (Pearson r > 0.95) were filtered out, and the remaining 453 features were normalized and used in a classification with k-means and hierarchical cluster analysis. The segments were split between the train and test sets in equal proportion (analysis I) or based on the scanner type (analysis II) into 64-slice train 16-slice validation cohorts for machine learning classification, and a subset of highly prognostic features was selected with recursive feature elimination. Results A classification with k-means and hierarchical cluster analysis divided segments into four main clusters. The average CT density was significantly higher in cluster-4 (110 HU ± SD = 10.1HU) compared to the other clusters (c1: 96.1 HU ± SD = 11.3HU; p < 0.0001; c2: 90.8 HU ± SD = 16.8HU; p < 0.0001; c3: 93.1 HU ± SD = 17.5HU; p < 0.0001); but there was no difference in liver stiffness or scanner type among the clusters. The optimized random forest classifier was able to distinguish between low-grade and high-grade fibrosis with excellent cross-validated accuracy in both the first and second analysis (AUC = 0.90, CI = 0.85–0.95 vs. AUC = 0.88, CI = 0.84–0.91). The final support vector machine model achieved an excellent prediction rate in the second analysis (AUC = 0.91, CI = 0.88–0.94) and an acceptable prediction rate in the first analysis (AUC = 0.76, CI = 0.67–0.84). Conclusions In conclusion, CTTA-based models can be successfully applied to differentiate high-grade from low-grade fibrosis irrespective of the imaging platform. Thus, CTTA may be useful in the non-invasive prognostication of patients with chronic liver disease.

Cartilaginous Tumors of the Larynx

1995 ◽  
Vol 104 (3) ◽  
pp. 251-255 ◽  
Author(s):  
Kenneth O. Devaney ◽  
Alfio Ferlito ◽  
Carl E. Silver
Keyword(s):  
Biopsy Specimen ◽  
Daily Practice ◽  
Low Grade ◽  
High Grade ◽  
Maximum Dimension ◽  
Cytologic Atypia ◽  
Clear Cut ◽  
Cartilaginous Tumor ◽  
Cartilaginous Tumors ◽  
Cartilaginous Lesion

Cartilaginous tumors of the larynx, while rare, will on occasion be encountered by the otolaryngologist in routine daily practice. True laryngeal chondromas are exceedingly rare, and as a consequence, a putative diagnosis of chondroma should be viewed with suspicion. On pathologic examination, laryngeal chondromas usually prove to be small lesions (less than 2 cm in maximum dimension) and may arise in children or adults. Laryngeal chondrosarcomas, by contrast, usually prove to be larger lesions (exceeding 3 cm in greatest dimension) and are typically found in adults. While high-grade chondrosarcomas are readily identifiable on light microscopic study, the distinction between a chondroma and a low-grade chondrosarcoma is often not so clear-cut. Some low-grade chondrosarcomas may show a slight increase in both cellularity and cytologic atypia when compared with chondromas, but the two patterns often overlap. When faced with a limited biopsy specimen of a laryngeal cartilaginous lesion in which neither increased cellularity nor recognizable cytologic atypia is found, a diagnosis of “cartilaginous tumor without obvious evidence of malignancy — further classification dependent on examination of the lesion in its entirety,” or words to this effect, is recommended.

scholarly journals The Role of Imagistics in the Diagnosis of Chondrosarcomas

2018 ◽  
Vol 1 (Supplement) ◽  
pp. 22
Author(s):  
A.N. Marinescu ◽  
A.I. Nicula ◽  
M.A. Bratu ◽  
G. Iana
Keyword(s):  
Biopsy Specimen ◽  
Bone Tumors ◽  
Malignant Tumors ◽  
Low Grade ◽  
High Grade ◽  
Histological Subtypes ◽  
Size Type ◽  
Cartilaginous Tumors

Abstract Chondrosarcomas are malignant cartilaginous tumors, with many histological subtypes and three grades based on cellularity. They are the second most common primary malignant tumors of the bone. From the histopathologically proved clinical cases, we are reviewing the imaging semiology - size, type of calcifications, cortical breach, endosteal scalloping, permeative or moth eaten bone appearance, and revealing other differentiation elements like location, age, and pain. A special point is the frequent difficulty in distinguishing between enchondromas and low grade conventional chondrosarcomas - as the lesions are very similar both histologically and radiographically. The radiologist should point the area to aim on the biopsy - at areas that may harbor foci of high-grade tumor, such as the ones of endosteal scalloping, soft-tissue components, or diffusely enhancing areas with minimal mineralization. It is known that with cartilaginous tumors, histopathologic examination of the biopsy specimen alone does not permit an accurate classification of the tumor. We emphasized the role of imaging in positive and differential diagnosis, management, and therapy of these bone tumors.

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