Gray scale histogram analysis of solid breast lesions with ultrasonography: can lesion echogenicity ratio be used to differentiate the malignancy?

The predictive value of the thyroid nodule benign and malignant based on the ultrasound nodule-to-muscle gray-scale ratio

OBJECTIVE

To investigate the efficacy of the ultrasonic nodule to muscle gray scale ratio as a predictive tool for distinguishing between benign and malignant thyroid nodules.

METHODS

A retrospective study was undertaken at the First People's Hospital of Hangzhou, affiliated with the Zhejiang University School of Medicine, analyzing ultrasound and pathological data of patients with thyroid nodules between May 2020 and December 2022. The study extracted ultrasound features of nodules and employed univariate and multivariate logistic regression analyses to identify independent risk factors for malignant tumors in the nodules. Subsequently, a predictive model for distinguishing benign and malignant thyroid nodules was developed.

RESULTS

A total of 466 patients were included in this retrospective study, of which 275 cases were malignant tumors. Univariate and multivariate logistic regression analyses showed that the nodular-muscle gray-scale ratio, nodule diameter, margin status, aspect ratio, and calcification were closely related to thyroid malignant tumors. The area under the curve (AUC) of training group was 0.832, with a sensitivity, specificity, and accuracy of 85.5%, 67.4%, and 76.6%, respectively. The AUC of the external validation group was 0.819, with a sensitivity, specificity, and accuracy of 76.4%, 74.5%, and 75.7%, respectively. The calibration and decision curves showed that the model had good diagnostic value.

CONCLUSION

The research findings indicate that ratio is significantly associated with the malignant nature of thyroid nodules. The application of a line chart model based on these parameters exhibits a high level of predictive performance.

Value of Histogram of Gray-Scale Ultrasound Image in Differential Diagnosis of Small Triple Negative Breast Invasive Ductal Carcinoma and Fibroadenoma

Objective

To investigate the value of gray-scale ultrasound (US) image histogram in the differential diagnosis between small (≤2.00 cm), oval, or round triple negative breast invasive ductal carcinoma (TN-IDC) and fibroadenoma (FA).

Methods

Fifty-five cases of triple negative breast invasive ductal carcinoma (TN-IDC group) and 57 cases of breast fibroadenoma (FA group) confirmed by pathology in Hubei cancer hospital from September 2017 to September 2021 were analyzed retrospectively. The gray-scale US images were analyzed by histogram analysis method, from which some parameters (including mean, variance, skewness, kurtosis and 1st, 10th, 50th, 90th and 99th percentile) can be obtained. Intraclass correlation coefficient (ICC) was used to evaluate the inter observer reliability of histogram parameters. Histogram parameters between the TN-IDC and FA groups were compared using independent Student’s t-test or Mann-Whitney U-test, respectively. In addition, the receiver operating characteristic (ROC) curve analysis was used for the significant parameters to calculate the differential diagnosis efficiency.

Results

All the histogram parameters showed excellent inter-reader consistency, with the ICC values ranged from 0.883 to 0.999. The mean value, 1st, 10th, 50th, 90th and 99th percentiles of TN-IDC group were significantly lower than those of FA group (P < 0.05). The area under ROC curve (AUC) values of mean and n percentiles were from 0.807 to 0.848. However, there were no significant differences in variance, skewness and kurtosis between the two groups (P > 0.05).

Conclusion

Histogram analysis of gray-scale US images can well distinguish small, oval, or round TN-IDC from FA.

Ultrasound grayscale ratio: a reliable parameter for differentiating between papillary thyroid microcarcinoma and micronodular goiter

BACKGROUND

The present study aimed to quantify and differentiate the echo levels of papillary thyroid microcarcinomas (PTMCs) and micronodular goiters (MNGs) using the ultrasound grayscale ratio (UGSR) and to investigate the repeatability of UGSR.

METHODS

The ultrasound (US) data of 241 patients with 265 PTMCs and 141 patients with 168 MNGs confirmed by surgery and pathology were retrospectively analyzed. All patients had received outpatient ultrasonic examination and preoperative ultrasonic positioning. The RADinfo radiograph reading system was used to measure the grayscales of PTMC, MNG, and thyroid tissues at the same gain level, and the UGSR values of the PTMC, MNG, and thyroid tissue were calculated. The patients were divided into outpatient examination, preoperative positioning, and mean value groups, and the receiver operating characteristic (ROC) curves were calculated to obtain the optimal UGSR threshold to distinguish PTMC from MNG. The interclass correlation coefficient (ICC) was used to assess the consistency of UGSR measured in three groups.

RESULTS

The UGSR values of the PTMC and MNG were 0.56 ± 0.14 and 0.80 ± 0.19 (t = 5.84, P < 0.001) in the outpatient examination group, 0.55 ± 0.14 and 0.80 ± 0.19 (t = 18.74, P < 0.001) in the preoperative positioning group, and 0.56 ± 0.12 and 0.80 ± 0.18 (t = 16.49, P < 0.001) in the mean value group. The areas under the ROC curves in the three groups were 0.860, 0.856, and 0.875, respectively. When the UGSR values for the outpatient examination, preoperative positioning, and mean value groups were 0.649, 0.646, and 0.657, respectively, each group obtained its largest Youden index. A reliable UGSR value was obtained between the outpatient examination and preoperative positioning groups (ICC = 0.79, P = 0.68).

CONCLUSION

UGSR is a simple and repeatable method to distinguish PTMC from MNG, and hence, can be widely applicable.

The Contribution of a Solid Breast Mass Gray-Scale Histographic Analysis in Ascertaining a Benign-Malignant Differentiation

Objective:

The aim of this study was to determine the efficiency of a fat-lesion histographic analysis (FLHA) to make a benign-malignant differentiation, for patients with a breast mass. In addition, it was important to examine the relationship between FLHA rates and Breast Imaging–Reporting and Data System (BI-RADS) scoring.

Materials and Methods:

This was a retrospective study conducted by examining the breast ultrasonograms (BUS) and histopathologic data of 76 women, having at least one solid breast mass. The patients were grouped according to their histopathologic diagnoses and BUS BI-RADS scores. The recorded digital gray-scale images were transferred to a workstation to quantitatively measure tissue echogenicity. The breast masses and adjacent adipose tissue were evaluated using the ImageJ analysis program, and gray-scale histographic analysis values were generated. The FLHA rate was determined by dividing the fat tissue histographic value by the mass-lesion histographic value. Statistical analysis was performed using this value as well as the patients’ histopathologic data and BUS BI-RADS score.

Results:

A complementary effect was noted using FLHA rates with the BUS BI-RADS criteria, and a statistically significant difference was detected between benign and malignant histopathology groups ( P < .001). Similarly, the malignant histopathologic diagnosis with BI-RADS 4 and benign histopathologic diagnosis with BI-RADS 4 groups were related ( P < .001). The correlation between BI-RADS criteria and FLHA rates demonstrated a significant difference between BI-RADS 3 and BI-RADS 5 ( P < .001), and BI-RADS 4 and BI-RADS 5 for FLHA rates ( P = .002).

Conclusion:

It was determined that using the FLHA rate was a complement to the BUS BI-RADS criteria. In this cohort, there was a statistically significant difference in predicting possible malignancy in all BI-RADS classes.

The role of quantitative gray-scale ultrasound histogram in the differential diagnosis of infected and non-infected hydronephrosis

BACKGROUND

The early detection of infected hydronephrosis is critical before lithotripsy. A feasible and noninvasive diagnostic method is of considerable clinical attention.

OBJECTIVES

This retrospective study was performed to find some quantitative evaluation parameters of B-mode Gray-scale ultrasound histogram analysis that might assist the early diagnosis of infected hydronephrosis and test their diagnostic efficacy.

MATERIALS AND METHODS

The ultrasound images and clinical data of 245 patients with hydronephrosis were retrospectively analyzed. Image J software was applied to obtain the gray-scale maps and the analysis results of the signal strength. The difference in the data between the infected and non-infected groups and the diagnostic value of the parameters were calculated.

RESULTS

In this retrospective study, 70 patients with infected hydronephrosis and 175 patients with non-infected hydronephrosis were enrolled. The echogenicity of internal effusion and the echogenicity ratio of infected cases were significantly higher than those of non-infected cases (p < 0.05). The cutoff values were 23.82 (AUC = 0.859) of echogenicity of internal effusion, while 0.27 (AUC = 0.832) of echogenicity ratio.

CONCLUSION

The quantitative evaluation of gray-scale ultrasound histogram is an objective and reliable method in differentiating infected from non-infected hydronephrosis.

Neural Network Pattern Recognition of Ultrasound Image Gray Scale Intensity Histograms of Breast Lesions to Differentiate Between Benign and Malignant Lesions: Analytical Study

Background

Ultrasound-based radiomic features to differentiate between benign and malignant breast lesions with the help of machine learning is currently being researched. The mean echogenicity ratio has been used for the diagnosis of malignant breast lesions. However, gray scale intensity histogram values as a single radiomic feature for the detection of malignant breast lesions using machine learning algorithms have not been explored yet.

Objective

This study aims to assess the utility of a simple convolutional neural network in classifying benign and malignant breast lesions using gray scale intensity values of the lesion.

Methods

An open-access online data set of 200 ultrasonogram breast lesions were collected, and regions of interest were drawn over the lesions. The gray scale intensity values of the lesions were extracted. An input file containing the values and an output file consisting of the breast lesions’ diagnoses were created. The convolutional neural network was trained using the files and tested on the whole data set.

Results

The trained convolutional neural network had an accuracy of 94.5% and a precision of 94%. The sensitivity and specificity were 94.9% and 94.1%, respectively.

Conclusions

Simple neural networks, which are cheap and easy to use, can be applied to diagnose malignant breast lesions with gray scale intensity values obtained from ultrasonogram images in low-resource settings with minimal personnel.

A Brief Review on Breast Carcinoma and Deliberation on Current Non Invasive Imaging Techniques for Detection

BACKGROUND

Breast carcinoma is a life threatening disease that accounts for 25.1% of all carcinoma among women worldwide. Early detection of the disease enhances the chance for survival.

DISCUSSION

This paper presents comprehensive report on breast carcinoma disease and its modalities available for detection and diagnosis, as it delves into the screening and detection modalities with special focus placed on the non-invasive techniques and its recent advancement work done, as well as a proposal on a novel method for the application of early breast carcinoma detection.

CONCLUSION

This paper aims to serve as a foundation guidance for the reader to attain bird's eye understanding on breast carcinoma disease and its current non-invasive modalities.

Diagnosis of thyroid nodules for ultrasonographic characteristics indicative of malignancy using random forest

Background

Various combinations of ultrasonographic (US) characteristics are increasingly utilized to classify thyroid nodules. But they lack theories, and heavily depend on radiologists’ experience, and cannot correctly classify thyroid nodules. Hence, our main purpose of this manuscript is to select the US characteristics significantly associated with malignancy and to develop an efficient scoring system for facilitating ultrasonic clinicians to correctly identify thyroid malignancy.

Methods

A logistic regression (LR) model is utilized to identify the potential thyroid malignancy, and the least absolute shrinkage and selection operator (LASSO) method is adopted to simultaneously select US characteristics significantly associated with malignancy and estimate parameters in LR model. Based on the selected US characteristics, we calculate the probability for each of thyroid nodules via random forest (RF) and extreme learning machine (ELM), and develop a scoring system to classify thyroid nodules. For comparison, we also consider eight state-of-the-art methods such as support vector machine (SVM), neural network (NET), etc. The area under the receiver operating characteristic curve (AUC) is employed to measure the accuracy of various classifiers.

Results

The US characteristics: nodule size, AP/T≥1, solid component, micro-calcifications, hackly border, hypoechogenicity, presence of halo, unclear border, irregular margin, and central vascularity are selected as the significant predictors associated with thyroid malignancy via the LASSO LR (LLR). Using the developed scoring system, thyroid nodules are classified into the following four categories: benign, low suspicion, intermediate suspicion, and high suspicion, whose rates of malignancy correctly identified for RF (ELM) method on the testing dataset are 0.0% (4.3%), 14.3% (50.0%), 58.1% (59.1%) and 96.1% (97.7%), respectively.

Conclusion

LLR together with RF performs better than other methods in identifying malignancy, especially for abnormal nodules, in terms of risk scores. The developed scoring system can well predict the risk of malignancy and guide medical doctors to make management decisions for reducing the number of unnecessary biopsies for benign nodules.

Gray Scale Histogram Analysis of Carpal Tunnel Syndrome with Ultrasonography

PURPOSE

In this study, we aim to evaluate the diagnostic value of echogenicity ratio with histogram analyses.

MATERIALS & METHODS

This retrospective study was performed on 22 patients with 44 hands. The patients had clinical presentations consistent with CTS, in at one hand. Quantitative ultrasound scanning and image capture were completed using a diagnostic sonography machine. For gray scale histogram analysis, image J software was used.

RESULTS

Mean flexor tendons histogram analysis echogenicity/Mean median nerve histogram analysis echogenicity was significantly high for 1,7 cutoff value.

CONCLUSION

Mean FTE/MNE ratio may be a useful sonographic parameter for CTS.

A Practical Approach to Quantitative Grayscale Ultrasound Analysis of Hepatic Steatosis in Pediatric Patients Using a Picture Archiving and Communication System-Based Tool

OBJECTIVES

To evaluate the efficacy of a picture archiving and communication system (PACS)-based ultrasound (US) quantification technique for diagnosis of hepatic steatosis in a pediatric population.

METHODS

Abdominal US images of 49 pediatric patients (≤18 years) with histopathologically proven diagnoses of hepatic steatosis (n = 17), nonsteatotic liver disease (n = 19), and a normal liver (n = 13) were retrospectively reviewed. Patient demographics, the fibrosis stage, and the steatosis grade were obtained from the database. Quantitative grayscale measurements of the echo intensity level of the liver and kidneys were performed on the US images using the PACS measuring tool. The hepatorenal ratio was obtained by dividing mean liver by mean kidney values. The heterogeneity index for the liver was calculated by dividing the liver standard deviation by mean liver values. Hepatorenal ratio and heterogeneity index values of the 3 groups were correlated with pathologic results and compared by a 1-way analysis of variance. A receiver operating characteristic curve analysis was performed, and cutoff values were determined.

RESULTS

The hepatorenal ratio of the hepatic steatosis group was significantly greater than those of the control and nonsteatotic liver disease groups (P < .001). The heterogeneity index of the hepatic steatosis group was significantly greater than that of the control group (P = .046). For a hepatorenal ratio cutoff value of 1.5, 88.2% sensitivity, 91.4% specificity, 88.3% positive predictive value, and 94.1% negative predictive value were obtained for predicting hepatic steatosis.

CONCLUSIONS

PACS-based quantitative grayscale US quantification is a safe, accurate, and easily applicable objective method for the diagnosis of hepatic steatosis in children. A hepatorenal ratio of greater than 1.5 can be used as a conservative parameter, permitting increased confidence in discriminating hepatic steatosis from other conditions.

Differential diagnosis value of the ultrasound gray scale ratio for papillary thyroid microcarcinomas and micronodular goiters

Background

To investigate the differential diagnosis value of the ultrasound gray scale ratio (UGSR) for papillary thyroid microcarcinomas (PTMCs) and micronodular goiters (MNGs).

Methods

A retrospective analysis was performed using ultrasound images from 521 PTMC patients (561 PTMC lesions) and 405 MNG patients (515 MNG lesions). All cases were surgically and histologically confirmed. Gray scale values of the thyroid lesions and the surrounding normal thyroid tissue were measured. The thyroid lesion to normal thyroid tissue (UGSR) was calculated. Statistical analysis was performed with Mann-Whitney test. Receiver operating characteristic curve determined the optimal UGSR threshold for differentiating PTMCs and MNGs.

Results

In 561 PTMCs, the mean UGSR was 0.54 (SD: 0.16; range: 0.24-1.26). In 515 MNGs, the mean UGSR was 0.87 (SD: 0.22; range: 0.34-2.06), with significant difference between values of PTMCs and MNGs (P<0.001). The UGSR area under the curve to differentiate PTMCs and MNGs was 0.895. When the UGSR decreased, the UGSR to PTMC sensitivity decreased and the specificity increased. When the UGSR was chosen to be 0.99, 0.72, 0.63 or 0.34, the sensitivity was 98.4% and 87.0%, 73.8% and 5.9% respectively, and specificity was 25.1% and 80.4%, 90.1% and 100.0% respectively. When the UGSR was 0.72, the Youden index maximum was 0.674.

Conclusions

The UGSR allows potential differentiation PTMCs and MNGs.

Ultrasonic histogram assessment of early response to concurrent chemo-radiotherapy in patients with locally advanced cervical cancer: a feasibility study

PURPOSE

To monitor early response for locally advanced cervical cancers undergoing concurrent chemo-radiotherapy (CCRT) by ultrasonic histogram.

METHODS

B-mode ultrasound examinations were performed at 4 time points in thirty-four patients during CCRT. Six ultrasonic histogram parameters were used to assess the echogenicity, homogeneity and heterogeneity of tumors.

RESULTS

I_peak increased rapidly since the first week after therapy initiation, whereas W_low, W_high and A_high changed significantly at the second week. The average ultrasonic histogram progressively moved toward the right and converted into more symmetrical shape.

CONCLUSION

Ultrasonic histogram could be served as a potential marker to monitor early response during CCRT.

Evaluation of Underlying Lymphocytic Thyroiditis With Histogram Analysis Using Grayscale Ultrasound Images

OBJECTIVES

The purpose of this study was to evaluate diagnostic performance of histogram analysis using grayscale ultrasound (US) images in the diagnosis of lymphocytic thyroiditis.

METHODS

Three radiologists reviewed a total of 505 US images and classified the images according to the presence/existence of lymphocytic thyroiditis. After 2 months, each reviewer repeated the process with the same 505 images in a randomly mixed order. The intraobserver and interobserver variability was analyzed with a generalized κ value. Four histogram parameters (mean value, standard deviation, skewness, and kurtosis) were obtained, and an index was calculated from principal component analysis. Diagnostic performances were compared.

RESULTS

Of 505 patients, 125 (24.8%) had lymphocytic thyroiditis, and 380 (75.2%) had normal thyroid parenchyma on pathologic analysis. The κ value for intraobserver variance ranged from -0.002 to 0.781, and the overall κ values for interobserver variance were 0.570 and 0.214 in the first and second tests, respectively. The sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the 3 reviewers versus the principal component analysis index were 28.0% to 83.2%, 43.7% to 82.6%, 53.5% to 79.0%, 24.6% to 56.2%, and 75.2% to 88.9% versus 58.4%, 72.4%, 68.9%, 41.0%, and 84.1%.

CONCLUSIONS

Histogram analysis of grayscale US images provided confirmable and quantitative information about lymphocytic thyroiditis and was comparable with performers' assessments in diagnostic performance.

Correlation of renal histopathology with renal echogenicity in dogs and cats: an ex-vivo quantitative study

Background

Increased cortical or cortical and medullary echogenicity is one of the most common signs of chronic or acute kidney disease in dogs and cats. Subjective evaluation of the echogenicity is reported to be unreliable. Patient and technical-related factors affect in-vivo quantitative evaluation of the echogenicity of parenchymal organs. The aim of the present study is to investigate the relationship between histopathology and ex-vivo renal cortical echogenicity in dogs and cats devoid of any patient and technical-related biases.

Results

Kidney samples were collected from 68 dog and 32 cat cadavers donated by the owners to the Veterinary Teaching Hospital of the University of Padua and standardized ultrasonographic images of each sample were collected. The echogenicity of the renal cortex was quantitatively assessed by means of mean gray value (MGV), and then histopathological analysis was performed. Statistical analysis to evaluate the influence of histological lesions on MGV was performed. The differentiation efficiency of MGV to detect pathological changes in the kidneys was calculated for dogs and cats. Statistical analysis revealed that only glomerulosclerosis was an independent determinant of echogenicity in dogs whereas interstitial nephritis, interstitial necrosis and fibrosis were independent determinants of echogenicity in cats. The global influence of histological lesions on renal echogenicity was higher in cats (23%) than in dogs (12%).

Conclusions

Different histopathological lesions influence the echogenicity of the kidneys in dogs and cats. Moreover, MGV is a poor test for distinguishing between normal and pathological kidneys in the dog with a sensitivity of 58.3% and specificity of 59.8%. Instead, it seems to perform globally better in the cat, resulting in a fair test, with a sensitivity of 80.6% and a specificity of 56%.