Quantification of intranodal vascularity by computer pixel-counting method enhances the accuracy of ultrasound in distinguishing metastatic and tuberculous cervical lymph nodes

Cheng, Sha; Ahuja, Anil T.; Ying, Michael

doi:10.21037/qims.2019.10.02

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…Recent applications of artificial intelligence in medical ultrasound images have already included a variety of specific tasks ranging from image segmentation to biometric measurement. For example, previous studies successfully developed methods for automated breast lesion segmentation and computer-aided quantification of intranodal vascularity on ultrasound (5,6). Artificial intelligence works well with radiomics which extracts image information that cannot be obtained by human like textural data and wavelet features.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the performance of traditional machine learning algorithms, convolutional neural network and AutoML Vision in ultrasound breast lesions classification: a comparative study

Wan¹,

Wong²,

Ip³

et al. 2021

Quant Imaging Med Surg

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Background: In recent years, there was an increasing popularity in applying artificial intelligence in the medical field from computer-aided diagnosis (CAD) to patient prognosis prediction. Given the fact that not all healthcare professionals have the required expertise to develop a CAD system, the aim of this study was to investigate the feasibility of using AutoML Vision, a highly automatic machine learning model, for future clinical applications by comparing AutoML Vision with some commonly used CAD algorithms in the differentiation of benign and malignant breast lesions on ultrasound. Methods: A total of 895 breast ultrasound images were obtained from the two online open-access ultrasound breast images datasets. Traditional machine learning models (comprising of seven commonly used CAD algorithms) with three content-based radiomic features (Hu Moments, Color Histogram, Haralick Texture) extracted, and a convolutional neural network (CNN) model were built using python language.AutoML Vision was trained in Google Cloud Platform. Sensitivity, specificity, F1 score and average precision (AUCPR) were used to evaluate the diagnostic performance of the models. Cochran's Q test was used to evaluate the statistical significance between all studied models and McNemar test was used as the post-hoc test to perform pairwise comparisons. The proposed AutoML model was also compared with the current related studies that involve similar medical imaging modalities in characterizing benign or malignant breast lesions.

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Section: Introductionmentioning

confidence: 99%

Evaluation of the performance of traditional machine learning algorithms, convolutional neural network and AutoML Vision in ultrasound breast lesions classification: a comparative study

Wan¹,

Wong²,

Ip³

et al. 2021

Quant Imaging Med Surg

Self Cite

View full text Add to dashboard Cite

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“…According to the American Thyroid Association (ATA) statement on preoperative imaging for thyroid cancer surgery, ultrasound (US) stands as the key imaging modality for the assessment of thyroid cancer (9); however, US can only detect 20-31% of patients with central cervical LNM (10)(11)(12)(13), whereas the rate of detection for lateral cervical LNM is 70-93.8% (11,12). US is also greatly affected by the operators' experience level and manipulation (14)(15)(16). For PTC patients, the spatial resolution and contrast resolution of traditional CT scans are not high enough for cervical LNs to be accurately detected, as these LNs cannot be easily distinguished from accompanying blood vessels, especially when they measure <0.5 cm (17,18).…”

Section: Introductionmentioning

confidence: 99%

Dual-energy computed tomography could reliably differentiate metastatic from non-metastatic lymph nodes of less than 0.5 cm in patients with papillary thyroid carcinoma

Zou

Zheng

et al. 2021

Quant Imaging Med Surg

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Background: Dual-energy computed tomography (DECT) has been widely applied to detect lymph node (LN) and lymph node metastasis (LNM) in various cancers, including papillary thyroid carcinoma (PTC). This study aimed to quantitatively evaluate metastatic cervical lymph nodes (LNs) <0.5 cm in patients with PTC using DECT, which has not been done in previous studies. Methods: Preoperative DECT data of patients with pathologically confirmed PTC were retrospectively collected and analyzed between May 2016 and June 2018. A total of 359 LNs from 52 patients were included. Diameter, iodine concentration (IC), normalized iodine concentration (NIC), and the slope of the energy spectrum curve (λ HU ) of LNs in the arterial and the venous phases were compared between metastatic and nonmetastatic LNs. The optimal parameters were obtained from the receiver operating characteristic (ROC) curves. The generalized estimation equation (GEE) model was used to evaluate independent diagnostic factors for LNM. Results: A total of 139 metastatic and 220 non-metastatic LNs were analyzed. There were statistical differences of quantitative parameters between the two groups (P value 0.000-0.007). The optimal parameter for diagnosing LNM was IC in the arterial phase, and its area under the curve (AUC), sensitivity, and specificity were 0.775, 71.9%, and 73.6%, respectively. When the three parameters of diameter, IC in the arterial phase, and NIC in the venous phase were combined, the prediction efficiency was better, and the AUC was 0.819. The GEE results showed that LNs located in level VIa [odds ratio (OR) 2.030, 95% confidence interval (CI): 1.134-3.634, P=0.017], VIb (OR 2.836, 95% CI: 1.597-5.038, P=0.000), diameter (OR 2.023, 95% CI: 1.158-3.532, P=0.013), IC in the arterial phase (OR 4.444, 95% CI: 2.808-7.035, P=0.000), and IC in the venous phase (OR 5.387, 95% CI: 3.449-8.413, P=0.000) were independent risk factors for LNM in patients with PTC. Conclusions: DECT had good diagnostic performance in the differentiation of cervical metastatic LNs <0.5 cm in patients with PTC.

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“…The importance of BUS and CDFI duplex ultrasound in patients with CLA is well recognized, and this method is recommended as the first-line diagnostic tool for unexplained CLA [11,13]. However, the diagnostic performance of the dual-modality US strongly relies on the clinical and professional expertise of radiologists [14,15]. Subjective image interpretation, lack of effective quantification, and persistent intra-and interobserver variability remain the main dilemmas faced in US examinations.…”

Section: Introductionmentioning

confidence: 99%

Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy

Zhu

Meng

Xiao

et al. 2022

BMC Med

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Background Accurate diagnosis of unexplained cervical lymphadenopathy (CLA) using medical images heavily relies on the experience of radiologists, which is even worse for CLA patients in underdeveloped countries and regions, because of lack of expertise and reliable medical history. This study aimed to develop a deep learning (DL) radiomics model based on B-mode and color Doppler ultrasound images for assisting radiologists to improve their diagnoses of the etiology of unexplained CLA. Methods Patients with unexplained CLA who received ultrasound examinations from three hospitals located in underdeveloped areas of China were retrospectively enrolled. They were all pathologically confirmed with reactive hyperplasia, tuberculous lymphadenitis, lymphoma, or metastatic carcinoma. By mimicking the diagnosis logic of radiologists, three DL sub-models were developed to achieve the primary diagnosis of benign and malignant, the secondary diagnosis of reactive hyperplasia and tuberculous lymphadenitis in benign candidates, and of lymphoma and metastatic carcinoma in malignant candidates, respectively. Then, a CLA hierarchical diagnostic model (CLA-HDM) integrating all sub-models was proposed to classify the specific etiology of each unexplained CLA. The assistant effectiveness of CLA-HDM was assessed by comparing six radiologists between without and with using the DL-based classification and heatmap guidance. Results A total of 763 patients with unexplained CLA were enrolled and were split into the training cohort (n=395), internal testing cohort (n=171), and external testing cohorts 1 (n=105) and 2 (n=92). The CLA-HDM for diagnosing four common etiologies of unexplained CLA achieved AUCs of 0.873 (95% CI: 0.838–0.908), 0.837 (95% CI: 0.789–0.889), and 0.840 (95% CI: 0.789–0.898) in the three testing cohorts, respectively, which was systematically more accurate than all the participating radiologists. With its assistance, the accuracy, sensitivity, and specificity of six radiologists with different levels of experience were generally improved, reducing the false-negative rate of 2.2–10% and the false-positive rate of 0.7–3.1%. Conclusions Multi-cohort testing demonstrated our DL model integrating dual-modality ultrasound images achieved accurate diagnosis of unexplained CLA. With its assistance, the gap between radiologists with different levels of experience was narrowed, which is potentially of great significance for benefiting CLA patients in underdeveloped countries and regions worldwide.

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Quantification of intranodal vascularity by computer pixel-counting method enhances the accuracy of ultrasound in distinguishing metastatic and tuberculous cervical lymph nodes

Cited by 5 publications

References 25 publications

Evaluation of the performance of traditional machine learning algorithms, convolutional neural network and AutoML Vision in ultrasound breast lesions classification: a comparative study

Evaluation of the performance of traditional machine learning algorithms, convolutional neural network and AutoML Vision in ultrasound breast lesions classification: a comparative study

Dual-energy computed tomography could reliably differentiate metastatic from non-metastatic lymph nodes of less than 0.5 cm in patients with papillary thyroid carcinoma

Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy

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