A Comparison of the Diagnostic Accuracy and Reliability of Subjective Grading and Computer-Aided Assessment of Intranodal Vascularity in Differentiating Metastatic and Reactive Cervical Lymphadenopathy

Lam, Joanne K.Y.; Ying, Michael; Cheung, S. Y.; Yeung, Ka-Wai; Yu, Peter H.; Cheng, Ho Ming; Ahuja, Anil T.

doi:10.1055/s-0034-1384939

Cited by 5 publications

(5 citation statements)

References 5 publications

(9 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Variable results for qualitative vascularity assessment can be attributed to the subjective grading of vascularity which is prone to interpreter bias. Quantitative vascularity analysis approaches based on segmentation of peripheral and central vascularity overcome limitations of subjective visual assessment of vascularity pattern and distribution [15,16,77]. Only one study demonstrated that the combination of Doppler ultrasound CAD with qualitative grey scale ultrasound features can result in an optimal sensitivity and specificity, but with reduced sensitivity compared to individual assessment of both qualitative and Doppler CAD [66].…”

Section: Discussionmentioning

confidence: 99%

“…Computer-aided diagnosis (CAD) systems have emerged in past years as non-invasive approaches to complement radiologists’ interpretation and potentially overcome subjective interpretation limitations. CAD detection and diagnosis methods are based on machine learning approaches that use statistical and data mining algorithms, which rely on textural ultrasound features and quantitative regional segmentation of vascularity, to differentiate benign and malignant nodules [13,14,15,16]. CAD software can be embedded within the ultrasound unit or be used as an isolated program for offline image analysis.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Diagnostic Efficiency of Ultrasound Computer–Aided Diagnosis in Differentiating Thyroid Nodules: A Systematic Review and Narrative Synthesis

Chambara

Ying

2019

Cancers

View full text Add to dashboard Cite

Computer-aided diagnosis (CAD) techniques have emerged to complement qualitative assessment in the diagnosis of benign and malignant thyroid nodules. The aim of this review was to summarize the current evidence on the diagnostic performance of various ultrasound CAD in characterizing thyroid nodules. PUBMED, EMBASE and Cochrane databases were searched for studies published until August 2019. The Quality Assessment of Studies of Diagnostic Accuracy included in Systematic Review 2 (QUADAS-2) tool was used to assess the methodological quality of the studies. Reported diagnostic performance data were analyzed and discussed. Fourteen studies with 2232 patients and 2675 thyroid nodules met the inclusion criteria. The study quality based on QUADAS-2 assessment was moderate. At best performance, grey scale CAD had a sensitivity of 96.7% while Doppler CAD was 90%. Combined techniques of qualitative grey scale features and Doppler CAD assessment resulted in overall increased sensitivity (92%) and optimal specificity (85.1%). The experience of the CAD user, nodule size and the thyroid malignancy risk stratification system used for interpretation were the main potential factors affecting diagnostic performance outcomes. The diagnostic performance of CAD of thyroid ultrasound is comparable to that of qualitative visual assessment; however, combined techniques have the potential for better optimized diagnostic accuracy.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Diagnostic Efficiency of Ultrasound Computer–Aided Diagnosis in Differentiating Thyroid Nodules: A Systematic Review and Narrative Synthesis

Chambara

Ying

2019

Cancers

View full text Add to dashboard Cite

show abstract

“…Computer-aided diagnosis (CAD) has attracted great attention of researchers as a newly developed technique that has potential in enhancing radiologists' interpretation and overcoming subjective limitations. The CAD detection and diagnosis methods are based on machine learning approaches that extract features based on shape, texture, and statistical values, differentiating benign and malignant nodules (5)(6)(7).…”

Section: Introductionmentioning

confidence: 99%

A Clinical Assessment of an Ultrasound Computer-Aided Diagnosis System in Differentiating Thyroid Nodules With Radiologists of Different Diagnostic Experience

Zhang

Chen

et al. 2020

Front. Oncol.

View full text Add to dashboard Cite

Introduction This study aimed to assess the diagnostic performance and the added value to radiologists of different levels of a computer-aided diagnosis (CAD) system for the detection of thyroid cancers. Methods 303 patients who underwent thyroidectomy from October 2018 to July 2019 were retrospectively reviewed. The diagnostic performance of the senior radiologist, the junior radiologist, and the CAD system were compared. The added value of the CAD system was assessed and subgroup analyses were performed according to the size of thyroid nodules. Results In total, 186 malignant thyroid nodules, and 179 benign thyroid nodules were included; 168 were papillary thyroid carcinoma (PTC), 7 were medullary thyroid carcinoma (MTC), 11 were follicular carcinoma (FTC), 127 were follicular adenoma (FA) and 52 were nodular goiters. The CAD system showed a comparable specificity as the senior radiologist (86.0% vs. 86.0%, p > 0.99), but a lower sensitivity and a lower area under the receiver operating characteristic (AUROC) curve (sensitivity: 71.5% vs. 95.2%, p < 0.001; AUROC: 0.788 vs. 0.906, p < 0.001). The CAD system improved the diagnostic sensitivities of both the senior and the junior radiologists (97.8% vs. 95.2%, p = 0.063; 88.2% vs. 75.3%, p < 0.001). Conclusion The use of the CAD system using artificial intelligence is a potential tool to distinguish malignant thyroid nodules and is preferable to serve as a second opinion for less experienced radiologists to improve their diagnosis performance.

show abstract

“…A previous study assessed intranodal vascularity by subjective visual judgement and classified lymph nodes according to the abundance of intranodal vascularity (13). Another study evaluated the diagnostic performance of solely VI analysis of lymph node but without studied the diagnostic performance of combining VI analysis with conventional ultrasound assessment of lymph node (16). In the present study, we have investigated the added value of VI analysis on conventional ultrasound assessment to distinguish between metastatic and tuberculous cervical lymph nodes.…”

Section: Discussionmentioning

confidence: 99%

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

Cheng

Ahuja

Ying

2019

Quant. Imaging Med. Surg.

View full text Add to dashboard Cite

Background: Ultrasound is a common imaging method for assessment of cervical lymph nodes. However, metastatic and tuberculous lymph nodes have similar sonographic features in routine ultrasound examination.Computer-aided assessment could be a potential adjunct to enhance the accuracy of differential diagnosis.Methods: Gray-scale and power Doppler sonograms of 100 patients with palpable cervical lymph nodes were reviewed and analyzed (60 metastatic nodes, 40 tuberculous nodes). Final diagnosis of lymph nodes was based on fine needle aspiration and cytology. Sonograms were reviewed and assessed for nodal shape, echogenic hilus, intranodal necrosis and vascular distribution (conventional assessment). Intranodal vascularity was quantified using a customized computer algorithm to determine vascularity index (VI). The diagnostic accuracy of using conventional assessment and its combination with intranodal VI method was evaluated and compared.Results: Metastatic and tuberculous nodes tended to be round (75.0% vs. 50.0%), without echogenic hilus (86.7% vs. 72.5%) and have peripheral vascularity (73.3% vs. 85.0%). Intranodal necrosis is more common in tuberculous nodes (27.5%) than metastatic nodes (8.3%). Using conventional assessment in differentiating metastatic and tuberculous nodes, the diagnostic accuracy was 56% with a sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 56.7%, 55%, 65.4% and 45.8% respectively.The VI of metastatic nodes (23.4%±2.1%) was significantly higher than that of tuberculous nodes (12.0%±1.6%) (P<0.05). The optimum cut-off of VI for the differential diagnosis was 20%. By combining conventional assessment and intranodal VI quantification, the diagnostic accuracy was increased to 69% with a sensitivity, specificity, PPV and NPV of 80%, 52.5%, 71.6%, 63.6% respectively. The increase in sensitivity was statistically significant (P=0.006).Conclusions: Computer-aided quantification of intranodal vascularity provides added value in routine ultrasound assessment of cervical lymph nodes. It enhances the accuracy of ultrasound in distinguishing metastatic and tuberculous cervical lymph nodes.

show abstract

A Comparison of the Diagnostic Accuracy and Reliability of Subjective Grading and Computer-Aided Assessment of Intranodal Vascularity in Differentiating Metastatic and Reactive Cervical Lymphadenopathy

Abstract: In the assessment of the degree of intranodal vascularity, the QCA method was more accurate and reliable than the QSG method in distinguishing metastatic and reactive lymph nodes.

Cited by 5 publications

References 5 publications

The Diagnostic Efficiency of Ultrasound Computer–Aided Diagnosis in Differentiating Thyroid Nodules: A Systematic Review and Narrative Synthesis

The Diagnostic Efficiency of Ultrasound Computer–Aided Diagnosis in Differentiating Thyroid Nodules: A Systematic Review and Narrative Synthesis

A Clinical Assessment of an Ultrasound Computer-Aided Diagnosis System in Differentiating Thyroid Nodules With Radiologists of Different Diagnostic Experience

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

Contact Info

Product

Resources

About