Dataset Information

Assessment of Functional Differences in Malignant and Benign Breast Lesions and Improvement of Diagnostic Accuracy by Using US-guided Diffuse Optical Tomography in Conjunction with Conventional US.

ABSTRACT: Purpose To investigate ultrasonography (US)-guided diffuse optical tomography to distinguish the functional differences of hemoglobin concentrations in a wide range of malignant and benign breast lesions and to improve breast cancer diagnosis in conjunction with conventional US. Materials and Methods The study protocol was approved by the institutional review boards and was HIPAA compliant. Written informed consent was obtained from all patients. Patients (288 women; mean age, 50 years; range, 17-94 years) who underwent US-guided biopsy were imaged with a handheld US and optical probe. The US-imaged lesion was used to guide reconstruction of light absorption maps at four wavelengths, and total hemoglobin (tHb), oxygenated hemoglobin (oxyHb), and deoxygenated hemoglobin (deoxyHb) were computed from the absorption maps. A threshold (80 ?mol/L) was chosen on the basis of this study population. Two radiologists retrospectively evaluated US images on the basis of the US Breast Imaging Reporting and Data System lexicon, and a lesion was considered malignant when a score of 4C or 5 was given or a lesion had tHb greater than 80 ?mol/L. A two-sample t test was used to calculate significance between groups, and Spearman ? was computed between hemoglobin parameters and tumor pathologic grades. Results Three tumors were Tis, 37 were T1, 19 were T2-T4 carcinomas, and 233 were benign lesions. The mean maximum tHb, oxyHb, and deoxyHb of Tis-T1 and T2-T4 groups were 89.3 ?mol/L ± 20.2 (standard deviation), 65.0 ?mol/L ± 20.8, and 33.5 ?mol/L ± 11.3, respectively, and 84.7 ?mol/L ± 32.8, 57.1 ?mol/L ± 19.8, and 34.7 ?mol/L ± 18.9, respectively. The corresponding values of benign lesions were 54.1 ?mol/L ± 23.5, 38.0 ?mol/L ± 17.4, and 25.2 ?mol/L ± 13.8, respectively. The mean maximum tHb, oxyHb, and deoxyHb were significantly higher in the malignant groups than the benign group (P <.001, <.001, and .041, respectively). For malignant lesions, the mean maximum tHb moderately correlated with tumor histologic grade and nuclear grade (? = 0.283 and 0.315, respectively). The mean maximum oxyHb moderately correlated with tumor nuclear grade (? = 0.267). When radiologists' US diagnosis and the tHb were used together, the sensitivity, specificity, positive predictive value, and negative predictive value were 96.6%-100%, 77.3%-83.3%, 52.7%-59.4%, and 99.0%-100%, respectively, for the combined malignant group. Conclusion The tHb and oxyHb correlate with breast cancer pathologic grade and can be used as an adjunct to US to improve sensitivity and negative predictive value in breast cancer diagnosis. (©) RSNA, 2016 Online supplemental material is available for this article.

SUBMITTER: Zhu Q

PROVIDER: S-EPMC4976463 | biostudies-literature | 2016 Aug

REPOSITORIES: biostudies-literature

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Assessment of Functional Differences in Malignant and Benign Breast Lesions and Improvement of Diagnostic Accuracy by Using US-guided Diffuse Optical Tomography in Conjunction with Conventional US.

Zhu Quing Q Ricci Andrew A Hegde Poornima P Kane Mark M Cronin Edward E Merkulov Alex A Xu Yan Y Tavakoli Behnoosh B Tannenbaum Susan S

Radiology 20160302 2

Purpose To investigate ultrasonography (US)-guided diffuse optical tomography to distinguish the functional differences of hemoglobin concentrations in a wide range of malignant and benign breast lesions and to improve breast cancer diagnosis in conjunction with conventional US. Materials and Methods The study protocol was approved by the institutional review boards and was HIPAA compliant. Written informed consent was obtained from all patients. Patients (288 women; mean age, 50 years; range, 1 ...[more]

PMID: 26937708

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Project description:Background: Because neurofibromatosis type I (NF1) is a cancer predisposition disease, it is important to distinguish between benign and malignant lesions, especially in the craniofacial area. Purpose: The purpose of this study is to improve effectiveness in the diagnostic performance in discriminating malignant from benign craniofacial lesions based on computed tomography (CT) using a Keras-based machine-learning model. Methods: The Keras-based machine learning technique, a neural network package in the Python language, was used to train the diagnostic model on CT datasets. Fifty NF1 patients with benign craniofacial neurofibromas and six NF1 patients with malignant peripheral nerve sheath tumors (MPNSTs) were selected as the training set. Three validation cohorts were used: validation cohort 1 (random selection of 90% of the patients in the training cohort), validation cohort 2 (an independent cohort of 9 NF1 patients with benign craniofacial neurofibromas and 11 NF1 patients with MPNST), and validation cohort 3 (eight NF1 patients with MPNST, not restricted to the craniofacial area). Sensitivity and specificity were tested using validation cohorts 1 and 2, and generalizability was evaluated using validation cohort 3. Results: A total of 59 NF1 patients with benign neurofibroma and 23 NF1 patients with MPNST were included. A Keras-based machine-learning model was successfully established using the training cohort. The accuracy was 96.99 and 100% in validation cohorts 1 and 2, respectively, discriminating NF1-related benign and malignant craniofacial lesions. However, the accuracy of this model was significantly reduced to 51.72% in the identification of MPNSTs in different body regions. Conclusion: The Keras-based machine learning technique showed the potential of robust diagnostic performance in the differentiation of craniofacial MPNSTs and benign neurofibromas in NF1 patients using CT images. However, the model has limited generalizability when applied to other body areas. With more clinical data accumulating in the model, this system may support clinical doctors in the primary screening of true MPNSTs from benign lesions in NF1 patients.

Project description:Background: We performed a network meta-analysis to compare the diagnostic accuracy of contrast-enhanced ultrasound (CEUS) and shear wave elastography (SWE) in differentiating benign and malignant lesions in different body sites. Methods: A computerized literature search of Medline, Embase, SCOPUS, and Web of Science was performed using relevant keywords. Following data extraction, we calculated sensitivity, specificity, positive likelihood ratio (LR), negative LR, and diagnostic odds ratio (DOR) for CEUS, and SWE compared to histopathology as a reference standard. Statistical analyses were conducted by MetaDiSc (version 1.4) and R software (version 3.4.3). Results: One hundred and fourteen studies (15,926 patients) were pooled in the final analyses. Network meta-analysis showed that CEUS had significantly higher DOR than SWE (DOR = 27.14, 95%CI [2.30, 51.97]) in breast cancer detection. However, there were no significant differences between CEUS and SWE in hepatic (DOR = -6.67, 95%CI [-15.08, 1.74]) and thyroid cancer detection (DOR = 3.79, 95%CI [-3.10, 10.68]). Interestingly, ranking analysis showed that CEUS achieved higher DOR in detecting breast and thyroid cancer, while SWE achieved higher DOR in detecting hepatic cancer. The overall DOR for CEUS in detecting renal cancer was 53.44, 95%CI [29.89, 95.56] with an AUROC of 0.95, while the overall DOR for SWE in detecting prostate cancer was 25.35, 95%CI [7.15, 89.89] with an AUROC of 0.89. Conclusion: Both diagnostic tests showed relatively high sensitivity and specificity in detecting malignant tumors in different organs. Network meta-analysis showed that CEUS had higher diagnostic accuracy than SWE in detecting breast and thyroid cancer, while SWE had higher accuracy in detecting hepatic cancer. However, the results were not statistically significant in hepatic and thyroid malignancies. Further head-to-head comparisons are needed to confirm the optimal imaging technique to differentiate each cancer type.

Project description:ObjectivesWe undertook a systematic review and meta-analysis of the diagnostic performance of mean apparent diffusion coefficient (ADC) values derived by diffusion-weighted (DW)-MRI in the characterization of solid benign and malignant liver lesions, and to assess their value in discriminating these lesions in daily routine practice.MethodsA systematic review of PubMed, Embase, Scopus, and Web of Science was conducted to retrieve studies that used ADC values for differentiating solid benign/dysplastic nodules and malignant liver lesions. A bivariate random-effects model with pooled sensitivity and specificity values with 95% CI (confidence interval) was used. This meta-analysis was performed on the per-lesion basis. Summary receiver operating characteristic (SROC) plot and area under curve (AUC) were created.ResultsA total of 14 original articles were retrieved. The combined (95% CI) sensitivity and specificity of mean ADC values for differentiating solid benign from malignant lesions were 78% (67-86%) and 74% (64-81%), respectively. The pooled (95% CI) positive and negative LRs were respectively 3 (2.3-3.8) and 0.3 (0.21-0.43). The DOR (95% CI) was 10 (7-15). The AUC (95% CI) of the SROC plot was 82% (78-85%). Reporting bias was negligible (p value of regression test = 0.36). Mean size of malignant lesions and breathing pattern of MRI were found to be sources of heterogeneity of pooled sensitivity.ConclusionADC measurement independently may not be an optimal diagnostic imaging method for differentiating solid malignant from solid benign hepatic lesions. The meta-analysis showed that ADC measurement had moderate diagnostic accuracy for characterizing solid liver lesions. Further prospective and comparative studies with pre-specified ADC thresholds could be performed to investigate the best MRI protocol and ADC threshold for characterizing solid liver lesions.Advances in knowledgeADC measurement by DW-MRI does not have a good diagnostic performance to differentiate solid malignant from solid benign lesions. Therefore, we suggest not using ADC values in clinical practice to evaluate solid liver lesions.

Dataset Information

Assessment of Functional Differences in Malignant and Benign Breast Lesions and Improvement of Diagnostic Accuracy by Using US-guided Diffuse Optical Tomography in Conjunction with Conventional US.

Publications

Assessment of Functional Differences in Malignant and Benign Breast Lesions and Improvement of Diagnostic Accuracy by Using US-guided Diffuse Optical Tomography in Conjunction with Conventional US.

Similar Datasets

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