Dataset Information

Differentiation of Malignant and Benign Incidental Breast Lesions Detected by Chest Multidetector-Row Computed Tomography: Added Value of Quantitative Enhancement Analysis.

ABSTRACT: To retrospectively determine the association between breast lesion morphology and malignancy and to determine the optimal value of lesion enhancement (HU, Hounsfield units) to improve the diagnostic accuracy of breast cancer in patients with incidental breast lesions (IBLs). A total of 97 patients with 102 IBLs detected from July 2009 to December 2012 were enrolled in this study. Two radiologists analyzed CT images for the presence of malignancy based on the morphology of the lesions alone and in combination with an enhancement value (HU) analysis. There were 36 malignant and 66 benign IBLs. When the morphology and enhancement values were combined, the sensitivity, specificity, and accuracy were 92%, 97%, and 95%, respectively, for reader 1 and 89%, 94%, and 92%, respectively, for reader 2. The addition of HU values led to correct changes in the diagnosis; specifically, the accuracy of the diagnosis of reader 1 and reader 2 improved by 6.9% and 11.8%, respectively. The addition of the enhancement value (HU) to the CT morphology improved the diagnostic accuracy in the differentiation of malignant from benign IBLs by using the region of interest (ROI) to measure the HU within the most suspicious part of the lesion.

SUBMITTER: Lin YP

PROVIDER: S-EPMC4851406 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Differentiation of Malignant and Benign Incidental Breast Lesions Detected by Chest Multidetector-Row Computed Tomography: Added Value of Quantitative Enhancement Analysis.

Lin Yu-Pang YP Hsu Hsian-He HH Ko Kai-Hsiung KH Chu Chi-Ming CM Chou Yu-Ching YC Chang Wei-Chou WC Chang Tsun-Hou TH

PloS one 20160429 4

To retrospectively determine the association between breast lesion morphology and malignancy and to determine the optimal value of lesion enhancement (HU, Hounsfield units) to improve the diagnostic accuracy of breast cancer in patients with incidental breast lesions (IBLs). A total of 97 patients with 102 IBLs detected from July 2009 to December 2012 were enrolled in this study. Two radiologists analyzed CT images for the presence of malignancy based on the morphology of the lesions alone and i ...[more]

PMID: 27128524

Similar Datasets

Project description:Background: Reconstruction with the use of perforator flaps makes it possible to make the skin surface resistant to the influence of mechanical factors and as similar to the lost skin cover as possible. However, while planning any flap, along with the design of the required shape and size, its blood supply should be taken into account to ensure optimal viability. Therefore, the task to precisely determine the topographic-anatomical relationships suitable for the formation of a pedicle of perforators is still relevant. The aim of this study was to increase the efficiency of surgical reconstruction of wound defects by transposition of locoregional perforator flaps. Methods: The authors conducted a retrospective analysis of 72 cases of reconstruction by means of locoregional perforator flaps with vascular pedicle detachment to determine the efficiency of preoperative diagnostic preparation with the help of multidetector-row computed tomographic angiography (MDCT) in the process of reconstruction. Thirty-seven individual cases of surgical interventions were chosen using a case-controlled study from the study group when MDCT with angiography was used for preoperative planning of perforator flaps, as well as 35 control cases similar in terms of important predictive peculiarities with the reconstruction at the same level of difficulty. The patient groups were precisely matched by gender (P = .950), age (P = .804), flap area (P = .192), and type of reconstruction that was performed. Results: In all cases, the location of the perforator with a diameter greater than 1.0 mm was marked. All perforators determined during MDCT scanning were faultlessly localized intraoperatively. The distance between the intraoperative position of the perforator and the position obtained in the result of the examination did not exceed 1 cm. There was no need to change the planned design of the flap intraoperatively. In all cases where MDCT was performed, the duration of the surgical procedure varied from 60 to 150 minutes (average: 120.77 [18.90] minutes) and was reduced by 49.40 minutes (95% CI: 39.17-59.63) compared with the patients who did not undergo preoperative visualization of perforators where the average duration of the operation was 170.17 (19.19) minutes (from 140 to 220 minutes). Among the patients examined by MDCT, surgical complications were noted in 5 cases (13.51%) compared to 14 cases (40.00%) in the control group. Conclusions: The preoperative MDCT for the locoregional perforator flap reconstruction makes it possible to increase the efficiency of patient treatment given the reduction in surgery duration by 49.40 minutes (95% CI: 39.17-59.63) on average and the reduction in the level of postsurgery complications from 40% to 13.5% compared with the group of patients in whom presurgical visualization was not performed (P = .031).

Project description:BackgroundEarly diagnosis is crucial to the treatment of breast cancer, but conventional imaging detection is challenging. Radiomics has the potential to improve early diagnostic efficacy in a noninvasive manner. This study examined whether integrating computed tomography (CT) radiomics information based on ultrasound (US) models can improve the efficacy of breast cancer prediction.MethodsWe retrospectively analyzed 420 patients with pathologically confirmed benign or malignant breast tumors. Clinical data and examination images were collected, and the population was divided into training (n=294) and validation (n=126) groups at a ratio of 7:3. The region of interest (ROI) was manually segmented along the tumor boundary using MaZda software, and the features of each ROI was extracted. After dimension reduction and screening, the best features were retained. Subsequently, random forest (RF), support vector machines, and K-nearest neighbor classifiers were used to establish prediction models in an US and combined-methods group.ResultsFinally, 8 of the 379 features were retained in the US group. Random forest was found to be the best model, and the area under the curve (AUC) of the training and validation groups was 0.90 [95% confidence interval (CI): 0.852-0.942] and 0.85 (95% CI: 0.775-0.930), respectively. Meanwhile, 12 of the 750 features were retained in the combined group. In this regard, random forest proved to be the best model, and the AUC of the training and validation group was 0.95 (95% CI: 0.918-0.981) and 0.92 (95% CI: 0.866-0.969), respectively. The calibration curve showed a good fit of the model. The decision curve showed that the clinical net benefit of the combined group was far greater than that of any single examination, and the prediction model of the combined group exhibited a degree of practical clinical value.ConclusionsThe combined model based on US and CT images has potential application value in the prognostic prediction of benign and malignant breast diseases.

Dataset Information

Differentiation of Malignant and Benign Incidental Breast Lesions Detected by Chest Multidetector-Row Computed Tomography: Added Value of Quantitative Enhancement Analysis.

Publications

Differentiation of Malignant and Benign Incidental Breast Lesions Detected by Chest Multidetector-Row Computed Tomography: Added Value of Quantitative Enhancement Analysis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets