Abbreviated approach to breast MRI examination reduces the image acquisition and the reading time associated with MR substantially without influencing the diagnostic accuracy (high sensitivity and NPV >99.5%). AP could translate into cost-savings and could enable a higher number of examinations within the same MR session.
AimTo evaluate whether the histology and grading of solitary pulmonary nodules (SPNs) correlated with the results of dynamic multiphase multidetector CT (MDCT) and the [18F]fluorodeoxyglucose standardised uptake value (SUV) in 30 patients.MethodsChest x-rays of 270 patients with incidentally detected SPNs were retrospectively evaluated. Thirty patients with histologically proven SPNs were enrolled. On MDCT and positron emission tomography (PET)/CT images, two experts measured the density of nodules in all perfusion phases and the SUV. Net enhancement (NE) was calculated by subtracting peak pre-contrast density from peak post-contrast density. The Pearson test was used to correlate nodule NE, SUV, grading, histology and diameter.ResultsOf the 30 malignant SPNs, six were classified as G1 (median NE, 31.5 Hounsfield units (HU); median SUV, 4.8 units), 15 were classified as G2 (median NE, 49 HU; median SUV, 6 units), and nine were classified as G3 (median NE, 32 HU; median SUV, 4.5 units). A highly negative correlation was found in G3 SPNs between NE and the corresponding diameters (r=−0.834; p=0.00524). NE increased with the increase in diameter (r=0.982; p=0.284). SUV increased as the SPN diameter increased (r=0.789; p=0.421). NE and SUV were higher in G2 than G1 SPNs, and lower in G2 than G3 SPNs (r=0.97; p=0.137).ConclusionsThe significant correlation in dedifferentiated (G3) SPNs between NE and diameter (r=−0.834; p=0.00524) supports the theory that stroma and neoangiogenesis are fundamental in SPN growth. The highly negative correlation between NE and diameter demonstrates a net decrease in perfusion despite an increase in dimension. The multidisciplinary approach used herein may result in a more precise prognosis and consequently a better therapeutic outcome, particularly in patients with undifferentiated lung cancer.
Our aim was to evaluate the surgical impact of preoperative MRI in young patients. We reviewed a single-institution database of 283 consecutive patients below 40 years of age and who were treated for breast cancer. Thirty-seven (13 %) patients who received neoadjuvant chemotherapy were excluded. The remaining 246 patients included 124 (50 %) who preoperatively underwent conventional imaging (CI), i.e., mammography/ultrasonography (CI-group), and 122 (50 %) who underwent CI and dynamic MRI (CI + MRI-group). Pathology of surgical specimens served as a reference standard. Mann-Whitney, χ (2), and McNemar statistics were used. There were no significant differences between groups in terms of age, tumor pathologic subtype, stage, receptor, or nodal status. The mastectomy rate was 111/246 (45 %) overall but was significantly different between groups (46/124, 37 %, for the CI group and 65/122, 53 %, for the CI + MRI group; p = 0.011). Of 122 CI + MRI patients, 46 (38 %) would have undergone mastectomy due to CI alone, while MRI determined 19 additional mastectomies, increasing the mastectomy rate from 38 % to 53 % (p < 0.001). The number of patients with multifocal, multicentric, synchronous, or bilateral cancers was significantly different between groups (10/124, 8 %, for the CI group and 33/122, 27 %, for the CI + MRI group; p < 0.001). In the CI + MRI group, multifocal, multicentric, or synchronous bilateral cancers were detected with mammography in 5/33 (15 %) patients, with ultrasonography in 15/33 (45 %) patients, and with MRI in 32/33 (97 %) patients (p < 0.005). Two mastectomies were due to false positives at both conventional tests in the CI group (2/124, 1.6 %) and two mastectomies were due to MRI false positives in the CI + MRI group (2/122, 1.6 %). In conclusion, breast cancer in young patients was treated with mastectomy in 37-38 % of cases on the basis of CI only and in these patients MRI was more sensitive than CI for multifocal, multicentric, or synchronous bilateral cancers, resulting in an additional mastectomy rate of 15 %. A low probability of inappropriate imaging-based decision-making for mastectomy exists for both CI alone and for CI + MRI, making presurgical needle biopsy mandatory for findings that suggest a need for mastectomy.
Purpose: To evaluate radiomics features in order to: differentiate malignant versus benign lesions; predict low versus moderate and high grading; identify positive or negative hormone receptors; and discriminate positive versus negative human epidermal growth factor receptor 2 related to breast cancer. Methods: A total of 182 patients with known breast lesions and that underwent Contrast-Enhanced Mammography were enrolled in this retrospective study. The reference standard was pathology (118 malignant lesions and 64 benign lesions). A total of 837 textural metrics were extracted by manually segmenting the region of interest from both craniocaudally (CC) and mediolateral oblique (MLO) views. Non-parametric Wilcoxon–Mann–Whitney test, receiver operating characteristic, logistic regression and tree-based machine learning algorithms were used. The Adaptive Synthetic Sampling balancing approach was used and a feature selection process was implemented. Results: In univariate analysis, the classification of malignant versus benign lesions achieved the best performance when considering the original_gldm_DependenceNonUniformity feature extracted on CC view (accuracy of 88.98%). An accuracy of 83.65% was reached in the classification of grading, whereas a slightly lower value of accuracy (81.65%) was found in the classification of the presence of the hormone receptor; the features extracted were the original_glrlm_RunEntropy and the original_gldm_DependenceNonUniformity, respectively. The results of multivariate analysis achieved the best performances when using two or more features as predictors for classifying malignant versus benign lesions from CC view images (max test accuracy of 95.83% with a non-regularized logistic regression). Considering the features extracted from MLO view images, the best test accuracy (91.67%) was obtained when predicting the grading using a classification-tree algorithm. Combinations of only two features, extracted from both CC and MLO views, always showed test accuracy values greater than or equal to 90.00%, with the only exception being the prediction of the human epidermal growth factor receptor 2, where the best performance (test accuracy of 89.29%) was obtained with the random forest algorithm. Conclusions: The results confirm that the identification of malignant breast lesions and the differentiation of histological outcomes and some molecular subtypes of tumors (mainly positive hormone receptor tumors) can be obtained with satisfactory accuracy through both univariate and multivariate analysis of textural features extracted from Contrast-Enhanced Mammography images.
Objective/Background: Qualitative assessment of uncertain (type II) time-intensity curves (TICs) in breast DCE-MRI is problematic and operator dependent. The aim of this work is to evaluate if a semi-quantitative assessment of uncertain TICs could improve overall diagnostic performance. Methods: In this study 49 lesions from 44 patients were retrospectively analysed. Per each lesion one region-of-interest (ROI)-averaged TIC was qualitatively evaluated by two radiologists in consensus: all the ROIs resulted in type II (uncertain) TIC. The same TICs were semi-quantitatively re-classified on the basis of the difference between the signal intensities of the last-time-point and of the peak: this difference was classified according to two different cut-off ranges (±5% and ±3%). All patients were cytological or histological biopsy proven. Fisher test and McNemar test were performed to evaluate if results were statistically significant (p < 0.05). Results: Using ±5% cut-off 16 TICs were reclassified as type III and 12 as type I while 21 were reclassified again as type II. Using ±3% 22 TICs were reclassified as type III and 16 as type I while 11 were reclassified again as type II. The semi-quantitative classification was compared to the histologicalcytological results: the sensitivity, specificity, positive and negative predictive values obtained with ±3% were 77%, 91%, 91% and 78% respectively while using ±5% were 58%, 96%, 94% and 68% respectively. Using the ±5% cut-off 26/28 (93%) TICs were correctly reclassified while using the ±3% cut-off 34/38 (90%) TICs were correctly reclassified (p < 0.05). Conclusions: Semi-quantitative methods in kinetic curve assessment on DCE-MRI could improve classification of qualitatively uncertain TICs, leading to a more accurate classification of suspicious breast lesions.
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