Initial MRI findings predictive of a pathological complete response to neoadjuvant treatments in HER2-positive breast cancers.

PURPOSE: This study aimed to determine if initial MRI findings could predict a pathological complete response (pCR) following neoadjuvant systemic therapy (NST) in HER2-positive breast cancers. METHODS: The study retrospectively included 111 patients (Center 1, training set) and 71 patients (Center 2, validation set) with HER2-positive cancer who underwent NST. Initial clinicopathological data and MRI findings were recorded. Continuous variables were analyzed using the Mann-Whitney and Student's t-tests, while categorical variables were analyzed using the χ2 or Fisher's exact test. Univariate analysis was conducted to determine the associations between these variables and pathological complete response (pCR), defined as the absence of invasive malignant cells in the breast and lymph nodes. Interobserver reproducibility was assessed for associated non-mass enhancement (NME) parameter by analyzing 50 MR studies (intraclass correlation coefficient). RESULTS: pCR was achieved in 67 patients, 51 (46 %) from Center 1 and 16 (23%) from Center 2 (p = 0.003), with significant differences between Centers 1 and 2 in tumor-infiltrating lymphocyte levels and lymphovascular invasion (p < 0.001). The initial presence of suspicious associated NME was the only significant parameter predictive of pCR (p < 0.001 for Center 1 and 0.04 for Center 2). The inter-observer reproducibility for this MRI feature was good, with an intraclass correlation coefficient of 0.872 (95 % CI: 0.73-1.00). CONCLUSION: The presence of suspicious associated NME in HER2-positive cancers on the initial MRI study was predictive of achieving pCR after NST. This significant preliminary finding warrants confirmation through prospective multicenter studies.

Multiparametric MRI and Radiomics for the Prediction of HER2-Zero, -Low, and -Positive Breast Cancers.

Background Half of breast cancers exhibit low expression levels of human epidermal growth factor receptor 2 (HER2) and can be targeted by new antibody-drug conjugates. The imaging differences between HER2-zero (immunohistochemistry [IHC] score of 0), HER2-low (IHC score of 1+ or 2+ with negative findings at fluorescence in situ hybridization [FISH]), and HER2-positive (IHC score of 2+ with positive findings at FISH or IHC score of 3+) breast cancers were unknown. Purpose To assess whether multiparametric dynamic contrast-enhanced MRI-based radiomic features can help distinguish HER2 expressions in breast cancer. Materials and Methods This study included women with breast cancer who underwent MRI at two different centers between December 2020 and December 2022. Tumor segmentation and radiomic feature extraction were performed on T2-weighted and dynamic contrast-enhanced T1-weighted images. Unsupervised correlation analysis of reproducible features and least absolute shrinkage and selector operation were used for the selection of features to build a radiomics signature. The area under the receiver operating characteristic curve (AUC) was used to assess the performance of the radiomic signature. Multivariable logistic regression was used to identify independent predictors for distinguishing HER2 expressions in both the training and prospectively acquired external data set. Results The training set included 208 patients from center 1 (mean age, 53 years ± 14 [SD]), and the external test set included 131 patients from center 2 (mean age, 54 years ± 13). In the external test data set, the radiomic signature achieved an AUC of 0.80 (95% CI: 0.71, 0.89) for distinguishing HER2-low and -positive tumors versus HER2-zero tumors and was a significant predictive factor for distinguishing these two groups (odds ratio = 7.6; 95% CI: 2.9, 19.8; P < .001). Among HER2-low or -positive breast cancers, histology type, associated nonmass enhancement, and multiple lesions at MRI had an AUC of 0.77 (95% CI: 0.68, 0.86) in the external test set for the prediction of HER2-positive versus HER2-low cancers. Conclusion The radiomic signature and tumor descriptors from multiparametric breast MRI may predict distinct HER2 expressions of breast cancers with therapeutic implications. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Kataoka and Honda in this issue.

Transthoracic lung biopsy for pulmonary nodules ≤20†mm in routine clinical care.

BACKGROUND: Computed tomography (CT) screening has improved lung cancer survival, yet increasingly detects small lung lesions. Thus, the number of transthoracic lung biopsies (TTLB) for small nodules is expected to rise significantly. The aim of the present study was to evaluate the diagnostic accuracy and safety of CT-guided TTLB for nodules ≤20 mm versus nodules >20 mm. STUDY DESIGN AND METHODS: Data for CT-guided TTLBs from 474 consecutive patients were prospectively collected over a 3-year period (198 lesions ≤20 mm and 276 lesions >20 mm) in a teaching hospital and analysed in terms of diagnostic performance and complications. RESULTS: There were more conclusive biopsies in the >20 mm lesion group (n=236, 85.5%) than in ≤20 mm lesion group (n=140, 70.7%; p<0.001). The overall accuracy, sensitivity, specificity and negative predictive value for diagnosing malignant lesions after first TTLB were 88.4%, 84%, 100% and 70.1%, respectively, for ≤20 mm lesions, and 94.2%, 93%, 100% and 74.6%, respectively, for >20 mm lesions. Pneumothorax requiring drainage was significantly more common for ≤20 mm lesions, compared to TTLB of larger lesions (9.6% versus 4.3%; p=0.02). Prolonged hospital stay due to pneumothorax occurred in 27 (17.4%) TTLBs of ≤20 mm lesions and 15 (7%) TTLBs of >20 mm lesions (p=0.002). There were no deaths. The only variable significantly associated with diagnostic failure in the ≤20 mm lesion group was the radiologist's experience. INTERPRETATION: TTLBs for lesions ≤20 mm were associated with slightly lower diagnostic performance, whereas the higher rate of major complications was still inferior to that extrapolated from United States insurance databases.