Breast mass assessment on chest CT: Axial, sagittal, coronal or maximal intensity projection?

Radiology of fibrosis. Part I: Thoracic organs

Sustained injury from factors such as hypoxia, infection, or physical damage may provoke improper tissue repair and the anomalous deposition of connective tissue that causes fibrosis. This phenomenon may take place in any organ, ultimately leading to their dysfunction and eventual failure. Tissue fibrosis has also been found to be central in both the process of carcinogenesis and cancer progression. Thus, its prompt diagnosis and regular monitoring is necessary for implementing effective disease-modifying interventions aiming to reduce mortality and improve overall quality of life. While significant research has been conducted on these subjects, a comprehensive understanding of how their relationship manifests through modern imaging techniques remains to be established. This work intends to provide a comprehensive overview of imaging technologies relevant to the detection of fibrosis affecting thoracic organs as well as to explore potential future advancements in this field.

Iodinated Contrast Enhancement of Breast Cancer on Prone Multidetector Computed Tomography-Preliminary Findings

OBJECTIVES

Contrast-enhanced breast imaging has gained increasing importance in the diagnosis and management of breast cancer. The aim of this study was to assess breast cancer enhancement after contrast administration on prone multidetector computed tomography (MDCT).

MATERIALS AND METHODS

This retrospective, unicentric, institutional review board-approved study included patients with newly diagnosed breast cancer who were submitted to contrast-enhanced MDCT in prone position, with image acquisition before and after nonionic iodinated contrast administration.

RESULTS

Sixty breast cancer patients aged between 31 and 74 years (mean, 49 years) were included. Most patients (n = 50, 83.3%) had no special type invasive breast carcinoma and luminal subtype (n = 45, 75%). All index breast tumors were identified on prone MDCT. Forty-three cases (70.5%) presented as mass, 13 (21.3%) as nonmass enhancement and 4 (6.6%) as both mass and nonmass enhancement. Mean tumor density was 37.8 HU and 87.9 HU on precontrast and postcontrast images, respectively. Mean contrast enhancement was 50.2 HU (range, 20-109 HU). There were no statistically significant differences in tumor enhancement according to histological type, molecular subtype, nuclear grade, tumor size, or imaging presentation.

CONCLUSIONS

Our results show that breast cancer usually can be identified and have significant contrast enhancement on prone MDCT images. This method could be used as an alternative when other contrast-enhanced breast imaging methods are not available.

Robustness Evaluation of a Deep Learning Model on Sagittal and Axial Breast DCE-MRIs to Predict Pathological Complete Response to Neoadjuvant Chemotherapy

To date, some artificial intelligence (AI) methods have exploited Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to identify finer tumor properties as potential earlier indicators of pathological Complete Response (pCR) in breast cancer patients undergoing neoadjuvant chemotherapy (NAC). However, they work either for sagittal or axial MRI protocols. More flexible AI tools, to be used easily in clinical practice across various institutions in accordance with its own imaging acquisition protocol, are required. Here, we addressed this topic by developing an AI method based on deep learning in giving an early prediction of pCR at various DCE-MRI protocols (axial and sagittal). Sagittal DCE-MRIs refer to 151 patients (42 pCR; 109 non-pCR) from the public I-SPY1 TRIAL database (DB); axial DCE-MRIs are related to 74 patients (22 pCR; 52 non-pCR) from a private DB provided by Istituto Tumori “Giovanni Paolo II” in Bari (Italy). By merging the features extracted from baseline MRIs with some pre-treatment clinical variables, accuracies of 84.4% and 77.3% and AUC values of 80.3% and 78.0% were achieved on the independent tests related to the public DB and the private DB, respectively. Overall, the presented method has shown to be robust regardless of the specific MRI protocol.