Weekly Paclitaxel given concurrently with Durvalumab has a favorable safety profile in triple-negative metastatic breast cancer

Therapeutic anti-PD-L1 antibodies are safe as a monotherapy, albeit with minimal efficacy in triple-negative breast cancer (TNBC). This trial aimed to test the safety and efficacy of Durvalumab and Paclitaxel in metastatic TNBC. In this open-label, one-arm trial, five cycles of weekly paclitaxel were delivered intravenously (IV) concurrent with Durvalumab that was given IV every 2 weeks. The combination was preceded by one cycle of paclitaxel alone, for immunological priming, followed by Durvalumab solo until disease progression or unacceptable toxicity. Between 2017 and 2019, 14 patients received at least one cycle of the combination therapy. The therapy was safe with no-dose limiting toxicity, except one case of skin lesions. Adverse events (AEs) were reported in 71% of patients, and there was no death due to the combination therapy. Regardless of grade, the most common AEs were headache and peripheral neuropathy, as each happened in four patients (29%), followed by fatigue and skin rash in three patients (21%) each. Grade 3/4 AEs were experienced by three patients (21%), with the most common being headache and anemia, which happened in two patients (14%). The confirmed objective response rate (ORR) was observed in five patients with a median duration of 10.0 months. Median Progression-free survival (PFS) and overall survival (OS) were 5 and 20.7 months, respectively. The combination of Durvalumab and Paclitaxel is safe, leaving room for additional agents. This is the first report on the combination of Durvalumab and Paclitaxel in the treatment of TNBC (NCT02628132).

A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis

COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis, prognosis and treatment strategies to combat the disease. Although nucleic acid detection has been mainly used as the gold standard to confirm this RNA virus-based disease, it has been shown that such a strategy has a high false negative rate, especially for patients in the early stage, and thus CT imaging has been applied as a major diagnostic modality in confirming positive COVID-19. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the state-of-the-art segmentation method requires a high level of human intervention. In this paper, we propose a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon two innovations: 1) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients' data measured at different time points, which greatly alleviates the data scarcity issue; and 2) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an order of magnitude and, at the same time, significantly improves the segmentation accuracy. Comprehensive experimental results over multi-country, multi-hospital, and multi-machine datasets demonstrate the superior performance of our method over the existing ones and suggest its important application value in combating the disease.

Evaluation of breast amorphous calcifications by a computer-aided detection system in full-field digital mammography

OBJECTIVES

The purpose of this study was to evaluate the performance of a direct computer-aided detection (d-CAD) system integrated with full-field digital mammography (FFDM) in assessment of amorphous calcifications.

METHODS

From 1438 consecutive stereotactic-guided biopsies, FFDM images with amorphous calcifications were selected for retrospective evaluation by d-CAD in 122 females (mean age, 56 years; range, 35-84 years). The sensitivity, specificity, accuracy and false-positive rate of the d-CAD system were calculated in the total group of 124 lesions and in the subgroups based on breast density, mammographic lesion distribution and extension. Logistic regression analysis was used to stratify the risk of malignancy by patient risk factors and age.

RESULTS

The d-CAD marked all (36/36) breast cancers, 85% (11/13) of the high-risk lesions and 80% (60/75) of benign amorphous calcifications (p<0.01) correctly. The sensitivity, specificity and diagnostic accuracy for the combined malignant and "high-risk" lesions was 96, 80 and 86%, respectively. The likelihood of malignancy was 29%. There was no significant difference between the marking of fatty or dense breasts (p>0.05); however, d-CAD marks showed differences for small (<7 mm) lesions (p=0.02) and clustered calcifications (p=0.03). The false-positive rate of d-CAD was 1.76 marks per full examination.

CONCLUSION

The d-CAD system correctly marked all biopsy-proven breast cancers and a large number of biopsy-proven high-risk lesions that presented as amorphous calcifications. Given our 29% likelihood of malignancy, imaging-guided biopsy appears to be a reasonable recommendation in cases of amorphous calcifications marked by d-CAD.

Imaging approaches and findings in the reconstructed breast: a pictorial essay

Advances in breast imaging over the last 15 years have improved early breast cancer detection and management. After treatment for breast cancer, many women choose to have reconstructive surgery. In addition, with the availability of widespread genetic screening for breast cancer, an increasing number of women are choosing prophylactic mastectomies and subsequent breast reconstruction. The purpose of this pictorial essay is to present the spectrum of imaging findings in the reconstructed breast.