Analysis of mammograms using artificial intelligence to predict response to neoadjuvant chemotherapy in breast cancer patients: proof of concept.

OBJECTIVES: In this proof of concept study, a deep learning-based method for automatic analysis of digital mammograms (DM) as a tool to aid in assessment of neoadjuvant chemotherapy (NACT) treatment response in breast cancer (BC) was examined. METHODS: Baseline DM from 453 patients receiving NACT between 2005 and 2019 were included in the study cohort. A deep learning system, using the aforementioned baseline DM, was developed to predict pathological complete response (pCR) in the surgical specimen after completion of NACT. Two image patches, one extracted around the detected tumour and the other from the corresponding position in the reference image, were fed into a classification network. For training and validation, 1485 images obtained from 400 patients were used, and the model was ultimately applied to a test set consisting of 53 patients. RESULTS: A total of 95 patients (21%) achieved pCR. The median patient age was 52.5 years (interquartile range 43.7-62.1), and 255 (56%) were premenopausal. The artificial intelligence (AI) model predicted the pCR as represented by the area under the curve of 0.71 (95% confidence interval 0.53-0.90; p = 0.035). The sensitivity was 46% at a fixed specificity of 90%. CONCLUSIONS: Our study describes an AI platform using baseline DM to predict BC patients' responses to NACT. The initial AI performance indicated the potential to aid in clinical decision-making. In order to continue exploring the clinical utility of AI in predicting responses to NACT for BC, further research, including refining the methodology and a larger sample size, is warranted. KEY POINTS: • We aimed to answer the following question: Prior to initiation of neoadjuvant chemotherapy, can artificial intelligence (AI) applied to digital mammograms (DM) predict breast tumour response? • DMs contain information that AI can make use of for predicting pathological complete (pCR) response after neoadjuvant chemotherapy for breast cancer. • By developing an AI system designed to focus on relevant parts of the DM, fully automatic pCR prediction can be done well enough to potentially aid in clinical decision-making.

ESTIMATES OF BREAST CANCER GROWTH RATE FROM MAMMOGRAMS AND ITS RELATION TO TUMOUR CHARACTERISTICS.

This study aimed to investigate the growth rate of 31 consecutive invasive breast cancers based on volume measures on at least two serial mammograms and its relation to histopathological findings. The average tumour volume-doubling time in all invasive breast cancer subtypes was 282 d (range 46-749 d). Grade III breast cancers had a significantly shorter average tumour volume-doubling time of 105 d (range 46-157 d) compared with Grade I and II tumours (average of 296 d, range 147-531 d and average of 353 d, range 139-749 d, respectively) (p = 0.002). Multiple linear regression identified that tumour volume-doubling time was positively associated with patient age, histological grade and progesterone receptor expression and inversely associated with axillary lymph node involvement, human epidermal growth factor receptor 2 and Ki-67 expression (p < 0.001). In conclusion, tumour volume-doubling time as estimated on serial mammography may provide important prognostic information relevant for clinical decision-making.

Breast cancer screening with tomosynthesis--initial experiences.

Experiences gained so far using tomosynthesis for breast cancer screening will be reported. A short summary of results from preparatory studies will also be presented. The sensitivity and specificity of breast tomosynthesis (BT) will be compared with conventional two-dimensional digital mammography (DM) for breast cancer screening in a population-based study. Over 2000 women have been examined so far with BT and DM. The BT reading is significantly more time-consuming than the DM reading. Preparatory studies have shown that BT has a higher diagnostic precision and higher accuracy of size measurements and stage determination than DM. There is potential to use lower compression force with BT compared with DM, without decreasing the diagnostic accuracy. BT might play an important role in clinical as well as screening mammography. A large-scale population-based study to investigate BT as a screening modality is underway.

The diagnostic accuracy of dual-view digital mammography, single-view breast tomosynthesis and a dual-view combination of breast tomosynthesis and digital mammography in a free-response observer performance study.

The purpose of the present study was to compare the diagnostic accuracy of dual-view digital mammography (DM), single-view breast tomosynthesis (BT) and BT combined with the opposite DM view. Patients with subtle lesions were selected to undergo BT examinations. Two radiologists who are non-participants in the study and have experience in using DM and BT determined the locations and extents of lesions in the images. Five expert mammographers interpreted the cases using the free-response paradigm. The task was to mark and rate clinically reportable findings suspicious for malignancy and clinically relevant benign findings. The marks were scored with reference to the outlined regions into lesion localization or non-lesion localization, and analysed by the jackknife alternative free-response receiver operating characteristic method. The analysis yielded statistically significant differences between the combined modality and dual-view DM (p < 0.05). No differences were found between single-view BT and dual-view DM or between single-view BT and the combined modality.

The effect of reduced breast compression in breast tomosynthesis: human observer study using clinical cases.

The aim of this study was to investigate whether the compression force used with conventional mammography can be reduced with breast tomosynthesis (BT), without adversely affecting the visualisation of normal and pathological structures. Forty-five women were examined with BT using full (same as for 2D mammography) and half compression force. Both examinations were performed with the same acquisition parameters. A total of 103 paired structure images were evaluated according to specified image quality criteria. Three experienced radiologists participated in the study. They had to make a forced choice, i.e. choose the image they felt best fulfilled the image quality criteria. The results showed no evident difference in the image quality, indicating that BT may be performed with substantially less compression force compared with 2D mammography. A majority of the examined women felt that half compression was more comfortable than full compression.