Purpose To evaluate the associations among mathematical modeling with the use of magnetic resonance (MR) imaging-based texture features and deep myometrial invasion (DMI), lymphovascular space invasion (LVSI), and histologic high-grade endometrial carcinoma. Materials and Methods Institutional review board approval was obtained for this retrospective study. This study included 137 women with endometrial carcinomas measuring greater than 1 cm in maximal diameter who underwent 1.5-T MR imaging before hysterectomy between January 2011 and December 2015. Texture analysis was performed with commercial research software with manual delineation of a region of interest around the tumor on MR images (T2-weighted, diffusion-weighted, and dynamic contrast material-enhanced images and apparent diffusion coefficient maps). Areas under the receiver operating characteristic curve and diagnostic performance of random forest models determined by using a subset of the most relevant texture features were estimated and compared with those of independent and blinded visual assessments by three subspecialty radiologists. Results A total of 180 texture features were extracted and ultimately limited to 11 features for DMI, 12 for LVSI, and 16 for high-grade tumor for random forest modeling. With random forest models, areas under the receiver operating characteristic curve, sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were estimated at 0.84, 79.3%, 82.3%, 81.0%, 76.7%, and 84.4% for DMI; 0.80, 80.9%, 72.5%, 76.6%, 74.3%, and 79.4% for LVSI; and 0.83, 81.0%, 76.8%, 78.1%, 60.7%, and 90.1% for high-grade tumor, respectively. Sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of visual assessment for DMI were 84.5%, 82.3%, 83.2%, 77.7%, and 87.8% (reader 3). Conclusion The mathematical models that incorporated MR imaging-based texture features were associated with the presence of DMI, LVSI, and high-grade tumor and achieved equivalent accuracy to that of subspecialty radiologists for assessment of DMI in endometrial cancers larger than 1 cm. However, these preliminary results must be interpreted with caution until they are validated with an independent data set, because the small sample size relative to the number of features extracted may have resulted in overfitting of the models. RSNA, 2017 Online supplemental material is available for this article.

Purpose To evaluate whether features from texture analysis of breast cancers were associated with pathologic complete response (pCR) after neoadjuvant chemotherapy and to explore the association between texture features and tumor subtypes at pretreatment magnetic resonance (MR) imaging. Materials and Methods Institutional review board approval was obtained. This retrospective study included 85 patients with 85 breast cancers who underwent breast MR imaging before neoadjuvant chemotherapy between April 10, 2008, and March 12, 2015. Two-dimensional texture analysis was performed by using software at T2-weighted MR imaging and contrast material-enhanced T1-weighted MR imaging. Quantitative parameters were compared between patients with pCR and those with non-pCR and between patients with triple-negative breast cancer and those with non-triple-negative cancer. Multiple logistic regression analysis was used to determine independent parameters. Results Eighteen tumors (22%) were triple-negative breast cancers. pCR was achieved in 30 of the 85 tumors (35%). At univariate analysis, mean pixel intensity with spatial scaling factor (SSF) of 2 and 4 on T2-weighted images and kurtosis on contrast-enhanced T1-weighted images showed a significant difference between triple-negative breast cancer and non-triple-negative breast cancer (P = .009, .003, and .001, respectively). Kurtosis (SSF, 2) on T2-weighted images showed a significant difference between pCR and non-pCR (P = .015). At multiple logistic regression, kurtosis on T2-weighted images was independently associated with pCR in non-triple-negative breast cancer (P = .033). A multivariate model incorporating T2-weighted and contrast-enhanced T1-weighted kurtosis showed good performance for the identification of triple-negative breast cancer (area under the receiver operating characteristic curve, 0.834). Conclusion At pretreatment MR imaging, kurtosis appears to be associated with pCR to neoadjuvant chemotherapy in non-triple-negative breast cancer and may be a promising biomarker for the identification of triple-negative breast cancer. RSNA, 2017.

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Breast cancer detection in the early stages is of great importance since the prognosis, and the treatment depends more on this. Multiple techniques relying on the mechanical properties of soft tissues have been developed to help in early detection. In this study, we implemented a technique that measures the nonlinear shear modulus (NLSM) (μ(NL)) in vivo and showed its utility to detect breast lesions from healthy tissue. The technique relies on the acoustoelasticity theory in quasi-incompressible media. In order to recover μ(NL), static elastography and supersonic shear imaging are combined to subsequently register strain maps and shear modulus maps while the medium is compressed. Then, μ(NL) can be recovered from the relationship between the stress, deduced from strain maps, and the shear modulus. For this study, a series of five nonlinear phantoms were built using biological tissue (pork liver) inclusions immersed in an agar-gelatin gel. Furthermore, 11 in vivo acquisitions were performed to characterize the NLSM of breast tissue. The phantom results showed a very good differentiation of the liver inclusions when measuring μ(NL) with a mean value of -114.1 kPa compared to -34.7 kPa for the gelatin. Meanwhile, values for the shear modulus for the liver and the gelatin were very similar, 3.7 and 3.4 kPa, respectively. In vivo NLSM mean value for the healthy breast tissue was of -95 kPa, while mean values of the benign and the malignant lesions were -619 and -806 kPa with a strong v ariability, respectively. This study shows the potential of the acoustoelasticity theory in quasi-incompressible medium to bring a new parameter for breast cancer diagnosis.

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Women diagnosed with high-grade serous ovarian cancers (HGSOC) are still likely to exhibit a bad prognosis, particularly when suffering from HGSOC of the Mesenchymal molecular subtype (50% cases). These tumors show a desmoplastic reaction with accumulation of extracellular matrix proteins and high content of cancer-associated fibroblasts. Using patient-derived xenograft mouse models of Mesenchymal and Non-Mesenchymal HGSOC, we show here that HGSOC exhibit distinct stiffness depending on their molecular subtype. Indeed, tumor stiffness strongly correlates with tumor growth in Mesenchymal HGSOC, while Non-Mesenchymal tumors remain soft. Moreover, we observe that tumor stiffening is associated with high stromal content, collagen network remodeling, and MAPK/MEK pathway activation. Furthermore, tumor stiffness accompanies a glycolytic metabolic switch in the epithelial compartment, as expected based on Warburg’s effect, but also in stromal cells. This effect is restricted to the central part of stiff Mesenchymal tumors. Indeed, stiff Mesenchymal tumors remain softer at the periphery than at the core, with stromal cells secreting high levels of collagens and showing an OXPHOS metabolism. Thus, our study suggests that tumor stiffness could be at the crossroad of three major processes, i.e. matrix remodeling, MEK activation and stromal metabolic switch that might explain at least in part Mesenchymal HGSOC aggressiveness.

Objectives Due to COVID-19, a lockdown took place between March 17 and May 1, 2020, in France. This study evaluates the impact of the lockdown on the diagnosis and staging of breast cancers in a tertiary cancer centre. Methods Our database was searched for all consecutive invasive breast cancers diagnosed in our institution during the lockdown (36 working days), during equivalent periods of 36 working days before and after lockdown and a reference period in 2019. The number and staging of breast cancers diagnosed during and after lockdown were compared to the pre-lockdown and reference periods. Tumour maximum diameters were compared using the Mann–Whitney test. Proportions of tumour size categories (T), ipsilateral axillary lymph node invasion (N) and presence of distant metastasis (M) were compared using Fisher’s exact test. Results Compared to the reference period ( n = 40 in average), the number of breast cancers diagnosed during lockdown ( n = 32) decreased by 20% but increased by 48% after the lockdown ( n = 59). After the lockdown, comparatively to the reference period, breast cancers were more often symptomatic (86% vs 57%; p = 0.001) and demonstrated bigger tumour sizes ( p = 0.0008), the rates of small tumours (T1) were reduced by 38%, locally advanced cancers (T3, T4) increased by 80% and lymph node invasion increased by 64%. Conclusion The COVID-19 lockdown was associated with a 20% decrease in the number of diagnosed breast cancers. Because of delayed diagnosis, breast cancers detected after the lockdown had poorer prognosis with bigger tumour sizes and higher rates of node invasion. Key Points • The number of breast cancer diagnosed in a large tertiary cancer centre in France decreased by 20% during the first COVID-19 lockdown. • Because of delayed diagnosis, breast cancers demonstrated bigger tumour size and more frequent axillary lymph node invasion after the lockdown. • In case of a new lockdown, breast screening programme and follow-up examinations should not be suspended and patients with clinical symptoms should be encouraged to seek attention promptly.

ILC rarely presented as dense masses but frequently demonstrated architectural distortion on DBT. DBT increased lesion conspicuity but failed to accurately assess tumour size of ILC. Advances in knowledge: (1) This study describes specific features of ILC on DBT. (2) It shows that DBT can improve conspicuity of ILC.