Progress in computing power and advances in medical imaging over recent decades have culminated in new opportunities for artificial intelligence (AI), computer vision, and using radiomics to facilitate clinical decision-making. These opportunities are growing in medical specialties, such as radiology, pathology, and oncology. As medical imaging and pathology are becoming increasingly digitized, it is recently recognized that harnessing data from digital images can yield parameters that reflect the underlying biology and physiology of various malignancies. This greater understanding of the behaviour of cancer can potentially improve on therapeutic strategies. In addition, the use of AI is particularly appealing in oncology to facilitate the detection of malignancies, to predict the likelihood of tumor response to treatments, and to prognosticate the patients' risk of cancer-related mortality. AI will be critical for identifying candidate biomarkers from digital imaging and developing robust and reliable predictive models. These models will be used to personalize oncologic treatment strategies, and identify confounding variables that are related to the complex biology of tumors and diversity of patient-related factors (ie, mining ''big data''). This commentary describes the growing body of work focussed on AI for precision oncology. Advances in AI-driven

Contrast-medium-enhanced digital mammography (CEDM) is an image subtraction technique which might help unmasking lesions embedded in very dense breasts. Previous works have stated the feasibility of CEDM and the imperative need of radiological optimization. This work presents an extension of a former analytical formalism to predict contrast-to-noise ratio (CNR) in subtracted mammograms. The goal is to optimize radiological parameters available in a clinical mammographic unit (x-ray tube anode/filter combination, voltage, and loading) by maximizing CNR and minimizing total mean glandular dose (D(gT)), simulating the experimental application of an iodine-based contrast medium and the image subtraction under dual-energy nontemporal, and single- or dual-energy temporal modalities. Total breast-entrance air kerma is limited to a fixed 8.76 mGy (1 R, similar to screening studies). Mathematical expressions obtained from the formalism are evaluated using computed mammographic x-ray spectra attenuated by an adipose/glandular breast containing an elongated structure filled with an iodinated solution in various concentrations. A systematic study of contrast, its associated variance, and CNR for different spectral combinations is performed, concluding in the proposal of optimum x-ray spectra. The linearity between contrast in subtracted images and iodine mass thickness is proven, including the determination of iodine visualization limits based on Rose's detection criterion. Finally, total breast-entrance air kerma is distributed between both images in various proportions in order to maximize the figure of merit CNR2/D(gT). Predicted results indicate the advantage of temporal subtraction (either single- or dual-energy modalities) with optimum parameters corresponding to high-voltage, strongly hardened Rh/Rh spectra. For temporal techniques, CNR was found to depend mostly on the energy of the iodinated image, and thus reduction in D(gT) could be achieved if the spectral energy of the noniodinated image is decreased and the breast-entrance air kerma is evenly distributed between both acquisitions. Predicted limits, in terms of iodine concentration, are found to guarantee the visualization of common clinical angiogenic concentrations in the breast.

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This manuscript reports preliminary results obtained by combining estimates of two or three (among seven) quantitative ultrasound (QUS) parameters in a model-free, multi-parameter classifier to differentiate breast carcinomas from fibroadenomas (the most common benign solid tumor). Forty-three subjects scheduled for core biopsy of a suspicious breast mass were recruited. Radiofrequency echo signal data were acquired using clinical breast ultrasound systems equipped with linear array transducers. The Reference Phantom Method was used to obtain systemindependent estimates of the specific attenuation (ATT), the average backscatter coefficients (ABSC), the effective scatterer diameter (ESD) and an effective scatterer diameter heterogeneity index (ESDHI) over regions of interest within each mass. In addition, the envelope amplitude signal-to-noise ratio (SNR), the Nakagami shape parameter, m, and the maximum collapsed average (maxCA) of the generalized spectrum were also computed. Classification was performed using the minimum Mahalanobis distance to the centroids of the training classes and tested against biopsy results. Classification performance was evaluated with the area under the receiver-operating characteristic (ROC) curve. The best performance with a two-parameter classifier used the ESD and ESDHI and resulted in an area under the ROC curve of 0.98 [0.95-1.00, 95% confidence interval]. Classification performance improved with three parameters (ATT, ESD, and ESDHI) yielding an area under the ROC curve of 0.999 [0.995-1.000].These results suggest that system independent QUS parameters, when combined in a model-free classifier, are a promising tool to characterize breast tumors. A larger study is needed to further test this idea.

One of the main limitations of ultrasound imaging is that image quality and interpretation depend on the skill of the user and the experience of the clinician. Quantitative ultrasound (QUS) methods provide objective, system-independent estimates of tissue properties, such as acoustic attenuation and backscattering properties of tissue, which are valuable as objective tools for both diagnosis and intervention. Accurate and precise estimation of these properties requires correct compensation for intervening tissue attenuation. Prior attempts to estimate intervening-tissue attenuation based on minimizing cost functions that compared backscattered echo data to models have resulted in limited precision and accuracy. To overcome these limitations, in this paper, we incorporate the prior information of piecewise continuity of QUS parameters as a regularization term into our cost function. We further propose to calculate this cost function using dynamic programming (DP), a computationally efficient optimization algorithm that finds the global optimum. Our results on tissue-mimicking phantoms show that DP substantially outperforms a published least squares method in terms of both estimation bias and variance.

We report here the results of a longitudinal study of cervix stiffness during pregnancy. Thirty women, ages ranging from 19 to 37 years, were scanned with ultrasound at five time points beginning at their normal first-trimester screening (8–13 weeks) through term pregnancy (nominally 40 week) using a clinical ultrasound imaging system modified with a special ultrasound transducer and system software. The system estimated the shear wave speed (its square proportional to the shear modulus under idealized conditions) in the cervix. We found a constant fractional reduction (about 4% per week) in shear wave speed with increasing gestational age. We also demonstrated a spatial gradient in shear wave speed along the length of the cervix (softest at the distal end). Results were consistent with our previous ex vivo and in vivo work in women. Shear wave elasticity imaging may be a potentially useful clinical tool for objective assessment of cervical softening in pregnancy.

As pregnancy progresses, the cervix remodels from a rigid structure to one compliant enough to allow delivery of a fetus, a process which involves progressive disorganization of cervical microstructure. Quantitative ultrasound biomarkers that may detect this process include those derived from the backscattered echo signal, namely, acoustic attenuation and backscattered power loss. We have recently shown that attenuation and backscattered power loss are affected by tissue anisotropy and heterogeneity in the ex vivo cervix. In this study, we compared attenuation and backscattered power difference in a group of women in early (first trimester), to a group in late (third trimester), pregnancy. We found a significant decrease in the backscattered power difference in late as compared to early pregnancy, suggesting decreased microstructural organization in late pregnancy, a finding that is consistent with animal models of cervical remodeling. In contrast, we found no difference in attenuation between the timepoints. These results suggest that the backscattered power difference, but perhaps not attenuation, may be a useful clinical biomarker of cervical remodeling.

Imaging biomarkers based on quantitative ultrasound can offer valuable information about properties that inform tissue function and behavior such as microstructural organization (e.g., collagen alignment) and viscoelasticity (i.e., compliance). For example, the cervix feels softer as its microstructure remodels during pregnancy, an increase in compliance that can be objectively quantified with shear wave speed and therefore shear wave speed estimation is a potential biomarker of cervical remodeling. Other proposed biomarkers include parameters derived from the backscattered echo signal, such as attenuation and backscattered power loss, because such parameters can provide insight into tissue microstructural alignment and organization. Of these, attenuation values for the pregnant cervix have been reported, but large estimate variance reduces their clinical value. That said, parameter estimates based on the backscattered echo signal may be incorrect if assumptions they rely on, such as tissue isotropy and homogeneity, are violated. For that reason, we explored backscatter and attenuation parameters as potential biomarkers of cervical remodeling via careful investigation of the assumptions of isotropy and homogeneity in cervical tissue. Specifically, we estimated the angle- and spatial-dependence of parameters of backscattered power and acoustic attenuation in the ex vivo human cervix, using the reference phantom method and electronic steering of the ultrasound beam. We found that estimates are anisotropic and spatially heterogeneous, presumably because the tissue itself is anisotropic and heterogeneous. We conclude that appropriate interpretation of imaging biomarkers of cervical remodeling must account for tissue anisotropy and heterogeneity.