Citations

Background Immune checkpoint inhibitors (ICI) improve cancer outcomes but are associated with increased atherosclerotic cardiovascular risk. Opportunistic coronary artery calcium (CAC) assessment from routine non-gated computed tomography (CT) using artificial intelligence (AI) may enable rapid cardiovascular risk stratification in this population. We evaluated the association between AI-derived CAC (AI-CAC) scores and 1-year myocardial infarction (MI) and all-cause mortality in patients receiving ICI therapy. Methods We conducted a retrospective multicenter study across three Mayo Clinic sites including patients who underwent chest CT within 3 months prior to ICI initiation between 2011 and 2022. Patients with prior coronary revascularization were excluded. AI-CAC scores derived from non-gated CT scans were categorized as 0, 1–99, 100–399, and ≥400. Cox regression analyses assessed associations with 1-year MI and all-cause mortality. Results Among 1,873 patients (mean age 66 years; 43% women), lung cancer was most common (50.3%), and 91% had at least one cardiovascular risk factor. Higher CAC burden was associated with increased 1-year MI risk (overall p=0.0032). AI-CAC was not associated with all-cause mortality (p=0.79), likely reflecting high cancer-related mortality (50.7% at 1 year). Only 15% of patients were receiving statins despite one-third having CAC >100. Conclusions Opportunistic AI-CAC assessment from routine CT scans provides modest prognostic value for MI risk among patients receiving ICI therapy and may identify opportunities to optimize preventive cardiovascular care. Elevated CAC should prompt risk factor modification rather than influence ICI treatment decisions. Prospective studies are warranted to evaluate clinical implementation and outcomes.

Abstract Aims Radiation therapy (RT) is an integral component of cancer therapy but associated with adverse events. Our goal was to establish risk prediction models for major adverse cardiovascular and cerebrovascular events (MACCE) after chest RT. Methods and results A retrospective study of lung/breast cancer patients who had chest RT with planning CT at Mayo Clinic between 01/2010 and 01/2014. Predictive models were developed based on weighted independent predictors using a derivation (406 lung and 711 breast cancer) and validation cohort (179 lung and 234 breast cancer). Patient characteristics, pre-RT CT for coronary artery calcification (CAC), and post-RT MACCE data were reviewed. Post-RT MACCE occurred in 6.1 and 5.6% in the derivation and validation cohort over a mean follow-up of 42 ± 13 months. Post-therapy model (C2AD2) included CAC (two points), MACCE history (two points), age ≥74 (three points), DM (two points), and mean heart radiation dose ≥ 850 mGy (two points), and pre-therapy model (C2AD) included post-therapy model parameters minus mean heart radiation dose. Both models stratified patients into three risk groups: low (0–2), intermediate (3–5), and high (≥6). Post-RT MACCE across these groups were 2.7, 8.9, and 19.8% in the derivation, and 3.9, 6.6, and 16.4% in the validation cohort for post-therapy model (C2AD2) and 2.8, 9.2, and 20.4% in the derivation and 3.7, 9.2, and 13.2% in the validation cohort for pre-therapy model. Both models showed statistically significant graded survival outcome. Conclusion Post-therapy (C2AD2) and pre-therapy (C2AD) models are simple, easy to use and effective tools to stratify breast and lung cancer patients undergoing chest radiation for post-RT MACCE.

Cardiovascular pathology is the leading cause of death and disability in the Western world, and current diagnostic testing usually evaluates the anatomy of the vessel to determine if the vessel contains blockages and plaques. However, there is a growing school of thought that other measures, such as wall shear stress, provide more useful information for earlier diagnosis and prediction of atherosclerotic related disease compared to pulsed-wave Doppler ultrasound, magnetic resonance angiography, or computed tomography angiography. A novel algorithm for quantifying wall shear stress (WSS) in atherosclerotic plaque using diagnostic ultrasound imaging, called Multifrequency ultrafast Doppler spectral analysis (MFUDSA), is presented. The development of this algorithm is presented, in addition to its optimisation using simulation studies and in-vitro experiments with flow phantoms approximating the early stages of cardiovascular disease. The presented algorithm is compared with commonly used WSS assessment methods, such as standard PW Doppler, Ultrafast Doppler, and Parabolic Doppler, as well as plane-wave Doppler. Compared to an equivalent processing architecture with one-dimensional Fourier analysis, the MFUDSA algorithm provided an increase in signal-to-noise ratio (SNR) by a factor of 4–8 and an increase in velocity resolution by a factor of 1.10–1.35. The results indicated that MFUDSA outperformed the others, with significant differences detected between the typical WSS values of moderate disease progression (p = 0.003) and severe disease progression (p = 0.001). The algorithm demonstrated an improved performance for the assessment of WSS and has potential to provide an earlier diagnosis of cardiovascular disease than current techniques allow.

In this paper we consider radar approaches for breast cancer detection. The aim is to give a brief review of the main features of incoherent methods, based on beam-forming and Multiple SIgnal Classification (MUSIC) algorithms, that we have recently developed, and to compare them with classical coherent beam-forming. Those methods have the remarkable advantage of not requiring antenna characterization/compensation, which can be problematic in view of the close (to the breast) proximity set-up usually employed in breast imaging. Moreover, we proceed to an experimental validation of one of the incoherent methods, i.e., the I-MUSIC, using the multimodal breast phantom we have previously developed. While in a previous paper we focused on the phantom manufacture and characterization, here we are mainly concerned with providing the detail of the reconstruction algorithm, in particular for a new multi-step clutter rejection method that was employed and only barely described. In this regard, this contribution can be considered as a completion of our previous study. The experiments against the phantom show promising results and highlight the crucial role played by the clutter rejection procedure.

AbstractIt is important to employ radiation dose reduction techniques in pediatric computed tomography (CT) to reduce potential risks of radiation‐induced malignancy. Automatic tube potential (kV) selection tools have been developed and become available on many CT scanners, which select the optimum kV based on the patient size and clinical task to improve the radiation dose efficiency. However, its use in pediatric CT has been mostly empirical, following manufacturer’s default recommendation without solid demonstration for quality improvement. This study aimed to implement an automatic tube potential tool (CAREkV, Siemens Healthcare) into routine pediatric CT practice, using the “Plan‐Do‐Study‐Act” quality improvement process, in place of an existing kV/mAs technique chart. The design of this quality improvement project involved Plan‐Do‐Study‐Act stages. Plan and Do stages identified the criteria for optimal automatic kV selection; a range of phantoms representing typical pediatric groups were scanned on a dual‐source 128‐slice scanner using a fast‐pitch scanning mode. The identified CAREkV settings were implemented into the CT protocol and evaluated after a 6‐month period. In the Study stage, an objective evaluation of the image metrics and radiation dose for two similar patient cohorts using CAREkV and the technique‐chart, respectively, were compared. The kV selected, image quality and radiation dose determined by CAREkV were comparable to those obtained while using the technique‐chart. The CAREkV was successfully implemented into our pediatric abdominopelvic CT practice. By utilizing the “PDSA” process optimal image quality and radiation dose reduction were achieved with an automatic kV selection tool to improve CT workflow.

The aim of this work was to develop multimodal anthropomorphic breast phantoms suitable for evaluating the imaging performance of a recently-introduced Microwave Imaging (MWI) technique in comparison to the established diagnostic imaging modalities of Magnetic Resonance Imaging (MRI), Ultrasound (US), mammography and Computed Tomography (CT). MWI is an emerging technique with significant potential to supplement established imaging techniques to improve diagnostic confidence for breast cancer detection. To date, numerical simulations have been used to assess the different MWI scanning and image reconstruction algorithms in current use, while only a few clinical trials have been conducted. To bridge the gap between the numerical simulation environment and a more realistic diagnostic scenario, anthropomorphic phantoms which mimic breast tissues in terms of their heterogeneity, anatomy, morphology, and mechanical and dielectric characteristics, may be used. Key in this regard is achieving realism in the imaging appearance of the different healthy and pathologic tissue types for each of the modalities, taking into consideration the differing imaging and contrast mechanisms for each modality. Suitable phantoms can thus be used by radiologists to correlate image findings between the emerging MWI technique and the more familiar images generated by the conventional modalities. Two phantoms were developed in this study, representing difficult-to-image and easy-to-image patients: the former contained a complex boundary between the mammary fat and fibroglandular tissues, extracted from real patient MRI datasets, while the latter contained a simpler and less morphologically accurate interface. Both phantoms were otherwise identical, with tissue-mimicking materials (TMMs) developed to mimic skin, subcutaneous fat, fibroglandular tissue, tumor and pectoral muscle. The phantoms’ construction used non-toxic materials, and they were inexpensive and relatively easy to manufacture. Both phantoms were scanned using conventional modalities (MRI, US, mammography and CT) and a recently introduced MWI radar detection procedure called in-coherent Multiple Signal Classification (I-MUSIC). Clinically realistic artifact-free images of the anthropomorphic breast phantoms were obtained using the conventional imaging techniques as well as the emerging technique of MWI.

PurposeThe aim of this study was to demonstrate that a low helical pitch causes increased photon starvation artifacts at ultra‐low‐dose CT.MethodsA cylindrical water phantom with a diameter of 30 cm was scanned on two different generation CT scanners: a 64‐slice scanner (Sensation 64, Siemens Healthcare) and a 192‐slice scanner (Somatom Force, Siemens Healthcare) at multiple effective mAs levels (mAs/pitch = 200, 100, 50, 25, and 12). The corresponding CTDIvol values were 4.1, 2.0, 1.0, 0.5 mGy, on the 64‐slice scanner and 3.8, 1.9, 1.0, 0.5 mGy on the 192‐slice scanner, for the selected effective mAs values. For each dose setting, the scan was repeated at four helical pitches: 1.2, 0.9, 0.6, and the lowest achievable pitch on each scanner. The tube current was automatically adjusted by the scanner so that the effective mAs, and thus CTDIvol, were kept the same for different pitches. All CT data sets were reconstructed with a slice thickness of 3mm and a medium smooth kernel. Images acquired at the same dose level but different helical pitches were visually inspected to assess photon starvation artifacts and noise levels.ResultsAt the same radiation dose, image noise increased with the decreasing helical pitch. The increase was more severe on the old‐generation 64‐slice scanner. Photon starvation artifacts were evident at 200 effective mAs on the 64‐slice scanner at 80 kV. On the 192‐slice scanner there was no visible photon starvation artifacts at both 200 and 50 effective mAs (CTDIvol = 4.1 mGy and 1.0 mGy, respectively); nor was there a visible impact from the lower helical pitch. Only when the dose was lowered to be extremely low (~0.26 mGy, achievable at 70 kV), did photon starvation artifacts become evident.ConclusionsA low helical pitch may increase image noise and photon starvation artifacts compared to a higher pitch for the same dose level, particularly at ultra‐low dose CT.

AbstractPurposeIt is unclear if a 3D transducer with the special design of mechanical swing or 2D array could provide acceptable 2D grayscale image quality for the general diagnosis purpose. The aim of this study is to compare the 2D image quality of a 3D intracavitary transducer with a conventional 2D intracavitary transducer using clinically relevant phantom experiments.MethodsAll measurements were performed on a GE Logiq E9 scanner with both a 2D (IC5‐9‐D) and a 3D (RIC5‐9‐D) transducer used in 2D mode. Selection of phantom targets and acquisition parameters were determined from analysis of 33 clinical pelvic exams. Depth of penetration (DOP), contrast response, contrast of anechoic cylinders (diameter: 6.7 mm) at 1.5 and 4.5 cm depths in transverse planes, and in‐plane resolution represented by full‐width half‐maximum of pin targets at multiple depths were measured with transmit frequencies of 7 and 8 MHz. Spherical signal‐noise‐ratio (SNR) (diameter: 4 and 2 mm) at multiple depths were measured at 8 MHz.ResultsRIC5‐9‐D demonstrated <8% decrease in DOP for both transmit frequencies (7 MHz: 69.7 ± 8.2 mm; 8 MHz: 64.3 ± 7.8 mm) compared with those from IC5‐9‐D (7 MHz: 73.9 ± 4.4 mm; 8 MHz: 69.4 ± 7.8 mm). A decreased anechoic contrast was observed with a 4.5 cm depth for RIC5‐9‐D (7 MHz: 23.2 ± 1.8 dB, P > 0.05; 8 MHz: 17.7 ± 0.9 dB, P < 0.01) compared with IC5‐9‐D (7 MHz: 25.9 ± 1.2 dB; 8 MHz: 21.5 ± 0.8 dB). The contrast response and spatial resolution performance were comparable between the two transducers. RIC5‐9‐D showed comparable SNR of anechoic spheres compared to IC5‐9‐D.Conclusions2D images from a 3D probe exhibited comparable overall image quality for routine clinical pelvic imaging.

This comparative review examines the efficacy of different imaging methods to detect and quantify renal artery stenosis (RAS). Detection of RAS is important because it can lead to renovascular hypertension which is the most common form of secondary hypertension. Furthermore, it is important that a RAS is detected as early as possible as it is a potentially correctable cause of hypertension. 1 If detected at an early stage RAS can potentially be treated using a minimally invasive drug treatment regimen rather than the more invasive percutaneous transluminal renal angioplasty without or with stent placement or surgery. Currently there are a number of different types of modalities used to image the renal artery and determine whether a stenosis is present. Each of these imaging modalities has its own positive and negative aspects, which will be discussed in turn.