Search

Motivated by the revitalized interest in the digital simulation of medium- and high-energy physics phenomena, we investigate the dynamics following a Yukawa interaction quench on IBM Q. Adopting the zero-dimensional version of the scalar Yukawa coupling model as our point of departure, we design low-depth quantum circuits, emulating its dynamics with up to three bosons. In the one-boson case, we demonstrate circuit compression, i.e., a constant-depth circuit containing only two controlled-NOT (CNOT) gates. In the more complex three-boson case, we design a circuit in which one Trotter step entails eight CNOTs. Using an analogy with the traveling salesman problem, we also provide a CNOT cost estimate for higher boson number truncations. Based on these circuits, we quantify the system dynamics by evaluating the expected boson number at an arbitrary time after the quench and the survival probability of the initial vacuum state (the Loschmidt echo). We also utilize these circuits to drive adiabatic transitions and compute the energies of the ground- and first-excited states of the considered model. Finally, through error mitigation, i.e., zero-noise extrapolation, we demonstrate the good agreement of our results with a numerically exact classical benchmark.

Background: Considering that most men benefit diagnostically from increased sampling of index lesions, limiting systematic biopsy (SBx) to the region around the index lesion could potentially minimize overdetection while maintaining the detection of clinically significant prostate cancer (csPCa). Objective: To evaluate the diagnostic performance of a hypothetical magnetic resonance imaging (MRI)-directed targeted-plus-perilesional biopsy approach. Design, setting, and participants: This single-center, retrospective analysis of prospectively generated data included all biopsy-naïve men with unilateral MRI-positive lesions (Prostate Imaging Reporting and Data System category ≥3), undergoing both MRI-directed targeted biopsies and SBx. Grade group 2–5 cancers were considered csPCa. Outcome measurements and statistical analysis: The diagnostic performance of a targeted-plus-perilesional biopsy approach was compared with that of a targeted-plus-systematic biopsy approach. The primary outcome was the detection of csPCa. Secondary outcomes included the detection of clinically insignificant prostate cancer (ciPCa) and the number of total biopsy cores. Results and limitations: A total of 235 men were included in the analysis; csPCa and ciPCa were detected, respectively, in 95 (40.4%) and 86 (36.6%) of these 235 men. A targeted-plus-perilesional biopsy approach would have detected 92/95 (96.8%; 95% confidence interval [CI] 91.0–99.3%) csPCa cases. At the same time, detection of systematically found ciPCa would be reduced by 11/86 (12.8%; 95% CI 6.6–21.7%). If a targeted-plus-perilesional biopsy approach would have been performed, the number of biopsy cores per patient would have been reduced significantly (a mean difference of 5.2; 95% CI 4.9–5.6, p

Motivated by the revitalized interest in the digital simulation of medium- and high-energy physics phenomena, we investigate the dynamics following a Yukawa interaction quench on IBM Q. Adopting the zero-dimensional version of the scalar Yukawa coupling model as our point of departure, we design low-depth quantum circuits, emulating its dynamics with up to three bosons. In the one-boson case, we demonstrate circuit compression, i.e., a constant-depth circuit containing only two controlled-NOT (CNOT) gates. In the more complex three-boson case, we design a circuit in which one Trotter step entails eight CNOTs. Using an analogy with the traveling salesman problem, we also provide a CNOT cost estimate for higher boson number truncations. Based on these circuits, we quantify the system dynamics by evaluating the expected boson number at an arbitrary time after the quench and the survival probability of the initial vacuum state (the Loschmidt echo). We also utilize these circuits to drive adiabatic transitions and compute the energies of the ground- and first-excited states of the considered model. Finally, through error mitigation, i.e., zero-noise extrapolation, we demonstrate the good agreement of our results with a numerically exact classical benchmark. [ABSTRACT FROM AUTHOR]

Prostate MRI has an important role in prostate cancer diagnosis and treatment, including detection, the targeting of prostate biopsies, staging and guiding radiotherapy and active surveillance. However, there are other ‘’less well-known’’ applications which are being studied and frequently used in our highly specialized medical center. In this review, we focus on two research topics that lie within the expertise of this study group: (1) anatomical parameters predicting the risk of urinary incontinence after radical prostatectomy, allowing more personalized shared decision-making, with special emphasis on the membranous urethral length (MUL); (2) the use of three-dimensional models to help the surgical planning. These models may be used for training, patient counselling, personalized estimation of nerve sparing and extracapsular extension and may help to achieve negative surgical margins and undetectable postoperative PSA values.

With the advent of soft ionization techniques such as electrospray (ESI) and matrix‐assisted laser desorption/ionization (MALDI) to produce intact gas‐phase ions from nonvolatile macromolecules, mass spectrometry has become an essential technique in the field of polymeric materials. However, (co)polymers of very high molecular weight or with reticulated architectures still escape ESI or MALDI, mainly due to solubility issues. Strategies developed to tackle such an analytical challenge all rely on sample degradation to produce low‐mass species amenable to existing ionization methods. Yet, chain degradation needs to be partial and controlled to generate sufficiently large species that still contain topological or architectural information. The present article reviews the different analytical degradation strategies implemented to perform mass spectrometry of these challenging synthetic polymers, covering thermal degradation approaches in sources developed in the 2000s, off‐line sample pre‐treatments for controlled chemical degradation of polymeric substrates, and most recent achievements employing reactive ionization modes to perform chemolysis on‐line with MS. [ABSTRACT FROM AUTHOR]

Abstract In recent years, neoadjuvant chemoradiotherapy (CRT) has become the standard of care for patients with locally advanced rectal cancer. Until recently, patients routinely proceeded to surgical resection after CRT, regardless of the response. Nowadays, treatment is tailored depending on the response to chemoradiotherapy. In patients that respond very well to CRT, organ-preserving treatments such as watch-and-wait are increasingly considered as an alternative to surgery. To facilitate such personalized treatment planning, there is now an increased demand for more detailed radiological response evaluation after chemoradiation. MRI is one of the main tools used to assess response, but has difficulties in assessing response within areas of post-radiation fibrosis. Hence, MR sequences such as diffusion-weighted imaging are increasingly adopted in clinical MR protocols to improve the differentiation between tumor and fibrosis. In this pictorial review, we discuss the strengths and weaknesses of modern MR imaging, including functional imaging sequences such as diffusion-weighted MRI, for response evaluation after chemoradiation treatment and provide the main pearls and pitfalls for image interpretation.

Background: Immune checkpoint inhibitor efficacy in advanced cancer patients remains difficult to predict. Imaging is the only technique available that can non-invasively provide whole body information of a patient's response to treatment. We hypothesize that quantitative whole-body prognostic information can be extracted by leveraging artificial intelligence (AI) for treatment monitoring, superior and complementary to the current response evaluation methods.Methods: To test this, a cohort of 74 stage-IV urothelial cancer patients (37 in the discovery set, 37 in the independent test, 1087 CTs), who received anti-PD1 or anti-PDL1 were retrospectively collected. We designed an AI system [named prognostic AI-monitor (PAM)] able to identify morphological changes in chest and abdominal CT scans acquired during follow-up, and link them to survival.Results: Our findings showed significant performance of PAM in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.73 (p < 0.001) for abdominal imaging, and 0.67 AUC (p < 0.001) for chest imaging. Subanalysis revealed higher accuracy of abdominal imaging around and in the first 6 months of treatment, reaching an AUC of 0.82 (p < 0.001). Similar accuracy was found by chest imaging, 5–11 months after start of treatment. Univariate comparison with current monitoring methods (laboratory results and radiological assessments) revealed higher or similar prognostic performance. In multivariate analysis, PAM remained significant against all other methods (p < 0.001), suggesting its complementary value in current clinical settings.Conclusions: Our study demonstrates that a comprehensive AI-based method such as PAM, can provide prognostic information in advanced urothelial cancer patients receiving immunotherapy, leveraging morphological changes not only in tumor lesions, but also tumor spread, and side-effects. Further investigations should focus beyond anatomical imaging. Prospective studies are warranted to test and validate our findings.

BackgroundCheckpoint inhibitors provided sustained clinical benefit to metastatic lung cancer patients. Nonetheless, prognostic markers in metastatic settings are still under research. Imaging offers distinctive advantages, providing whole-body information non-invasively, while routinely available in most clinics. We hypothesized that more prognostic information can be extracted by employing artificial intelligence (AI) for treatment monitoring, superior to 2D tumor growth criteria.MethodsA cohort of 152 stage-IV non-small-cell lung cancer patients (NSCLC) (73 discovery, 79 test, 903CTs), who received nivolumab were retrospectively collected. We trained a neural network to identify morphological changes on chest CT acquired during patients’ follow-ups. A classifier was employed to link imaging features learned by the network with overall survival.ResultsOur results showed significant performance in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.69 (p < 0.01), up to AUC 0.75 (p < 0.01) in the first 3 to 5 months of treatment, and 0.67 AUC (p = 0.01) for durable clinical benefit (6 months progression-free survival). We found the AI-derived survival score to be independent of clinical, radiological, PDL1, and histopathological factors. Visual analysis of AI-generated prognostic heatmaps revealed relative prognostic importance of morphological nodal changes in the mediastinum, supraclavicular, and hilar regions, lung and bone metastases, as well as pleural effusions, atelectasis, and consolidations.ConclusionsOur results demonstrate that deep learning can quantify tumor- and non–tumor-related morphological changes important for prognostication on serial imaging. Further investigation should focus on the implementation of this technique beyond thoracic imaging.