Two-photon sideband transition in a driven quantum Rabi model : Quantitative discussions with derived longitudinal drives and beyond the rotating wave approximation

In this work, we analytically and numerically study the sideband transition dynamics of the driven quantum Rabi model (QRM). We focus in particular on the conditions when the external transverse drive fields induce first-order sideband transitions. Inducing sideband transitions between two different systems is an essential technique for various physical models, including the QRM. However, despite its importance, a precise analytical study has not been reported yet that successfully explains the sideband transition rates in a driven QRM applicable for all system parameter configurations. In our study, we analytically derive the sideband transition rates based on second-order perturbation theory, not relying on the rotating wave approximation (RWA) \cite{RWA}. Our formula are valid for all ranges of drive frequencies and system's parameters. Our analytical derived formula agrees well with the numerical results in a regime of moderate drive amplitudes. Interestingly, we have found a non-trivial longitudinal drive effect derived from the transverse drive Hamiltonian. This accounts for significant corrections to the sideband transition rates that are expected without considering the derived longitudinal effect. Using this approach, one can precisely estimate the sideband transition rates in the QRM not confining themselves within specific parameter regimes. This provides important contributions for understanding experiments described by the driven QRM

Adaptive FDG-PET/CT guided dose escalation in head and neck squamous cell carcinoma: Late toxicity and oncologic outcomes (The ADMIRE study)

PURPOSE: To report on the late toxicity and local control (LC) of head and neck cancer patients treated with adaptive FDG-PET/CT response-guided radiotherapy (ADMIRE) with dose escalation (NCT03376386). MATERIALS AND METHODS: Between December 2017 and April 2019, 20 patients with stage II-IV squamous cell carcinoma of the larynx, hypopharynx or oropharynx were treated within the ADMIRE study where FDG-PET/CT response-guided (Week 2&4) dose escalation was applied (total dose 70-78 Gy). Cisplatin or cetuximab was added to radiotherapy in case of T3-4 and/or N2c disease. To compare the LC and late toxicity of the study population, we used an external control group (n = 67) consisting of all eligible patients for the study (but not participated). These patients were treated in our institution during the same period with the current standard of 70 Gy radiotherapy. To reduce the effect of confounding, logistic regression analyses was done using stabilized inverse probability of treatment weighting (SIPTW). RESULTS: After median follow-up of 40 and 43 months for the ADMIRE and control groups, the 3-year LC-rates were 74% and 78%, respectively (adjusted HR after SIPTW 0.80, 95 %CI 0.25-2.52, p = 0.70). The incidences of any late G3 toxicity were 35% and 18%, respectively. The adjusted OR for any late G3 toxicity was 5.09 (95 %CI 1.64-15.8, p = 0.005), for any late G ≥ 2 toxicity was 3.67 (95 %CI 1.2-11.7, p = 0.02), for persistent laryngeal edema was 10.95 (95% CI 2.71-44.29, p = 0.001), for persistent mucosal ulcers was 4.67 (95% CI 1.23-17.7, p = 0.023), and for late G3 radionecrosis was 15.69 (95 %CI 2.43-101.39, p = 0.004). CONCLUSION: Given the comparable LC rates with increased late toxicity in the ADMIRE group, selection criteria for future adaptive dose escalation trials (preferably randomized) need to be refined to include only patients at higher risk of local failure and/or lower risk of severe late toxicity

Spatial and nonspatial implicit motor learning in Korsakoff’s amnesia: evidence for selective deficits

Patients with amnesia have deficits in declarative memory but intact memory for motor and perceptual skills, which suggests that explicit memory and implicit memory are distinct. However, the evidence that implicit motor learning is intact in amnesic patients is contradictory. This study investigated implicit sequence learning in amnesic patients with Korsakoff’s syndrome (N = 20) and matched controls (N = 14), using the classical Serial Reaction Time Task and a newly developed Pattern Learning Task in which the planning and execution of the responses are more spatially demanding. Results showed that implicit motor learning occurred in both groups of participants; however, on the Pattern Learning Task, the percentage of errors did not increase in the Korsakoff group in the random test phase, which is indicative of less implicit learning. Thus, our findings show that the performance of patients with Korsakoff’s syndrome is compromised on an implicit learning task with a strong spatial response component

Oncologic outcomes of screen-detected and non-screen-detected T1 colorectal cancers

Background:The incidence of T1 colorectal cancer (CRC) has increased with the implementation of CRC screening programs. It is unknown whether the outcomes and risk models for T1 CRC based on non-screen-detected patients can be extrapolated to screen-detected T1 CRC. This study aimed to compare the stage distribution and oncologic outcomes of T1 CRC patients within and outside the screening program. Methods: Data from T1 CRC patients diagnosed between 2014 and 2017 were collected from 12 hospitals in the Netherlands. The presence of lymph node metastasis (LNM) at diagnosis was compared between screen-detected and non-screen-detected patients using multivariable logistic regression. Cox proportional hazard regression was used to analyze differences in the time to recurrence (TTR), metastasis-free survival (MFS), cancer-specific survival (CSS), and overall survival. Additionally, the performance of conventional risk factors for LNM was evaluated across the groups. Results: 1803 patients were included (1114 [62%] screendetected), with median follow-up of 51 months (interquartile range 30). The proportion of LNM did not significantly differ between screen- and non-screen-detected patients (12.6% vs. 8.9%; odds ratio 1.41; 95%CI 0.89-2.23); a prediction model for LNM performed equally in both groups. The 3- and 5-year TTR, MFS, and CSS were similar for patients within and outside the screening program. However, overall survival was significantly longer in screen-detected T1 CRC patients (adjusted hazard ratio 0.51; 95%CI 0.38- 0.68). Conclusions: Screen-detected and non-screen-detected T1 CRCs have similar stage distributions and oncologic outcomes and can therefore be treated equally. However, screen-detected T1 CRC patients exhibit a lower rate of non-CRC-related mortality, resulting in longer overall survival.</p

Reproducible radiomics through automated machine learning validated on twelve clinical applications

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, findingthe optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-anderror process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows perapplication. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms foreach component. To optimize the workflow per application, we employ automated machine learning using a random search andensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1)liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77);5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis(0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer’s disease (0.87); and 12) head and neck cancer (0.84). Weshow that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performssimilar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automaticallyoptimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications.To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework,and the code to reproduce this study

Oncologic outcomes of screen-detected and non-screen-detected T1 colorectal cancers

Background The incidence of T1 colorectal cancer (CRC) has increased with the implementation of CRC screening programs. It is unknown whether the outcomes and risk models for T1 CRC based on non-screen-detected patients can be extrapolated to screen-detected T1 CRC. This study aimed to compare the stage distribution and oncologic outcomes of T1 CRC patients within and outside the screening program. Methods Data from T1 CRC patients diagnosed between 2014 and 2017 were collected from 12 hospitals in the Netherlands. The presence of lymph node metastasis (LNM) at diagnosis was compared between screen-detected and non-screen-detected patients using multivariable logistic regression. Cox proportional hazard regression was used to analyze differences in the time to recurrence (TTR), metastasis-free survival (MFS), cancer-specific survival (CSS), and overall survival. Additionally, the performance of conventional risk factors for LNM was evaluated across the groups. Results 1803 patients were included (1114 [62%] screendetected), with median follow-up of 51 months (interquartile range 30). The proportion of LNM did not significantly differ between screen- and non-screen-detected patients (12.6% vs. 8.9%; odds ratio 1.41; 95%CI 0.89-2.23); a prediction model for LNM performed equally in both groups. The 3- and 5-year TTR, MFS, and CSS were similar for patients within and outside the screening program. However, overall survival was significantly longer in screen-detected T1 CRC patients (adjusted hazard ratio 0.51; 95%CI 0.38- 0.68). Conclusions Screen-detected and non-screen-detected T1 CRCs have similar stage distributions and oncologic outcomes and can therefore be treated equally. However, screen-detected T1 CRC patients exhibit a lower rate of non-CRC-related mortality, resulting in longer overall survival

Impact of ≥ 0.1-mm free resection margins on local intramural residual cancer after local excision of T1 colorectal cancer

Background and study aims  A free resection margin (FRM) > 1 mm after local excision of a T1 colorectal cancer (CRC) is known to be associated with a low risk of local intramural residual cancer (LIRC). The risk is unclear, however, for FRMs between 0.1 to 1 mm. This study evaluated the risk of LIRC after local excision of T1 CRC with FRMs between 0.1 and 1 mm in the absence of lymphovascular invasion (LVI), poor differentiation and high-grade tumor budding (Bd2-3). Patients and methods  Data from all consecutive patients with local excision of T1 CRC between 2014 and 2017 were collected from 11 hospitals. Patients with a FRM ≥ 0.1 mm without LVI and poor differentiation were included. The main outcome was risk of LIRC (composite of residual cancer in the local excision scar in adjuvant resection specimens or local recurrence during follow-up). Tumor budding was also assessed for cases with a FRM between 0.1 and 1mm. Results  A total of 171 patients with a FRM between 0.1 and 1 mm and 351 patients with a FRM > 1 mm were included. LIRC occurred in five patients (2.9 %; 95 % confidence interval [CI] 1.0-6.7 %) and two patients (0.6 %; 95 % CI 0.1-2.1 %), respectively. Assessment of tumor budding showed Bd2-3 in 80 % of cases with LIRC and in 16 % of control cases. Accordingly, in patients with a FRM between 0.1 and 1 mm without Bd2-3, LIRC was detected in one patient (0.8%; 95 % CI 0.1-4.4 %). Conclusions  In this study, risks of LIRC were comparable for FRMs between 0.1 and 1 mm and > 1 mm in the absence of other histological risk factors

Two-photon sideband interaction in a driven quantum Rabi model: Quantitative discussions with derived longitudinal drives and beyond the rotating wave approximation

In this paper, we analytically and numerically study the sideband interaction dynamics of the driven quantum Rabi model (QRM). We focus in particular on the conditions when the external transverse drive fields induce first-order sideband interactions. Inducing sideband interactions between two different systems is an essential technique for various physical models, including the QRM. However, despite its importance, a precise analytical study has not been reported yet that successfully explains the sideband interaction rates in a driven QRM applicable for all system parameter configurations. In our paper, we analytically derive the sideband interaction rates based on second-order perturbation theory, not relying on the rotating wave approximation (RWA). Our formula are valid for all ranges of drive frequencies and system's parameters. Our analytical derived formula agrees well with the numerical results in a regime of moderate drive amplitudes. Interestingly, we have found a nontrivial longitudinal drive effect derived from the transverse drive Hamiltonian. This accounts for significant corrections to the sideband interaction rates that are expected without considering the derived longitudinal effect. Using this approach, one can precisely estimate the sideband interaction rates in the QRM not confining themselves within specific parameter regimes for moderate drive amplitudes. This provides important contributions for quantitatively understanding experiments described by the driven QRM.QN/Steele La