Pulmonary MRI with ultra-short TE using single- and dual-echo methods: comparison of capability for quantitative differentiation of non- or minimally invasive adenocarcinomas from other lung cancers with that of standard-dose thin-section CT.

OBJECTIVE: The purpose of this study was thus to compare capabilities for quantitative differentiation of non- and minimally invasive adenocarcinomas from other of pulmonary MRIs with ultra-short TE (UTE) obtained with single- and dual-echo techniques (UTE-MRISingle and UTE-MRIDual) and thin-section CT for stage IA lung cancer patients. METHODS: Ninety pathologically diagnosed stage IA lung cancer patients who underwent thin-section standard-dose CT, UTE-MRISingle, and UTE-MRIDual, surgical treatment and pathological examinations were included in this retrospective study. The largest dimension (Dlong), solid portion (solid Dlong), and consolidation/tumor (C/T) ratio of each nodule were assessed. Two-tailed Student's t-tests were performed to compare all indexes obtained with each method between non- and minimally invasive adenocarcinomas and other lung cancers. Receiver operating characteristic (ROC)-based positive tests were performed to determine all feasible threshold values for distinguishing non- or minimally invasive adenocarcinoma (MIA) from other lung cancers. Sensitivity, specificity, and accuracy were then compared by means of McNemar's test. RESULTS: Each index showed significant differences between the two groups (p < 0.0001). Specificities and accuracies of solid Dlong for UTE-MRIDual2nd echo and CTMediastinal were significantly higher than those of solid Dlong for UTE-MRISingle and UTE-MRIDual1st echo and all C/T ratios except CTMediastinal (p < 0.05). Moreover, the specificities and accuracies of solid Dlong and C/T ratio were significantly higher than those of Dlong for each method (p < 0.05). CONCLUSION: Pulmonary MRI with UTE is considered at least as valuable as thin-section CT for quantitative differentiation of non- and minimally invasive adenocarcinomas from other stage IA lung cancers. CLINICAL RELEVANCE STATEMENT: Pulmonary MRI with UTE's capability for quantitative differentiation of non- and minimally invasive adenocarcinomas from other lung cancers in stage IA lung cancer patients is equal or superior to that of thin-section CT. KEY POINTS: • Correlations were excellent for pathologically examined nodules with the largest dimensions (Dlong) and a solid component (solid Dlong) for all indexes (0.95 ≤ r ≤ 0.99, p < 0.0001). • Pathologically examined Dlong and solid Dlong obtained with all methods showed significant differences between non- and minimally invasive adenocarcinomas and other lung cancers (p < 0.0001). • Solid tumor components are most accurately measured by UTE-MRIDual2nd echo and CTMediastinal, whereas the ground-glass component is imaged by UTE-MRIDual1st echo and CTlung with high accuracy. UTE-MRIDual predicts tumor invasiveness with 100% sensitivity and 87.5% specificity at a C/T threshold of 0.5.

Iridium-Catalyzed [2 + 2 + 2] Cycloaddition of Bithiophen-Linked Diynes with Nitriles: Scope and Mechanistic Study with Quantum Chemical Calculation.

We report a simple and atom-efficient method for the synthesis of bithiophene-fused isoquinolines by iridium-catalyzed [2 + 2 + 2] cycloaddition of bithiophene-linked diynes with nitriles. All three structural isomers of bithiophene-linked diynes underwent [2 + 2 + 2] cycloaddition, and the trend in the reactivity for cycloaddition was diyne 1 = diyne 3 > diyne 2. Dibenzothiophene-linked diyne also reacted with nitriles to form a variety of cycloadducts. Cycloaddition of bithiophene-linked diynes with alkynes and an isocyanate formed naphthodithiophenes and a 2-pyridone derivative, respectively. Cycloadducts bearing a 2-aminopyridine moiety and benzothiophene rings showed intense fluorescence at around 530 nm and gave a fluorescence quantum yield of 0.44. Furthermore, quantum chemical calculations provided insight into the origin of the difference in reactivity of three bithiophene-linked diynes. The different reactivities of the three diynes 1-3 are believed to originate from the step where an iridacyclopentadiene reacts with a coordinated nitrile to form azairidabicyclo[3.2.0]heptatriene. HOMOs of iridacyclopentadiene play a decisive role in this step.

Excitation configuration analysis for divide-and-conquer excited-state calculation method using dynamical polarizability.

The authors previously developed a divide-and-conquer (DC)-based non-local excited-state calculation method for large systems using dynamical polarizability [Nakai and Yoshikawa, J. Chem. Phys. 146, 124123 (2017)]. This method evaluates the excitation energies and oscillator strengths using information on the dynamical polarizability poles. This article proposes a novel analysis of the previously developed method to obtain further configuration information on excited states, including excitation and de-excitation coefficients of each excitation configuration. Numerical applications to simple molecules, such as ethylene, hydrogen molecule, ammonia, and pyridazine, confirmed that the proposed analysis could accurately reproduce the excitation and de-excitation coefficients. The combination with the DC scheme enables both the local and non-local excited states of large systems with an excited nature to be treated.

Chemical Exchange Saturation Transfer MRI: Capability for Predicting Therapeutic Effect of Chemoradiotherapy on Non-Small Cell Lung Cancer Patients.

BACKGROUND: Amide proton transfer (APT) weighted chemical exchange saturation transfer CEST (APTw/CEST) magnetic resonance imaging (MRI) has been suggested as having the potential for assessing the therapeutic effect of brain tumors or rectal cancer. Moreover, diffusion-weighted imaging (DWI) and positron emission tomography fused with computed tomography by means of 2-[fluorine-18]-fluoro-2-deoxy-D-glucose (FDG-PET/CT) have been suggested as useful in same setting. PURPOSE: To compare the capability of APTw/CEST imaging, DWI, and FDG-PET/CT for predicting therapeutic effect of chemoradiotherapy (CRT) on stage III non-small cell lung cancer (NSCLC) patients. STUDY TYPE: Prospective. POPULATION: Eighty-four consecutive patients with Stage III NSCLC, 45 men (age range, 62-75 years; mean age, 71 years) and 39 women (age range, 57-75 years; mean age, 70 years). All patients were then divided into two groups (Response Evaluation Criteria in Solid Tumors [RECIST] responders, consisting of the complete response and partial response groups, and RECIST non-responders, consisting of the stable disease and progressive disease groups). FIELD STRENGTH/SEQUENCE: 3 T, echo planar imaging or fast advanced spin-echo (FASE) sequences for DWI and 2D half Fourier FASE sequences with magnetization transfer pulses for CEST imaging. ASSESSMENT: Magnetization transfer ratio asymmetry (MTRasym ) at 3.5 ppm, apparent diffusion coefficient (ADC), and maximum standard uptake value (SUVmax, ) on PET/CT were assessed by means of region of interest (ROI) measurements at primary tumor. STATISTICAL TESTS: Kaplan-Meier method followed by log-rank test and Cox proportional hazards regression analysis with multivariate analysis. A P value <0.05 was considered statistically significant. RESULTS: Progression-free survival (PFS) and overall survival (OS) had significant difference between two groups. MTRasym at 3.5 ppm (hazard ratio [HR] = 0.70) and SUVmax (HR = 1.41) were identified as significant predictors for PFS. Tumor staging (HR = 0.57) was also significant predictors for OS. DATA CONCLUSION: APTw/CEST imaging showed potential performance as DWI and FDG-PET/CT for predicting the therapeutic effect of CRT on stage III NSCLC patients. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY: Stage 1.

Computed DWI MRI Results in Superior Capability for N-Stage Assessment of Non-Small Cell Lung Cancer Than That of Actual DWI, STIR Imaging, and FDG-PET/CT.

BACKGROUND: Computed diffusion-weighted imaging (cDWI) is a mathematical computation technique that generates DWIs for any b-value by using actual DWI (aDWI) data with at least two different b-values and may improve differentiation of metastatic from nonmetastatic lymph nodes. PURPOSE: To determine the appropriate b-value for cDWI to achieve a better diagnostic capability for lymph node staging (N-staging) in non-small cell lung cancer (NSCLC) patients compared to aDWI, short inversion time (TI) inversion recovery (STIR) imaging, or positron emission tomography with 2-[fluorine-18] fluoro-2-deoxy-d-glucose combined with computed tomography (FDG-PET/CT). STUDY TYPE: Prospective. SUBJECTS: A total of 245 (127 males and 118 females; mean age 72 years) consecutive histopathologically confirmed NSCLC patients. FIELD STRENGTH/SEQUENCE: A 3 T, half-Fourier single-shot turbo spin-echo sequence, electrocardiogram (ECG)-triggered STIR fast advanced spin-echo (FASE) sequence with black blood and STIR acquisition and DWI obtained by FASE with b-values of 0 and 1000 sec/mm2 . ASSESSMENT: From aDWIs with b-values of 0 and 1000 (aDWI1000 ) sec/mm2 , cDWI using 400 (cDWI400 ), 600 (cDWI600 ), 800 (cDWI800 ), and 2000 (cDWI2000 ) sec/mm2 were generated. Then, 114 metastatic and 114 nonmetastatic nodes (mediastinal and hilar lymph nodes) were selected and evaluated with a contrast ratio (CR) for each cDWI and aDWI, apparent diffusion coefficient (ADC), lymph node-to-muscle ratio (LMR) on STIR, and maximum standard uptake value (SUVmax ). STATISTICAL TESTS: Receiver operating characteristic curve (ROC) analysis, Youden index, and McNemar's test. RESULTS: Area under the curve (AUC) of CR600 was significantly larger than the CR400 , CR800 , CR2000 , aCR1000 , and SUVmax . Comparison of N-staging accuracy showed that CR600 was significantly higher than CR400 , CR2000 , ADC, aCR1000 , and SUVmax , although there were no significant differences with CR800 (P = 0.99) and LMR (P = 0.99). DATA CONCLUSION: cDWI with b-value at 600 sec/mm2 may have potential to improve N-staging accuracy as compared with aDWI, STIR, and PET/CT. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.

Iridium-Catalyzed Branch-Selective Hydroalkylation of Simple Alkenes with Malonic Amides and Malonic Esters.

We report the iridium-catalyzed branch-selective hydroalkylation of simple alkenes such as aliphatic alkenes and aromatic alkenes with malonic amides and malonic esters under neutral reaction conditions. A variety of aliphatic alkenes and aromatic alkenes bearing bromine, chlorine, ester, 2-thienylcarboxylate, silyl, and phthalimide groups were all found to be suitable for this hydroalkylation. The combination of this method with Krapcho dealkoxycarbonylation realized a one-pot synthesis of ß-substituted amide and ester from ß-amide ester and malonic ester. The hydroalkylated products derived from malonic amides are suitable for further transformation. The finely tuned reaction conditions realized the selective transformation of hydroalkylated products to 1,3-diamines or monoamides with the same reagent. Deuterium labeling experiments and measurement of the kinetic isotope effect indicated that the catalytic cycle involves a reversible step and cleavage of the C-H bond is not a rate-determining step. Density functional theory calculations provided insight into the reaction mechanism, where the carboiridation step is followed by C-H reductive elimination.

Divide-and-Conquer Linear-Scaling Quantum Chemical Computations.

Fragmentation and embedding schemes are of great importance when applying quantum-chemical calculations to more complex and attractive targets. The divide-and-conquer (DC)-based quantum-chemical model is a fragmentation scheme that can be connected to embedding schemes. This feature article explains several DC-based schemes developed by the authors over the last two decades, which was inspired by the pioneering study of DC self-consistent field (SCF) method by Yang and Lee (J. Chem. Phys. 1995, 103, 5674-5678). First, the theoretical aspects of the DC-based SCF, electron correlation, excited-state, and nuclear orbital methods are described, followed by the two-component relativistic theory, quantum-mechanical molecular dynamics simulation, and the introduction of three programs, including DC-based schemes. Illustrative applications confirmed the accuracy and feasibility of the DC-based schemes.

Unveiling controlling factors of the S0/S1 minimum-energy conical intersection (3): Frozen orbital analysis based on the spin-flip theory.

Conical intersections (CIs), which indicate the crossing of two or more adiabatic electronic states, are crucial in the mechanisms of photophysical, photochemical, and photobiological processes. Although various geometries and energy levels have been reported using quantum chemical calculations, the systematic interpretation of the minimum energy CI (MECI) geometries is unclear. A previous study [Nakai et al., J. Phys. Chem. A 122, 8905 (2018)] performed frozen orbital analysis (FZOA) based on time-dependent density functional theory (TDDFT) at the MECI formed between the ground and first electronic excited states (S0/S1 MECI), thereby inductively clarifying two controlling factors. However, one of the factors that the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) energy gap became close to the HOMO-LUMO Coulomb integral was not valid in the case of spin-flip TDDFT (SF-TDDFT), which is frequently used as a means of the geometry optimization of MECI [Inamori et al., J. Chem. Phys. 152, 144108 (2020)]. This study revisited the controlling factors using FZOA for the SF-TDDFT method. Based on spin-adopted configurations within a minimum active space, the S0-S1 excitation energy is approximately represented by the HOMO and LUMO energy gap ΔεHL, a contribution from Coulomb integrals JHL″ and that from the HOMO-LUMO exchange integral KHL″. Furthermore, numerical applications of the revised formula at the SF-TDDFT method confirmed the control factors of S0/S1 MECI.

Efficacy of Ultrashort Echo Time Pulmonary MRI for Lung Nodule Detection and Lung-RADS Classification.

Background Pulmonary MRI with ultrashort echo time (UTE) has been compared with chest CT for nodule detection and classification. However, direct comparisons of these methods' capabilities for Lung CT Screening Reporting and Data System (Lung-RADS) evaluation remain lacking. Purpose To compare the capabilities of pulmonary MRI with UTE with those of standard- or low-dose thin-section CT for Lung-RADS classification. Materials and Methods In this prospective study, standard- and low-dose chest CT (270 mA and 60 mA, respectively) and MRI with UTE were used to examine consecutive participants enrolled between January 2017 and December 2020 who met American College of Radiology Appropriateness Criteria for lung cancer screening with low-dose CT. Probability of nodule presence was assessed for all methods with a five-point visual scoring system by two board-certified radiologists. All nodules were then evaluated in terms of their Lung-RADS classification using each method. To compare nodule detection capability of the three methods, consensus for performances was rated by using jackknife free-response receiver operating characteristic analysis, and sensitivity was compared by means of the McNemar test. In addition, weighted κ statistics were used to determine the agreement between Lung-RADS classification obtained with each method and the reference standard generated from standard-dose CT evaluated by two radiologists who were not included in the image analysis session. Results A total of 205 participants (mean age: 64 years ± 7 [standard deviation], 106 men) with 1073 nodules were enrolled. Figure of merit (FOM) (P < .001) had significant differences among three modalities (standard-dose CT: FOM = 0.91, low-dose CT: FOM = 0.89, pulmonary MRI with UTE: FOM = 0.94), with no evidence of false-positive findings in participants with all modalities (P > .05). Agreements for Lung-RADS classification between all modalities and the reference standard were almost perfect (standard-dose CT: κ = 0.82, P < .001; low-dose CT: κ = 0.82, P < .001; pulmonary MRI with UTE: κ = 0.82, P < .001). Conclusion In a lung cancer screening population, ultrashort echo time pulmonary MRI was comparable to standard- or low-dose CT for Lung CT Screening Reporting and Data System classification. © RSNA, 2021 Online supplemental material is available for this article. See also the editorial by Wielpütz in this issue.

Small Cell Lung Cancer Staging: Prospective Comparison of Conventional Staging Tests, FDG PET/CT, Whole-Body MRI, and Coregistered FDG PET/MRI.

BACKGROUND. Whole-body MRI and FDG PET/MRI have shown encouraging results for staging of thoracic malignancy but are poorly studied for staging of small cell lung cancer (SCLC). OBJECTIVE. The purpose of our study was to compare the performance of conventional staging tests, FDG PET/CT, whole-body MRI, and FDG PET/MRI for staging of SCLC. METHODS. This prospective study included 98 patients (64 men, 34 women; median age, 74 years) with SCLC who underwent conventional staging tests (brain MRI; neck, chest, and abdominopelvic CT; and bone scintigraphy), FDG PET/CT, and whole-body MRI within 2 weeks before treatment; coregistered FDG PET/MRI was generated. Two nuclear medicine physicians independently reviewed conventional tests and FDG PET/CT examinations in separate sessions, and two chest radiologists independently reviewed whole-body MRI and FDG PET/MRI examinations in separate sessions. Readers assessed T, N, and M categories; TNM stage; and Veterans Administration Lung Cancer Study Group (VALSG) stage. Reader pairs subsequently reached consensus. Stages determined clinically during tumor board sessions served as the reference standard. RESULTS. Accuracy for T category was higher (p < .05) for whole-body MRI (94.9%) and FDG PET/MRI (94.9%) than for FDG PET/CT (85.7%). Accuracy for N category was higher (p < .05) for whole-body MRI (84.7%), FDG PET/MRI (83.7%), and FDG PET/CT (81.6%) than for conventional staging tests (75.5%). Accuracy for M category was higher (p < .05) for whole-body MRI (94.9%), FDG PET/MRI (94.9%), and FDG PET/CT (94.9%) than for conventional staging tests (84.7%). Accuracy for TNM stage was higher (p < .05) for whole-body MRI (88.8%) and FDG PET/MRI (86.7%) than for FDG PET/CT (77.6%) and conventional staging tests (72.4%). Accuracy for VALSG stage was higher (p < .05) for whole-body MRI (95.9%), FDG PET/MRI (95.9%), and FDG PET/CT (98.0%) than for conventional staging tests (82.7%). Interobserver agreement, expressed as kappa coefficients, ranged from 0.81 to 0.94 across imaging tests and staging endpoints. CONCLUSION. FDG PET/CT, whole-body MRI, and coregistered FDG PET/MRI outperformed conventional tests for various staging endpoints in patients with SCLC. Whole-body MRI and FDG PET/MRI outperformed FDG PET/CT for T category and thus TNM stage, indicating the utility of MRI for assessing extent of local invasion in SCLC. CLINICAL IMPACT. Incorporation of either MRI approach may improve initial staging evaluation in SCLC.

Machine learning for lung texture analysis on thin-section CT: Capability for assessments of disease severity and therapeutic effect for connective tissue disease patients in comparison with expert panel evaluations.

BACKGROUND: The need for quantitative assessment of interstitial lung involvement on thin-section computed tomography (CT) has arisen in interstitial lung diseases including connective tissue disease (CTD). PURPOSE: To evaluate the capability of machine learning (ML)-based CT texture analysis for disease severity and treatment response assessments in comparison with qualitatively assessed thin-section CT for patients with CTD. MATERIAL AND METHODS: A total of 149 patients with CTD-related ILD (CTD-ILD) underwent initial and follow-up CT scans (total 364 paired serial CT examinations), pulmonary function tests, and serum KL-6 level tests. Based on all follow-up examination results, all paired serial CT examinations were assessed as "Stable" (n = 188), "Worse" (n = 98) and "Improved" (n = 78). Next, quantitative index changes were determined by software, and qualitative disease severity scores were assessed by consensus of two radiologists. To evaluate differences in each quantitative index as well as in disease severity score between paired serial CT examinations, Tukey's honestly significant difference (HSD) test was performed among the three statuses. Stepwise regression analyses were performed to determine changes in each pulmonary functional parameter and all quantitative indexes between paired serial CT scans. RESULTS: Δ% normal lung, Δ% consolidation, Δ% ground glass opacity, Δ% reticulation, and Δdisease severity score showed significant differences among the three statuses (P < 0.05). All differences in pulmonary functional parameters were significantly affected by Δ% normal lung, Δ% reticulation, and Δ% honeycomb (0.16 ≤r2 ≤0.42; P < 0.05). CONCLUSION: ML-based CT texture analysis has better potential than qualitatively assessed thin-section CT for disease severity assessment and treatment response evaluation for CTD-ILD.

3D Oxygen-Enhanced MRI at 3T MR System: Comparison With Thin-Section CT of Quantitative Capability for Pulmonary Functional Loss Assessment and Clinical Stage Classification of COPD in Smokers.

BACKGROUND: Oxygen (O2 )-enhanced MRI is mainly performed by a 2D sequence using 1.5T MR systems but trying to be obtained by a 3D sequence using a 3T MR system. PURPOSE: To compare the capability of 3D O2 -enhanced MRI and that of thin-section computed tomography (CT) for pulmonary functional loss assessment and clinical stage classification of chronic obstructive pulmonary disease (COPD) in smokers. STUDY TYPE: Prospective study. POPULATION: Fifty six smokers were included. FIELD STRENGTH/ SEQUENCE: 3T, 3D O2 -enhanced MRIs were performed with a 3D T1 -weighted fast field echo pulse sequence using the multiple flip angles. ASSESSMENTS: Smokers were classified into four stages ("Without COPD," "Mild COPD," "Moderate COPD," "Severe or very severe COPD"). Maps of regional changes in T1 values were generated from O2 -enhanced MR data. Regions of interest (ROIs) were then placed over the lung on all slices and averaged to determine mean T1 value change (ΔT1 ). Quantitative CT used the percentage of low attenuation areas within the entire lung (LAA%). STATISTICAL TESTS: ΔT1 and LAA% were correlated with pulmonary functional parameters, and compared for four stages using Tukey's Honestly Significant Difference test. Discrimination analyses were performed and McNemar's test was used for a comparison of the accuracy of the indexes. RESULTS: There were significantly higher correlations between ΔT1 and pulmonary functional parameters (-0.83 ≤ r ≤ -0.71, P < 0.05) than between LAA% and the same pulmonary functional parameters (-0.76 ≤ r ≤ -0.69, P < 0.05). ΔT1 and LAA% of the "Mild COPD" and "Moderate COPD" groups were significantly different from those of the "Severe or Very Severe COPD" group (P < 0.05). Discriminatory accuracy of ΔT1 (62.5%) and ΔT1 with LAA% (67.9%) was significantly greater than that of LAA% (48.2%, P < 0.05). DATA CONCLUSION: Compared with thin-section CT, 3D O2 -enhanced MRI has a similar capability for pulmonary functional assessment but better potential for clinical stage classification in smokers. LEVEL OF EVIDENCE: 2 TECHNICAL EFFICACY STAGE: 1.

Spin-flip approach within time-dependent density functional tight-binding method: Theory and applications.

A spin-flip time-dependent density functional tight-binding (SF-TDDFTB) method is developed that describes target states as spin-flipping excitation from a high-spin reference state obtained by the spin-restricted open shell treatment. Furthermore, the SF-TDDFTB formulation is extended to long-range correction (LC), denoted as SF-TDLCDFTB. The LC technique corrects the overdelocalization of electron density in systems such as charge-transfer systems, which is typically found in conventional DFTB calculations as well as density functional theory calculations using pure functionals. The numerical assessment of the SF-TDDFTB method shows smooth potential curves for the bond dissociation of hydrogen fluoride and the double-bond rotation of ethylene and the double-cone shape of H3 as the simplest degenerate systems. In addition, numerical assessments of SF-TDDFTB and SF-TDLCDFTB for 39 S0 /S1 minimum energy conical intersection (MECI) structures are performed. The SF-TDDFTB and SF-TDLCDFTB methods drastically reduce the computational cost with accuracy for MECI structures compared with SF-TDDFT.

Differentiation of Benign from Malignant Pulmonary Nodules by Using a Convolutional Neural Network to Determine Volume Change at Chest CT.

Background Deep learning may help to improve computer-aided detection of volume (CADv) measurement of pulmonary nodules at chest CT. Purpose To determine the efficacy of a deep learning method for improving CADv for measuring the solid and ground-glass opacity (GGO) volumes of a nodule, doubling time (DT), and the change in volume at chest CT. Materials and Methods From January 2014 to December 2016, patients with pulmonary nodules at CT were retrospectively reviewed. CADv without and with a convolutional neural network (CNN) automatically determined total nodule volume change per day and DT. Area under the curves (AUCs) on a per-nodule basis and diagnostic accuracy on a per-patient basis were compared among all indexes from CADv with and without CNN for differentiating benign from malignant nodules. Results The CNN training set was 294 nodules in 217 patients, the validation set was 41 nodules in 32 validation patients, and the test set was 290 nodules in 188 patients. A total of 170 patients had 290 nodules (mean size ± standard deviation, 11 mm ± 5; range, 4-29 mm) diagnosed as 132 malignant nodules and 158 benign nodules. There were 132 solid nodules (46%), 106 part-solid nodules (36%), and 52 ground-glass nodules (18%). The test set results showed that the diagnostic performance of the CNN with CADv for total nodule volume change per day was larger than DT of CADv with CNN (AUC, 0.94 [95% confidence interval {CI}: 0.90, 0.96] vs 0.67 [95% CI: 0.60, 0.74]; P < .001) and CADv without CNN (total nodule volume change per day: AUC, 0.69 [95% CI: 0.62, 0.75]; P < .001; DT: AUC, 0.58 [95% CI: 0.51, 0.65]; P < .001). The accuracy of total nodule volume change per day of CADv with CNN was significantly higher than that of CADv without CNN (P < .001) and DT of both methods (P < .001). Conclusion Convolutional neural network is useful for improving accuracy of computer-aided detection of volume measurement and nodule differentiation capability at CT for patients with pulmonary nodules. © RSNA, 2020 Online supplemental material is available for this article.

Finite-temperature-based time-dependent density-functional theory method for static electron correlation systems.

In this study, we developed a time-dependent density-functional theory (TDDFT) with a finite-temperature (FT) scheme, denoted as FT-TDDFT. We introduced the concept of fractional occupation numbers for random phase approximation equation and evaluated the excited-state electronic entropy terms with excited-state occupation number. The orbital occupation numbers for the excited state were evaluated from the change in the ground-state electron configuration with excitation and deexcitation coefficients. Furthermore, we extended the FT formulation to the time-dependent density-functional tight-binding (TDDFTB) method for larger systems, denoted as FT-TDDFTB. Numerical assessment for the FT-(TD)DFT method showed smooth potential curves for double-bond rotation of ethylene in both ground and excited states. Excited-state calculations based on the FT-TDDFTB method were applied to the uniform π-stacking columns composed of trioxotriangulene, possessing neutral radicals in strong correlation systems.

Large-scale excited-state calculation using dynamical polarizability evaluated by divide-and-conquer based coupled cluster linear response method.

This study attempted to propose an efficient scheme at the coupled cluster linear response (CCLR) level to perform large-scale excited-state calculations of not only local excitations but also nonlocal ones such as charge transfers and transitions between delocalized orbitals. Although standard applications of fragmentation techniques to the excited-state calculations brought about the limitations that could only deal with local excitations, this study solved the problem by evaluating the excited states as the poles of dynamical polarizability. Because such an approach previously succeeded at the time-dependent density functional theory level [H. Nakai and T. Yoshikawa, J. Chem. Phys. 146, 124123 (2017)], this study was considered as an extension to the CCLR level. To evaluate the dynamical polarizability at the CCLR level, we revisited three equivalent formulas, namely, coupled-perturbed self-consistent field (CPSCF), random phase approximation (RPA), and Green's function (GF). We further extended these formulas to the linear-scaling methods based on the divide-and-conquer (DC) technique. We implemented the CCLR with singles and doubles (CCSDLR) program for the six schemes, i.e., the standard and DC-type CPSCF, RPA, and GF. Illustrative applications of the present methods demonstrated the accuracy and efficiency. Although the standard three treatments could exactly reproduced the conventional frequency-domain CCSDLR results, their computational costs were commonly higher than that of the conventional ones due to large amount of computations for individual frequencies of the external electric field. The DC-type treatments, which approximately reproduced the conventional results, could achieve quasilinear scaling computational costs. Among them, DC-GF was found to exhibit the best performance.

Machine-learned electron correlation model based on frozen core approximation.

The machine-learned electron correlation (ML-EC) model is a regression model in the form of a density functional that reproduces the correlation energy density based on wavefunction theory. In a previous study [T. Nudejima et al., J. Chem. Phys. 151, 024104 (2019)], the ML-EC model was constructed using the correlation energy density from all-electron calculations with basis sets including core polarization functions. In this study, we applied the frozen core approximation (FCA) to the correlation energy density to reduce the computational cost of the response variable used in machine learning. The coupled cluster singles, doubles, and perturbative triples [CCSD(T)] correlation energy density obtained from a grid-based energy density analysis was analyzed within FCA and correlation-consistent basis sets without core polarization functions. The complete basis set (CBS) limit of the correlation energy density was obtained using the extrapolation and composite schemes. The CCSD(T)/CBS correlation energy densities based on these schemes showed reasonable behavior, indicating its appropriateness as a response variable. As expected, the computational time was significantly reduced, especially for systems containing elements with a large number of inner-shell electrons. Based on the density-to-density relationship, a large number of data (5 662 500 points), which were accumulated from 30 molecules, were sufficient to construct the ML-EC model. The valence-electron correlation energies and reaction energies calculated using the constructed model were in good agreement with the reference values, the latter of which were superior in accuracy to density functional calculations using 71 exchange-correlation functionals. The numerical results indicate that the FCA is useful for constructing a versatile model.

Unveiling controlling factors of the S0/S1 minimum energy conical intersection (2): Application to penalty function method.

Minimum-energy conical intersection (MECI) geometries play an important role in photophysics, photochemistry, and photobiology. In a previous study [Nakai et al., J. Phys. Chem. A 122, 8905 (2018)], frozen orbital analysis at the MECI geometries between the ground and first electronic excited states (S0/S1 MECI), which considers the main configurations contributing to the excitation, inductively clarified two controlling factors. First, the exchange integral between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) approximately becomes zero. Second, the HOMO-LUMO gap becomes close to the HOMO-LUMO Coulomb integral. This study applies the controlling factors to the penalty function method, which is the standard MECI optimization technique, and minimizes the energy average of the two states with the constraint that the energy gap between the states vanishes. Numerical assessments clarified that the present method could obtain the S0/S1 MECI geometries more efficiently than the conventional one.

Downregulation of RalGTPase-activating protein promotes invasion of prostatic epithelial cells and progression from intraepithelial neoplasia to cancer during prostate carcinogenesis.

RalGTPase-activating protein (RalGAP) is an important negative regulator of small GTPases RalA/B that mediates various oncogenic signaling pathways in various cancers. Although the Ral pathway has been implicated in prostate cancer (PCa) development and progression, the significance of RalGAP in PCa has been largely unknown. We examined RalGAPα2 expression using immunohistochemistry on two independent tissue microarray sets. Both datasets demonstrated that the expression of RalGAPα2 was significantly downregulated in PCa tissues compared to adjacent benign prostatic epithelia. Silencing of RalGAPα2 by short hairpin RNA enhanced migration and invasion abilities of benign and malignant prostate epithelial cell lines without affecting cell proliferation. Exogenous expression of wild-type RalGAP, but not the GTPase-activating protein activity-deficient mutant of RalGAP, suppressed migration and invasion of multiple PCa cell lines and was phenocopied by pharmacological inhibition of RalA/B. Loss of Ralgapa2 promoted local microscopic invasion of prostatic intraepithelial neoplasia without affecting tumor growth in a Pten-deficient mouse model for prostate tumorigenesis. Our findings demonstrate the functional significance of RalGAP downregulation to promote invasion ability, which is a property necessary for prostate carcinogenesis. Thus, loss of RalGAP function has a distinct role in promoting progression from prostatic intraepithelial neoplasia to invasive adenocarcinoma.

GPU-Accelerated Large-Scale Excited-State Simulation Based on Divide-and-Conquer Time-Dependent Density-Functional Tight-Binding.

The present study implemented the divide-and-conquer time-dependent density-functional tight-binding (DC-TDDFTB) code on a graphical processing unit (GPU). The DC method, which is a linear-scaling scheme, divides a total system into several fragments. By separately solving local equations in individual fragments, the DC method could reduce slow central processing unit (CPU)-GPU memory access, as well as computational cost, and avoid shortfalls of GPU memory. Numerical applications confirmed that the present code on GPU significantly accelerated the TDDFTB calculations, while maintaining accuracy. Furthermore, the DC-TDDFTB simulation of 2-acetylindan-1,3-dione displays excited-state intramolecular proton transfer and provides reasonable absorption and fluorescence energies with the corresponding experimental values. © 2019 Wiley Periodicals, Inc.