Magnetic resonance-based imaging biopsy with signatures including topological Betti number features for prediction of primary brain metastatic sites

This study incorporated topology Betti number (BN) features into the prediction of primary sites of brain metastases and the construction of magnetic resonance-based imaging biopsy (MRB) models. The significant features of the MRB model were selected from those obtained from gray-scale and three-dimensional wavelet-filtered images, BN and inverted BN (iBN) maps, and clinical variables (age and gender). The primary sites were predicted as either lung cancer or other cancers using MRB models, which were built using seven machine learning methods with significant features chosen by three feature selection methods followed by a combination strategy. Our study dealt with a dataset with relatively smaller brain metastases, which included effective diameters greater than 2 mm, with metastases ranging from 2 to 9 mm accounting for 17% of the dataset. The MRB models were trained by T1-weighted contrast-enhanced images of 494 metastases chosen from 247 patients and applied to 115 metastases from 62 test patients. The most feasible model attained an area under the receiver operating characteristic curve (AUC) of 0.763 for the test patients when using a signature including features of BN and iBN maps, gray-scale and wavelet-filtered images, and clinical variables. The AUCs of the model were 0.744 for non-small cell lung cancer and 0.861 for small cell lung cancer. The results suggest that the BN signature boosted the performance of MRB for the identification of primary sites of brain metastases including small tumors.

Imaging-based chromatin and epigenetic age, ImAge, quantitates aging and rejuvenation

Biomarkers of biological age that predict the risk of disease and expected lifespan better than chronological age are key to efficient and cost-effective healthcare1-3. To advance a personalized approach to healthcare, such biomarkers must reliably and accurately capture individual biology, predict biological age, and provide scalable and cost-effective measurements. We developed a novel approach - image-based chromatin and epigenetic age (ImAge) that captures intrinsic progressions of biological age, which readily emerge as principal changes in the spatial organization of chromatin and epigenetic marks in single nuclei without regression on chronological age. ImAge captured the expected acceleration or deceleration of biological age in mice treated with chemotherapy or following a caloric restriction regimen, respectively. ImAge from chronologically identical mice inversely correlated with their locomotor activity (greater activity for younger ImAge), consistent with the widely accepted role of locomotion as an aging biomarker across species. Finally, we demonstrated that ImAge is reduced following transient expression of OSKM cassette in the liver and skeletal muscles and reveals heterogeneity of in vivo reprogramming. We propose that ImAge represents the first-in-class imaging-based biomarker of aging with single-cell resolution.

Combination of Clinical Factors and Radiomics Can Predict Local Recurrence and Metastasis After Stereotactic Body Radiotherapy for Non-small Cell Lung Cancer

BACKGROUND/AIM

Radiomics, which links radiological image features with patient prognoses, is expected to be applied for the prediction of the clinical outcomes of radiotherapy. We investigated the clinical and radiomic factors associated with recurrence patterns after stereotactic body radiotherapy (SBRT) for non-small cell lung cancer (NSCLC).

PATIENTS AND METHODS

We retrospectively analyzed 125 patients with histologically confirmed NSCLC who underwent SBRT between April 2003 and June 2017 at our institution. A radiomic score was calculated from five radiomics features (histogram and texture features) selected using the LASSO Cox regression model. These features were extracted from the gross tumor volume (GTV) in three-dimensional wavelet decomposition CT images. We used univariate and multivariate analyses to determine the associations between local control (LC) time and metastasis-free survival (MFS), clinical factors (age, sex, performance status, operability, smoking, histology, and tumor diameter), and the radiomic score.

RESULTS

With a median follow-up of 37 months, the following 3-year rates were observed: overall survival, 80.9%; progression-free survival, 61.7%; LC, 75.1%, and MFS; 74.5%. In multivariate analysis, histology (squamous cell carcinoma vs. non-squamous cell carcinoma, p=0.0045), tumor diameter (>3 cm vs. ≤3 cm, p=0.039); and radiomic score (>0.043 vs. ≤0.043, p=0.042) were significantly associated with LC, and the radiomic score (>0.304 vs. ≤0.304, p<0.001) was significantly associated with MFS.

CONCLUSION

Histology, tumor diameter, and radiomic score could be significant factors for predicting NSCLC recurrence patterns after SBRT.

Erratum: Search for Cosmic-Ray Boosted Sub-GeV Dark Matter Using Recoil Protons at Super-Kamiokande [Phys. Rev. Lett. 130, 031802 (2023)]

This corrects the article DOI: 10.1103/PhysRevLett.130.031802.

Topology-based radiomic features for prediction of parotid gland cancer malignancy grade in magnetic resonance images

PURPOSE

The malignancy grades of parotid gland cancer (PGC) have been assessed for a decision of treatment policies. Therefore, we have investigated the feasibility of topology-based radiomic features for the prediction of parotid gland cancer (PGC) malignancy grade in magnetic resonance (MR) images.

MATERIALS AND METHODS

Two-dimensional T1- and T2-weighted MR images of 39 patients with PGC were selected for this study. Imaging properties of PGC can be quantified using the topology, which could be useful for assessing the number of the k-dimensional holes or heterogeneity in PGC regions using invariants of the Betti numbers. Radiomic signatures were constructed from 41,472 features obtained after a harmonization using an elastic net model. PGC patients were stratified using a logistic classification into low/intermediate- and high-grade malignancy groups. The training data were increased by four times to avoid the overfitting problem using a synthetic minority oversampling technique. The proposed approach was assessed using a 4-fold cross-validation test.

RESULTS

The highest accuracy of the proposed approach was 0.975 for the validation cases, whereas that of the conventional approach was 0.694.

CONCLUSION

This study indicated that topology-based radiomic features could be feasible for the noninvasive prediction of the malignancy grade of PGCs.

Proof-of-Principle Experiment for Testing Strong-Field Quantum Electrodynamics with Exotic Atoms: High Precision X-Ray Spectroscopy of Muonic Neon

To test bound-state quantum electrodynamics (BSQED) in the strong-field regime, we have performed high precision x-ray spectroscopy of the 5g-4f and 5f- 4d transitions (BSQED contribution of 2.4 and 5.2 eV, respectively) of muonic neon atoms in the low-pressure gas phase without bound electrons. Muonic atoms have been recently proposed as an alternative to few-electron high-Z ions for BSQED tests by focusing on circular Rydberg states where nuclear contributions are negligibly small. We determined the 5g_{9/2}- 4f_{7/2} transition energy to be 6297.08±0.04(stat)±0.13(syst)  eV using superconducting transition-edge sensor microcalorimeters (5.2-5.5 eV FWHM resolution), which agrees well with the most advanced BSQED theoretical prediction of 6297.26 eV.

Geriatric nutritional risk index as the prognostic factor in older patients with fragility hip fractures

This study investigated the long-term survival and incidence of secondary fractures after fragility hip fractures. The 5-year survival rate was 62%, and the mortality risk was seen in patients with GNRI < 92. The 5-year incidence of secondary fracture was 22%, which was significantly higher in patients with a BMI < 20.

BACKGROUND

Malnutrition negatively influences the postoperative survival of patients with fragility hip fractures (FHFs); however, little is known about their association over the long term.

OBJECTIVE

This study evaluated the ability of the geriatric nutritional risk index (GNRI) as a risk factor for long-term mortality after FHFs.

METHODS

This study included 623 Japanese patients with FHFs over the age of 60 years. We prospectively collected data on admission and during hospitalization and assessed the patients' conditions after discharge through a questionnaire. We examined the long-term mortality and the incidence of secondary FHFs and assessed the prognostic factors.

RESULTS

The mean observation period was 4.0 years (range 0-7 years). The average age at the time of admission was 82 years (range 60-101 years). The overall survival after FHFs (1 year, 91%; 5 years, 62%) and the incidence of secondary FHFs were high (1 year, 4%; 5 years, 22%). The multivariate Cox proportional hazard analysis revealed the risk factors for mortality as older age (hazard ratio [HR] 1.04), male sex (HR 1.96), lower GNRI score (HR 0.96), comorbidities (malignancy, HR 2.51; ischemic heart disease, HR 2.24; revised Hasegawa dementia scale ≤ 20, HR 1.64), no use of active vitamin D3 on admission (HR 0.46), and a lower Barthel index (BI) (on admission, HR 1.00; at discharge, HR 0.99). The GNRI scores were divided into four risk categories: major risk (GNRI, < 82), moderate risk (82-91), low risk (92-98), and no risk (> 98). Patients at major and moderate risks of GNRI had a significantly lower overall survival rate (p < 0.001). Lower body mass index (BMI) was also identified as a prognostic factor for secondary FHFs (HR 0.88 [p = 0.004]).

CONCLUSIONS

We showed that older age, male sex, a lower GNRI score, comorbidities, and a lower BI are risk factors for mortality following FHFs. GNRI is a novel and simple predictor of long-term survival after FHFs.

Three-dimensional topological radiogenomics of epidermal growth factor receptor Del19 and L858R mutation subtypes on computed tomography images of lung cancer patients

OBJECTIVES

To elucidate a novel radiogenomics approach using three-dimensional (3D) topologically invariant Betti numbers (BNs) for topological characterization of epidermal growth factor receptor (EGFR) Del19 and L858R mutation subtypes.

METHODS

In total, 154 patients (wild-type EGFR, 72 patients; Del19 mutation, 45 patients; and L858R mutation, 37 patients) were retrospectively enrolled and randomly divided into 92 training and 62 test cases. Two support vector machine (SVM) models to distinguish between wild-type and mutant EGFR (mutation [M] classification) as well as between the Del19 and L858R subtypes (subtype [S] classification) were trained using 3DBN features. These features were computed from 3DBN maps by using histogram and texture analyses. The 3DBN maps were generated using computed tomography (CT) images based on the Čech complex constructed on sets of points in the images. These points were defined by coordinates of voxels with CT values higher than several threshold values. The M classification model was built using image features and demographic parameters of sex and smoking status. The SVM models were evaluated by determining their classification accuracies. The feasibility of the 3DBN model was compared with those of conventional radiomic models based on pseudo-3D BN (p3DBN), two-dimensional BN (2DBN), and CT and wavelet-decomposition (WD) images. The validation of the model was repeated with 100 times random sampling.

RESULTS

The mean test accuracies for M classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.810, 0.733, 0.838, 0.782, and 0.799, respectively. The mean test accuracies for S classification with 3DBN, p3DBN, 2DBN, CT, and WD images were 0.773, 0.694, 0.657, 0.581, and 0.696, respectively.

CONCLUSION

3DBN features, which showed a radiogenomic association with the characteristics of the EGFR Del19/L858R mutation subtypes, yielded higher accuracy for subtype classifications in comparison with conventional features.

Dual segmentation models for poorly and well-differentiated hepatocellular carcinoma using two-step transfer deep learning on dynamic contrast-enhanced CT images

The aim of this study was to develop dual segmentation models for poorly and well-differentiated hepatocellular carcinoma (HCC), using two-step transfer learning (TSTL) based on dynamic contrast-enhanced (DCE) computed tomography (CT) images. From 2013 to 2019, DCE-CT images of 128 patients with 80 poorly differentiated and 48 well-differentiated HCCs were selected at our hospital. In the first transfer learning (TL) step, a pre-trained segmentation model with 192 CT images of lung cancer patients was retrained as a poorly differentiated HCC model. In the second TL step, a well-differentiated HCC model was built from a poorly differentiated HCC model. The average three-dimensional Dice's similarity coefficient (3D-DSC) and 95th-percentile of the Hausdorff distance (95% HD) were mainly employed to evaluate the segmentation accuracy, based on a nested fourfold cross-validation test. The DSC denotes the degree of regional similarity between the HCC reference regions and the regions estimated using the proposed models. The 95% HD is defined as the 95th-percentile of the maximum measures of how far two subsets of a metric space are from each other. The average 3D-DSC and 95% HD were 0.849 ± 0.078 and 1.98 ± 0.71 mm, respectively, for poorly differentiated HCC regions, and 0.811 ± 0.089 and 2.01 ± 0.84 mm, respectively, for well-differentiated HCC regions. The average 3D-DSC for both regions was 1.2 times superior to that calculated without the TSTL. The proposed model using TSTL from the lung cancer dataset showed the potential to segment poorly and well-differentiated HCC regions on DCE-CT images.

Formation and evolution of carbonaceous asteroid Ryugu: Direct evidence from returned samples

Samples of the carbonaceous asteroid Ryugu were brought to Earth by the Hayabusa2 spacecraft. We analyzed 17 Ryugu samples measuring 1 to 8 millimeters. Carbon dioxide-bearing water inclusions are present within a pyrrhotite crystal, indicating that Ryugu's parent asteroid formed in the outer Solar System. The samples contain low abundances of materials that formed at high temperatures, such as chondrules and calcium- and aluminum-rich inclusions. The samples are rich in phyllosilicates and carbonates, which formed through aqueous alteration reactions at low temperature, high pH, and water/rock ratios of <1 (by mass). Less altered fragments contain olivine, pyroxene, amorphous silicates, calcite, and phosphide. Numerical simulations, based on the mineralogical and physical properties of the samples, indicate that Ryugu's parent body formed ~2 million years after the beginning of Solar System formation.

Search for Cosmic-Ray Boosted Sub-GeV Dark Matter Using Recoil Protons at Super-Kamiokande

We report a search for cosmic-ray boosted dark matter with protons using the 0.37  megaton×years data collected at Super-Kamiokande experiment during the 1996-2018 period (SKI-IV phase). We searched for an excess of proton recoils above the atmospheric neutrino background from the vicinity of the Galactic Center. No such excess is observed, and limits are calculated for two reference models of dark matter with either a constant interaction cross section or through a scalar mediator. This is the first experimental search for boosted dark matter with hadrons using directional information. The results present the most stringent limits on cosmic-ray boosted dark matter and exclude the dark matter-nucleon elastic scattering cross section between 10^{-33}cm^{2} and 10^{-27}cm^{2} for dark matter mass from 1  MeV/c^{2} to 300  MeV/c^{2}.

Can Persistent Homology Features Capture More Intrinsic Information about Tumors from 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Images of Head and Neck Cancer Patients?

This study hypothesized that persistent homology (PH) features could capture more intrinsic information about the metabolism and morphology of tumors from ¹⁸F-fluorodeoxyglucose positron emission tomography (PET)/computed tomography (CT) images of patients with head and neck (HN) cancer than other conventional features. PET/CT images and clinical variables of 207 patients were selected from the publicly available dataset of the Cancer Imaging Archive. PH images were generated from persistent diagrams obtained from PET/CT images. The PH features were derived from the PH PET/CT images. The signatures were constructed in a training cohort from features from CT, PET, PH-CT, and PH-PET images; clinical variables; and the combination of features and clinical variables. Signatures were evaluated using statistically significant differences (p-value, log-rank test) between survival curves for low- and high-risk groups and the C-index. In an independent test cohort, the signature consisting of PH-PET features and clinical variables exhibited the lowest log-rank p-value of 3.30 × 10⁻⁵ and C-index of 0.80, compared with log-rank p-values from 3.52 × 10⁻² to 1.15 × 10⁻⁴ and C-indices from 0.34 to 0.79 for other signatures. This result suggests that PH features can capture the intrinsic information of tumors and predict prognosis in patients with HN cancer.

Diagnostic performance of angiography-derived index of microvascular resistance: a systematic review and pooled meta-analysis

Background

The index of microvascular resistance (IMR) is an established measurement of coronary microcirculation status. However, it has not been widely incorporated into routine practice due to need for intracoronary instrumentation (pressure wire) and hyperaemic agents. Several angiography-derived quantitative flow ratio-based indexes of microvascular resistance (angio-IMR) have been proposed rekindling the interest for the assessment and management of microvascular disease.

Purpose

To review the overall diagnostic accuracy of angio-IMR against wire based IMR.

Methods

A systematic review of the literature was performed and studies comparing angio-IMR with wire based IMR were included. Individual data was extracted using semi-automatic digitalization. Correlation of angio-IMR with IMR and its diagnostic performance against IMR were analysed.

Results

Six studies directly comparing angio-IMR with IMR were included. Data extraction rate was 85.1% (582/684 vessels). There was a linear correlation between angio-IMR and IMR (β=0.483, R square=0.298) (Figure 1A). Pooled sensitivity was 77%, specificity was 66%, positive predictive value was 65%, negative predictive value was 78%, and accuracy was 71.0%. Pooled area under receiver operator curve of angio-IMR for predicting IMR diagnosed coronary microvascular disease was 0.754 (95% confidential interval 0.715 to 0.793) (Figure 1B). Similar diagnostic performance was observed in subgroups of patients with or without ST-segment elevation myocardial infarction.

Conclusions

Currently available angio-IMR showed a clearly useful discrimination and diagnostic performance against the standard of wire based IMR.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): China Scholarship Council

Impact of bifurcation lesion on 10-year mortality in the SYNTAX trial

Background

Percutaneous coronary intervention (PCI) of bifurcation lesions is associated with higher rates of adverse events, and currently it is unclear whether PCI or coronary artery bypass grafting (CABG) is the safer treatment for these patients at very long-term follow up.

Objectives

To investigate the impact of bifurcation lesions on observed all-cause 10-year mortality in the SYNTAX trial.

Methods

In the SYNTAX Extended Survival study, 10-year observed mortality was compared among four groups: (a) presence of ≥1 bifurcation lesion and treatment with PCI (n=649), (b) no bifurcation lesion and treatment with PCI (n=248), (c) presence of ≥1 bifurcation lesion and treatment with CABG (n=651), and (d) no bifurcation lesion and treatment with CABG (n=239).

Results

Compared to patients without bifurcations, those with bifurcation lesion(s) treated with PCI had a significantly higher risk of all-cause death (19.8% vs 30.1%; HR: 1.55, 95% CI: 1.12 to 2.14; p=0.007), whereas following CABG, mortality was similar in patients with or without bifurcation lesion(s) (23.3% vs 23.0%; HR: 0.81, 95% CI: 0.59 to 1.12; p=0.207). (Figure1) There was a significant interaction between bifurcation lesion(s) and treatment arm (p for interaction=0.006).

In PCI patients, at 5-years there was no significant difference in mortality between 1- vs 2-stent techniques, whereas at 10-years, a 2-stent technique was associated with higher mortality (33.3% vs 25.9%; HR: 1.51, 95% CI: 1.06 to 2.14; p=0.021, Figure2).

Conclusions

Bifurcation lesion(s) require special attention from the heart team discussion, considering the higher 10-year all-cause mortality associated with PCI. Careful evaluation of bifurcation lesion complexity may be helpful in decision-making.

Funding Acknowledgement

Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): The SYNTAX Extended Survival study was supported by the German Foundation of Heart Research (Frankfurt am Main, Germany). The SYNTAX trial, during 0-5 years follow-up, was funded by Boston Scientific Corporation (Marlborough, MA, USA). Both sponsors had no role in the study design, data collection, data analyses, and interpretation of the study data, nor were involved in the decision to publish the final manuscript. The principal investigators and authors had complete scientific freedom.

Geographic disparity in 10-year mortality after coronary artery revascularization in the SYNTAXES trial

Aims

To investigate geographic disparity in long-term mortality following revascularization in patients with complex coronary artery disease (CAD).

Methods and results

The SYNTAXES trial randomized 1800 patients with three-vessel and/or left main CAD to percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) and assessed their survival at 10-years. Patients were stratified according to the region of recruitment: North America (N-A, n=245), Eastern Europe (E-E: Poland, Hungary, Czech, n=189), Northern Europe (N-E: United Kingdom, Sweden, Norway, Latvia, Finland, and Denmark, n=425), Southern Europe (S-E: Spain, Portugal, and Italy, n=263), and Western Europe (W-E: Netherlands, Germany, France, Belgium, and Austria, n=678), which also served as the reference group. Compared to W-E, patients were younger in E-E (62 vs 65 years, p&lt;0.001), and less frequently male in N-A (65.3% vs 79.6%, p&lt;0.001). Diabetes (16.0% vs 25.4%, p&lt;0.001) and peripheral vascular disease (6.8% vs 10.9%, p=0.025) were less frequent in N-E than W-E. Ejection fraction was highest in W-E (62% vs 56%, p&lt;0.001). Compared to W-E, the mean anatomic SYNTAX score was higher in S-E (29 vs 31, p=0.008) and lower in N-A (26, p&lt;0.001). Crude ten-year mortality was similar in N-A (31.6%), and W-E (30.7%), and significantly lower in E-E (22.5%, p=0.041), N-E (21.9%, p=0.003) and S-E (22.0%, p=0.014) as presented in left-middle lower of the graphical abstract. We adjusted the survival curves by following factors based on previous report; age, sex, medically treated diabetes, current smokers, peripheral vascular disease, chronic obstructive pulmonary disease, chronic kidney disease, left ventricular ejection fraction, disease type, and anatomical SYNTAX score [1]. When the differences in baseline characteristics were adjusted, mortality was still significantly lower in N-E (HR 0.85, 95% CI [0.74–0.97], p=0.019) and trended lower in S-E (HR 0.72 95% CI [0.52–0.99] p=0.043) compared to W-E (right middle-lower of the graphical abstract). However, no significant interaction (P interaction = 0.728) between region and modality of revascularization was seen.

Discussion and conclusions

The main findings of this study are:

1. Rates of crude 10-year mortality were significantly lower in E-E, N-E, and S-E compared to W-E and N-A.

2. The differences in 10-year mortality remained significantly lower with N-E and S-E even after adjustment for confounding factors.

3. However, when comparing PCI to CABG in the five geographic regions, there were no statistically significant interactions between the geographic disparity in pre- and peri-procedural characteristics and all-cause mortality.

In the era of globalization, knowledge and understanding of geographic disparity are of paramount importance for the correct interpretation of global studies.

Funding Acknowledgement

Type of funding sources: Foundation. Main funding source(s): The German Foundation of Heart Research (Frankfurt am Main, Germany)

Ten years survival benefit of CABG or PCI based on individual prediction

Background

To compare the observed and individual predicted mortalities according to the SYNTAX score II 2020 (SSII-2020) in the all-comers SYNTAX population, and retrospectively assess the appropriateness of revascularization with percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) in patients with three vessel disease (3VD) and/or left main disease (LMCAD).

Methods

Internal and external validation of the SSII-2020 to predict 10-year all-cause death was performed in the respective randomized and registry SYNTAX populations. Differences in individual predicted mortalities following CABG or PCI were ranked and displayed with the observed mortalities. The proportions of screened patients deriving a survival benefit from CABG or PCI were determined retrospectively.

Results

A total of 2602 participants (as-treated population) were included in the randomized and registry cohorts. In the randomized cohort, all-cause mortality at 10 years, as an average treatment effect, was 23.8% (199/865) with CABG and 28.6% (249/901) with PCI, with a differential survival benefit of 4.6% (95% CI: 0.58% to 8.7%, log-rank p value=0.023). In the CABG and PCI registries, mortalities were 27.8% (167/644) and 55.4% (99/192), respectively. Calibration and discrimination of the SSII-2020 was helpful in CABG and PCI patients in the randomized and registry cohorts. In the PCI registry, the SSII-2020 underestimated mortality since specific comorbidities that entail high mortality are not included in the formula (C-index: 0.72, intercept: 0.38, slope: 0.66), whilst in the CABG registry, it predicted mortality with a helpful calibration and discrimination (C-index: 0.70, intercept: 0.00, slope: 0.76). The proportions of patients with a predicted survival benefit following CABG and PCI were respectively 78.3% (1383/1766) and 21.7% (383/1766) in the randomized cohort, and 82.4% (2143/2602) and 17.7% (459/2602) in the whole SYNTAX trial population.

Conclusion

In the randomized and registry cohort of this all-comers population with 3VD and/or LMCAD, there was reasonable agreement between the individual predicted and observed mortalities after CABG or PCI, such that the predicted 10-year survival benefit might be helpful in determining the appropriateness of each modality of revascularization.

Funding Acknowledgement

Type of funding sources: Private grant(s) and/or Sponsorship. Main funding source(s): The SYNTAX Extended Survival study was supported by the German Foundation of Heart Research (Frankfurt am Main, Germany). The SYNTAX trial, during 0-5 years follow-up, was funded by Boston Scientific Corporation (Marlborough, MA, USA). Both sponsors had no role in the study design, data collection, data analyses, and interpretation of the study data, nor were involved in the decision to publish the final manuscript. The principal investigators and authors had complete scientific freedom.

Impact of left ventricular ejection fraction on 10-year mortality after percutaneous coronary intervention or coronary artery bypass grafting

Backgrounds

The impact on vital prognosis at very long-term of percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) in patients with reduced ejection fraction (EF) remains to be elucidated.

Objective

To investigate the impact of left ventricular ejection fraction (LVEF) on 10-year mortality after PCI and CABG in the SYNTAX trial.

Methods

In the SYNTAXES study, 1,800 randomized patients were categorized into three groups according to the current guidelines; (1) reduced EF (rEF; LVEF ≤40%), (2) mildly reduced EF (mrEF; LVEF 41–49%), (3) preserved EF (pEF; LVEF ≥50%). The primary endpoint was 10-year all-cause mortality. Event rate up to 10 years was estimated according to the Kaplan-Meier method, and the log-rank test was performed to examine the differences among LVEF subgroups. The SYNTAX score 2020 (SS-2020) was compared between the patients with reduced (LVEF &lt;50%) and preserved EF (LVEF ≥50%) in order to better refine their respective personalized vital prognosis and assess in cross-validation the value of the risk score.

Results

The population was stratified as rEF (n=168), mrEF (n=179), and pEF (n=1453). Ten-year all-cause mortality were 44.0% vs. 31.8% vs. 22.6% (P&lt;0.001), in patients with rEF, mrEF and pEF, respectively. The significant interaction was not identified between LVEF classification and treatment (P interaction = 0.183). In patients with rEF, there was a tendency toward higher mortality in PCI group than CABG (52.9% vs 39.6%, P=0.054), and no significant differences in patients with mrEF (36.0% vs. 28.6%, P=0.273) and pEF (23.9% vs. 22.2%, P=0.275). According to the SS-2020, PCI was a relatively safe modality of revascularization in 37.8% of the patients with reduced EF (LVEF &lt;50%). In the population with preserved EF (LVEF ≥50%), the proportion of patients eligible to PCI with predicted equipoise in mortality with CABG was 57.5%.

Conclusion

LVEF could an important factor for determining the revascularization treatment in patients presenting with complex coronary artery disease. Calculation of individualized 10-year prognosis using the SS-2020 may be a viable option in decision-making.

Funding Acknowledgement

Type of funding sources: None.

Appropriateness of the modality of revascularization according to the SYNTAX 2020 in the FASTTRACK CABG study

Objectives

To compare the modality of revascularization selected by the local heart team to the one recommended by the core laboratory according to the SYNTAX score 2020 amongst patients with three-vessel disease (3VD) with or without left main disease (LMCAD), who were allocated to CABG planned and solely guided by coronary computerized tomographic angiography in the FASTTRACK CABG trial.

Background

Personalized long term vital prognosis plays a key role in deciding between PCI and coronary artery bypass grafting (CABG) in patients with complex coronary artery disease.

Methods

In an interim analysis requested by the Data Safety Monitoring Board the treatment recommendations according to the SYNTAX 2020 were prospectively assessed in 57 consecutive patients (half of the planned population in this First in Man) by a core laboratory and compared to the decision of the “on site” heart team.

Results

According to SS-2020, the predicted absolute risk difference (ARD) in mortality between the virtual PCI treatment population and the CABG treatment group, which can be considered a virtual surrogate for the average treatment effect, increased with the duration of follow up, from 4.8±3.5% at 5 years to 8.8±5.1% at 10 years (Table 1). The ARD of less than 0% in mortality at 5-year in favour of PCI was only documented in two patients while the 55 remaining patients had a predicted survival benefit over PCI if receiving CABG. However, based on a novel threshold of equipoise (ARD &lt;4.5%) recently validated in a contemporary registry of 3VD and LMCAD, CABG was mandatory in 26 (45.6%) patients, whereas PCI or CABG could have been equally selected in 31 (54.4%) patients (Figure 1).

Conclusions

According to the SYNTAX Score 2020 there was a strict observance of the CABG treatment recommendation in the first 57 consecutive patients with 3VD or LMCAD, screened on site in the FAST TRACK CABG trial. The more lenient selection criteria derived from the contemporary regitry will have to be tested propectively. Application of artificial intelligence with expanded collection of baseline characteristics, scientific endorsement and regulatory enforcement as well as further prospective evaluation are the challenges of future decision-making scores, that should be ultimately shared with the patients.

Funding Acknowledgement

Type of funding sources: None.

Murray law-based quantitative flow ratio for assessment of left main bifurcation derived from a single fluoroscopic angiographic view as compared to FFRCT

Background

In patients with complex CAD, the presence of left main (LM) disease is an important prognostic factor in assessing the risk balance between PCI and CABG. Functional assessment has become standard of care to evaluate the significance of coronary stenosis and to justify the performance of PCI in the contemporary practice. FFRCT is a well-established method based on 3D reconstruction of coronary artery derived from CCTA. The Murray law-based quantitative flow reserve (μQFR) is a novel computational method of invasive angiography relying on a single angiographic view that takes into account side branches diameters to compute fractal flow division. The aim of the current analysis is to evaluate in patients with complex CAD the feasibility of μQFR in LM bifurcation and its diagnostic concordance with FFRCT. The impact of the optimal viewing angle defined by CCTA on the physiological assessment of the LM bifurcation using a single angiographic view was also evaluated.

Methods

In 299 consecutive patients with 3-vessel disease with or without LM coronary artery disease, up to 3 analyzable fluoroscopic projections per patient were analysed with μQFR retrospectively. FFRCT and μQFR were measured at 3 fiducial landmark points: i) point of LM bifurcation (POB); ii) proximal LAD 10 mm distal to POB; ii) proximal LCX 10 mm distal to POB. CCTA-based “optimal viewing angle” of LM bifurcation are computed by creating a 3-point closed spline involving the LM, LAD, and LCX at 5mm from the POB and subsequently by reconstructing the “en face” fluoroscopic viewing angle of the spline. The en face viewing angle provides an optimal assessment of the bifurcation geometry [1]. In terms of Rx gantry angulation, the closest angiographic projection to the optimal viewing angle derived from CCTA was defined as the “best fluoroscopic projection” for each patient.

Results

In 299 patients, 793 projections were analysed with μQFR and compared to FFRCT. Single view μQFR was analyzable in 100%. Correlation and agreement between μQFR and FFRCT for 793 projections in 299 patients are shown in Figure 1A, 2A. The Spearman's correlation coefficient showed moderate correlations at POB (r=0.481, p&lt;0.001) and LCX (r=0.584, p&lt;0.001), and strong correlation at LAD (r=0.642, p&lt;0.001). Correlation and agreement between μQFR and FFRCT for best projections from each patient are shown in Figure 1B, 2B. Correlations were improved in the best projections with the following Spearman's correlation coefficient: at POB (r=0.522, p&lt;0.001), LCX (r=0.622, p&lt;0.001), and LAD (r=0.695, p&lt;0.001).

Conclusion

Computation of μQFR from a single angiographic view has a high feasibility. Tailored optimal fluoroscopic view is essential for the physiological assessment of the LM bifurcation using a single angiographic view. Evaluation of diagnostic accuracy of μQFR warrants further analysis of the LMCAD after prospective planning of the optimal fluoroscopic view based on the selection of the best CCTA 3D view.

Funding Acknowledgement

Type of funding sources: None.

Angiographic derived physiological assessment after intervention for predicting 2-year vessel-oriented composite endpoints in Multivessel TALENT trial

Aims

The purpose of the study is to assess the treatment results in the first 200 patients in the ongoing Multivessel TALENT trial and to predict the vessel-oriented endpoint by assessing the quantitative flow ratio (QFR) post PCI in a central independent core laboratory.

Methods

In this prospective, randomized, 1:1 balanced, multi-centre, open-label trial, de novo multivessel coronary artery disease patients without left main disease are assigned to the sirolimus-eluting stent or everolimus-eluting stent arm. The percutaneous coronary intervention (PCI) is planned based on functional lesion evaluation by QFR provided by the Core laboratory and the PCI has to be optimized by intravascular imaging, optimal pharmacological treatment and prasugrel monotherapy.

Results

Mean age of the population was 66.6±14.7 years, and 78.1% of them were male. This population consists of 17.4% unstable angina and of 31.8% diabetic patients. Anatomical SYNTAX score was 18.8±9.1. Total 458 lesions were treated from September 2020 to December 2021. Left anterior descending artery accounts for 40.3%, bifurcation lesion was present in 40.8%, total occlusion in 4.2%. The average stent diameter and total stent length were 3.0±0.4mm and 38.7±22.4mm, respectively. Intravascular imaging was used in 92.8% of treated vessels (intravascular ultrasound 56.5%, optical coherent tomography 36.3%). Pre-PCI QFR was analysable in 435 vessels (0.59±0.21), and 5.5% of them were treated even though the pre-PCI QFR values were more than 0.8. Post-PCI QFR has been so far analysed in 303 vessels (0.93±0.11) and 79.5% of them achieved a post-PCI QFR equal or superior to 0.91. Based on the previous study [1], Two-year vessel-oriented composite endpoint (VOCE) was estimated to become 5.4% in the present study population (3.7% in the patient group that achieved post PCI QFR equal or superior to 0.91, while 12% in the patients who could not reach the threshold), which was almost equal to what is expected in the power calculation.

Conclusions

In the Multivessel TALENT trial, the large majority of the vessels treated (94.5%) complied with the hemodynamic criteria of recommended PCI as provided by the QFRs of the Core laboratory. Favourable post-PCI QFR (≥0.91) obtained in 80% of the patients let expected a favourable VOCE outcome of 5.4%.

Funding Acknowledgement

Type of funding sources: Public Institution(s). Main funding source(s): The National University of Ireland Galway

Comparison of the SYNTAX score 2020 based on Coronary Artery Computed Tomography (CCTA) with Invasive Coronary Angiography (ICA)

Introduction

The SYNTAX III REVOLUTION trial demonstrated that clinical decision-making between coronary artery bypass graft (CABG) and percutaneous coronary intervention (PCI) based on coronary artery computed tomography (CCTA) and predicting four years mortality according to the SYNTAX score II had a high agreement with the treatment decision derived from invasive coronary angiography (ICA). The agreement of the novel SYNTAX score 2020 (SS-2020) based on CCTA and ICA has not yet been evaluated in a prospective fashion.

Methods

This study included 54 consecutive patients in the ongoing FASTTRACK CABG trial that investigates decision making, planning and procedural CABG guidance based solely on CCTA and FFRct. All the patients underwent CCTA and ICA, and SS-2020 was calculated based on the results of anatomical SYNTAX score derived from either CCTA or ICA, and the respective scores were compared by using paired t-test.

Results

The mean age was 67.3±9.7, and 48 were men (88.9%). Anatomical SYNTAX score derived from CCTA was assessed in the 54 cases (analysability 100%). Anatomical SYNTAX scores based on CCTA and ICA were 34.3±9.3, and 35.5±11.3, respectively (P=0.480). As shown in the table predicted 5 years major adverse cardiac and cerebrovascular events (MACCE) following either PCI or CABG, as well as predicted 10 years mortality following CABG differed significantly. However the absolute risk differences (ARD) in 5 years MACCE and 10 years mortality following either PCI or CABG, were comparable.

Conclusions

High agreements were confirmed in the calculations of anatomical SYNTAX scores with CCTA and ICA. Despite significant differences in predicted MACCE rates at 5 years and mortalities at 10 years, the ARD in MACCE rates and mortality were comparable. In terms of treatment decision-making, SS-2020 calculations based on CCTA is a non-invasive predictive tool comparable to the one based on ICA.

Funding Acknowledgement

Type of funding sources: None.

Relapse predictability of topological signature on pretreatment planning CT images of stage I non-small cell lung cancer patients before treatment with stereotactic ablative radiotherapy

Background

This study aimed to explore the predictability of topological signatures linked to the locoregional relapse (LRR) and distant metastasis (DM) on pretreatment planning computed tomography images of stage I non‐small cell lung cancer (NSCLC) patients before treatment with stereotactic ablative radiotherapy (SABR).

Methods

We divided 125 primary stage I NSCLC patients (LRR: 34, DM: 22) into training (n = 60) and test datasets (n = 65), and the training dataset was augmented to 260 cases using a synthetic minority oversampling technique. The relapse predictabilities of the conventional wavelet‐based features (WF), topology‐based features [BF, Betti number (BN) map features; iBF, inverted BN map features], and their combined features (BWF, iBWF) were compared. The patients were stratified into high‐risk and low‐risk groups using the medians of the radiomics scores in the training dataset.

Results

For the LRR in the test, the iBF, iBWF, and WF showed statistically significant differences (p < 0.05), and the highest nLPC was obtained for the iBF. For the DM in the test, the iBWF showed a significant difference and the highest nLPC.

Conclusion

The iBF indicated the potential of improving the LRR and DM prediction of stage I NSCLC patients prior to undergoing SABR.

Stratification of prostate cancer patients into low- and high-grade groups using multiparametric magnetic resonance radiomics with dynamic contrast-enhanced image joint histograms

PURPOSE

This study aimed to investigate the potential of stratification of prostate cancer patients into low- and high-grade groups (GGs) using multiparametric magnetic resonance (mpMR) radiomics in conjunction with two-dimensional (2D) joint histograms computed with dynamic contrast-enhanced (DCE) images.

METHODS

A total of 101 prostate cancer regions extracted from the MR images of 44 patients were identified and divided into training (n = 31 with 72 cancer regions) and test datasets (n = 13 with 29 cancer regions). Each dataset included low-grade tumors (International Society of Urological Pathology [ISUP] GG ≤ 2) and high-grade tumors (ISUP GG ≥ 3). A total of 137,970 features consisted of mpMR image (16 types of images in four sequences)-based and joint histogram (DCE images at 10 phases)-based features for each cancer region. Joint histogram features can visualize temporally changing perfusion patterns in prostate cancer based on the joint histograms between different phases or subtraction phases of DCE images. Nine signatures (a set of significant features related to GGs) were determined using the best combinations of features selected using the least absolute shrinkage and selection operator. Further, support vector machine models with the nine signatures were built based on a leave-one-out cross-validation for the training dataset and evaluated with receiver operating characteristic (ROC) curve analysis.

RESULTS

The signature showing the best performance was constructed using six features derived from the joint histograms, DCE original images, and apparent diffusion coefficient maps. The areas under the ROC curves for the training and test datasets were 1.00 and 0.985, respectively.

CONCLUSION

This study suggests that the proposed approach with mpMR radiomics in conjunction with 2D joint histogram computed with DCE images could have the potential to stratify prostate cancer patients into low- and high-GGs.

Synergistic combination of a topologically invariant imaging signature and a biomarker for the accurate prediction of symptomatic radiation pneumonitis before stereotactic ablative radiotherapy for lung cancer: A retrospective analysis

Objectives

We aimed to explore the synergistic combination of a topologically invariant Betti number (BN)-based signature and a biomarker for the accurate prediction of symptomatic (grade ≥2) radiation-induced pneumonitis (RP+) before stereotactic ablative radiotherapy (SABR) for lung cancer.

Methods

A total of 272 SABR cases with early-stage non-small cell lung cancer were chosen for this study. The occurrence of RP+ was predicted using a support vector machine (SVM) model trained with the combined features of the BN-based signature extracted from planning computed tomography (pCT) images and a pretreatment biomarker, serum Krebs von den Lungen-6 (BN+KL-6 model). In all, 242 (20 RP+ and 222 RP–(grade 1)) and 30 cases (8 RP+ and 22 RP–) were used for training and testing the model, respectively. The BN-based features were extracted from BN maps that characterize topologically invariant heterogeneous traits of potential RP+ lung regions on pCT images by applying histogram- and texture-based feature calculations to the maps. The SVM models were built to predict RP+ patients with a BN signature that was constructed based on the least absolute shrinkage and selection operator logistic regression model. The evaluation of the prediction models was performed based on the area under the receiver operating characteristic curves (AUCs) and accuracy in the test. The performance of the BN+KL-6 model was compared to the performance based on the BN, conventional original pCT, and wavelet decomposition (WD) models.

Results

The test AUCs obtained for the BN+KL-6, BN, pCT, and WD models were 0.825, 0.807, 0.642, and 0.545, respectively. The accuracies of the BN+KL-6, BN, pCT, and WD models were found to be 0.724, 0.708, 0.591, and 0.534, respectively.

Conclusion

This study demonstrated the comprehensive performance of the BN+KL-6 model for the prediction of potential RP+ patients before SABR for lung cancer.

Deexcitation Dynamics of Muonic Atoms Revealed by High-Precision Spectroscopy of Electronic K X Rays

We observed electronic K x rays emitted from muonic iron atoms using superconducting transition-edge sensor microcalorimeters. The energy resolution of 5.2 eV in FWHM allowed us to observe the asymmetric broad profile of the electronic characteristic Kα and Kβ x rays together with the hypersatellite K^{h}α x rays around 6 keV. This signature reflects the time-dependent screening of the nuclear charge by the negative muon and the L-shell electrons, accompanied by electron side feeding. Assisted by a simulation, these data clearly reveal the electronic K- and L-shell hole production and their temporal evolution on the 10-20 fs scale during the muon cascade process.

Robust radiogenomics approach to the identification of EGFR mutations among patients with NSCLC from three different countries using topologically invariant Betti numbers

Objectives

To propose a novel robust radiogenomics approach to the identification of epidermal growth factor receptor (EGFR) mutations among patients with non-small cell lung cancer (NSCLC) using Betti numbers (BNs).

Materials and methods

Contrast enhanced computed tomography (CT) images of 194 multi-racial NSCLC patients (79 EGFR mutants and 115 wildtypes) were collected from three different countries using 5 manufacturers’ scanners with a variety of scanning parameters. Ninety-nine cases obtained from the University of Malaya Medical Centre (UMMC) in Malaysia were used for training and validation procedures. Forty-one cases collected from the Kyushu University Hospital (KUH) in Japan and fifty-four cases obtained from The Cancer Imaging Archive (TCIA) in America were used for a test procedure. Radiomic features were obtained from BN maps, which represent topologically invariant heterogeneous characteristics of lung cancer on CT images, by applying histogram- and texture-based feature computations. A BN-based signature was determined using support vector machine (SVM) models with the best combination of features that maximized a robustness index (RI) which defined a higher total area under receiver operating characteristics curves (AUCs) and lower difference of AUCs between the training and the validation. The SVM model was built using the signature and optimized in a five-fold cross validation. The BN-based model was compared to conventional original image (OI)- and wavelet-decomposition (WD)-based models with respect to the RI between the validation and the test.

Results

The BN-based model showed a higher RI of 1.51 compared with the models based on the OI (RI: 1.33) and the WD (RI: 1.29).

Conclusion

The proposed model showed higher robustness than the conventional models in the identification of EGFR mutations among NSCLC patients. The results suggested the robustness of the BN-based approach against variations in image scanner/scanning parameters.

Radiomic features based on Hessian index for prediction of prognosis in head-and-neck cancer patients

This study demonstrated the usefulness of radiomic features based on the Hessian index of differential topology for the prediction of prognosis prior to treatment in head-and-neck (HN) cancer patients. The Hessian index, which can indicate tumor heterogeneity with convex, concave, and other points (saddle points), was calculated as the number of negative eigenvalues of the Hessian matrix at each voxel on computed tomography (CT) images. Three types of signatures were constructed in a training cohort (n = 126), one type each from CT conventional features, Hessian index features, and combined features from the conventional and index feature sets. The prognostic value of the signatures were evaluated using statistically significant difference (p value, log-rank test) to compare the survival curves of low- and high-risk groups. In a test cohort (n = 68), the p values of the models built with conventional, index, combined features, and clinical variables were 2.95 [Formula: see text] 10-2, 1.85 [Formula: see text] 10-2, 3.17 [Formula: see text] 10-2, and 1.87 [Formula: see text] 10-3, respectively. When the features were integrated with clinical variables, the p values of conventional, index, and combined features were 3.53 [Formula: see text] 10-3, 1.28 [Formula: see text] 10-3, and 1.45 [Formula: see text] 10-3, respectively. This result indicates that index features could provide more prognostic information than conventional features and further increase the prognostic value of clinical variables in HN cancer patients.

Semi-automated prediction approach of target shifts using machine learning with anatomical features between planning and pretreatment CT images in prostate radiotherapy

The goal of this study was to develop a semi-automated prediction approach of target shifts using machine learning architecture (MLA) with anatomical features for prostate radiotherapy. Our hypothesis was that anatomical features between planning computed tomography (pCT) and pretreatment cone-beam computed tomography (CBCT) images could be used to predict the target, i.e. clinical target volume (CTV) shifts, with small errors. The pCT and daily CBCT images of 20 patients with prostate cancer were selected. The first 10 patients were employed for the development, and the second 10 patients for a validation test. The CTV position errors between the pCT and CBCT images were determined as reference CTV shifts (teacher data) after an automated bone-based registration. The anatomical features associated with rectum, bladder and prostate were calculated from the pCT and CBCT images. The features were fed as the input with the teacher data into five MLAs, i.e. three types of artificial neural networks, support vector regression (SVR) and random forests. Since the CTV shifts along the left-right direction were negligible, the MLAs were developed along the superior-inferior and anterior-posterior directions. The proposed framework was evaluated from the residual errors between the reference and predicted CTV shifts. In the validation test, the mean residual error with its standard deviation was 1.01 ± 1.09 mm in SVR using only one feature (one click), which was associated with positional difference of the upper rectal wall. The results suggested that MLAs with anatomical features could be useful in prediction of CTV shifts for prostate radiotherapy.

Automated approach for estimation of grade groups for prostate cancer based on histological image feature analysis

BACKGROUND

There is a low reproducibility of the Gleason scores that determine the grade group of prostate cancer given the intra- and interobserver variability among pathologists. This study aimed to develop an automated approach for estimating prostate cancer grade groups based on features obtained from histological image analysis.

METHODS

Fifty-nine patients who underwent radical prostatectomy were selected under the approval of the institutional review board of our university hospital. For estimation, we followed the grade group criteria provided by the International Society of Urological Pathology in 2014. One hundred eight specimen slides obtained from the patients were digitized to extract 110 regions of interest (ROI) from hematoxylin and eosin-stained histological images using a digital whole slide scanner at ×20 magnification with a pixel size of 0.4 μm. Each color pixel value in the ROI was decomposed into six intensities corresponding to the RGB (red, green, and blue) and HSV (hue, saturation, and value) color models. Image features were extracted by histological image analysis, obtaining 54 features from the ROI based on histogram and texture analyses in the six types of decomposed histological images. Then, 40 representative features were selected from the 324 histological image features based on statistically significant differences (P < .05) between the mean image feature values for high (≥3, Gleason score ≥4 + 3) and low (≤2, Gleason score ≤3 + 4) grade groups. The relationship between grade groups and the most representative image feature (ie, complexity) was approximated using regression to estimate real-number grade groups defined by continuous numerical grading. Finally, the grade groups were expressed as the conventional grade groups (ie, integers from 1 to 5) using a piecewise step function.

RESULTS

The grade groups were correctly estimated by the proposed approach without errors on training (70 ROIs) and validation (40 ROIs) data.

CONCLUSIONS

Our results suggest that the proposed approach may support pathologists during the evaluation of grade groups for prostate cancer, thus mitigating intra- and interobserver variability.

Homological radiomics analysis for prognostic prediction in lung cancer patients

PURPOSE

This study explored a novel homological analysis method for prognostic prediction in lung cancer patients.

MATERIALS AND METHODS

The potential of homology-based radiomic features (HFs) was investigated by comparing HFs to conventional wavelet-based radiomic features (WFs) and combined radiomic features consisting of HFs and WFs (HWFs), using training (n = 135) and validation (n = 70) datasets, and Kaplan-Meier analysis. A total of 13,824 HFs were derived through homology-based texture analysis using Betti numbers, which represent the topologically invariant morphological characteristics of lung cancer. The prognostic potential of HFs was evaluated using statistically significant differences (p-values, log-rank test) to compare the survival curves of high- and low-risk patients. Those patients were stratified into high- and low-risk groups using the medians of the radiomic scores of signatures constructed with an elastic-net-regularized Cox proportional hazard model. Furthermore, deep learning (DL) based on AlexNet was utilized to compare HFs by stratifying patients into the two groups using a network that was pre-trained with over one million natural images from an ImageNet database.

RESULTS

For the training dataset, the p-values between the two survival curves were 6.7 × 10^-6 (HF), 5.9 × 10^-3 (WF), 7.4 × 10^-6 (HWF), and 1.1 × 10^-3 (DL). The p-values for the validation dataset were 3.4 × 10^-5 (HF), 6.7 × 10^-1 (WF), 1.7 × 10^-7 (HWF), and 1.2 × 10^-1 (DL).

CONCLUSION

This study demonstrates the excellent potential of HFs for prognostic prediction in lung cancer patients.

Radiomics with artificial intelligence for precision medicine in radiation therapy

Recently, the concept of radiomics has emerged from radiation oncology. It is a novel approach for solving the issues of precision medicine and how it can be performed, based on multimodality medical images that are non-invasive, fast and low in cost. Radiomics is the comprehensive analysis of massive numbers of medical images in order to extract a large number of phenotypic features (radiomic biomarkers) reflecting cancer traits, and it explores the associations between the features and patients' prognoses in order to improve decision-making in precision medicine. Individual patients can be stratified into subtypes based on radiomic biomarkers that contain information about cancer traits that determine the patient's prognosis. Machine-learning algorithms of AI are boosting the powers of radiomics for prediction of prognoses or factors associated with treatment strategies, such as survival time, recurrence, adverse events, and subtypes. Therefore, radiomic approaches, in combination with AI, may potentially enable practical use of precision medicine in radiation therapy by predicting outcomes and toxicity for individual patients.

Potentials of radiomics for cancer diagnosis and treatment in comparison with computer-aided diagnosis

Computer-aided diagnosis (CAD) is a field that is essentially based on pattern recognition that improves the accuracy of a diagnosis made by a physician who takes into account the computer's "opinion" derived from the quantitative analysis of radiological images. Radiomics is a field based on data science that massively and comprehensively analyzes a large number of medical images to extract a large number of phenotypic features reflecting disease traits, and explores the associations between the features and patients' prognoses for precision medicine. According to the definitions for both, you may think that radiomics is not a paraphrase of CAD, but you may also think that these definitions are "image manipulation". However, there are common and different features between the two fields. This review paper elaborates on these common and different features and introduces the potential of radiomics for cancer diagnosis and treatment by comparing it with CAD.

Diel patterns of larval drift of honmoroko Gnathopogon caerulescens in an inlet of Ibanaiko Lagoon, Lake Biwa, Japan

Diel drift patterns of larvae of the endangered cyprinid Gnathopogon caerelescens in an inlet of the Ibanaiko Lagoon, connected to Lake Biwa in Japan, were assessed in April 2012. Peak occurrence of yolk-sac larvae was within a few hours after dark. Drift of newly hatched larvae is considered to be an important biological mechanism that ensures larval dispersal and recruitment from the inlets (spawning grounds) to the lagoon which functions as a nursery ground.

Measurements of the neutron activation cross sections for Bi and Co at 386 MeV

Neutron activation cross sections for Bi and Co at 386 MeV were measured by activation method. A quasi-monoenergetic neutron beam was produced using the (7)Li(p,n) reaction. The energy spectrum of these neutrons has a high-energy peak (386 MeV) and a low-energy tail. Two neutron beams, 0° and 25° from the proton beam axis, were used for sample irradiation, enabling a correction for the contribution of the low-energy neutrons. The neutron-induced activation cross sections were estimated by subtracting the reaction rates of irradiated samples for 25° irradiation from those of 0° irradiation. The measured cross sections were compared with the findings of other studies, evaluated in relation to nuclear data files and the calculated data by Particle and Heavy Ion Transport code System code.