Comparative Analysis of Repeatability in CT Radiomics and Dosiomics Features under Image Perturbation: A Study in Cervical Cancer Patients.

This study aims to evaluate the repeatability of radiomics and dosiomics features via image perturbation of patients with cervical cancer. A total of 304 cervical cancer patients with planning CT images and dose maps were retrospectively included. Random translation, rotation, and contour randomization were applied to CT images and dose maps before radiomics feature extraction. The repeatability of radiomics and dosiomics features was assessed using intra-class correlation of coefficient (ICC). Pearson correlation coefficient (r) was adopted to quantify the correlation between the image characteristics and feature repeatability. In general, the repeatability of dosiomics features was lower compared with CT radiomics features, especially after small-sigma Laplacian-of-Gaussian (LoG) and wavelet filtering. More repeatable features (ICC > 0.9) were observed when extracted from the original, Large-sigma LoG filtered, and LLL-/LLH-wavelet filtered images. Positive correlations were found between image entropy and high-repeatable feature number in both CT and dose (r = 0.56, 0.68). Radiomics features showed higher repeatability compared to dosiomics features. These findings highlight the potential of radiomics features for robust quantitative imaging analysis in cervical cancer patients, while suggesting the need for further refinement of dosiomics approaches to enhance their repeatability.

Automatic planning for functional lung avoidance radiotherapy based on function-guided beam angle selection and plan optimization.

Objective.This study aims to develop a fully automatic planning framework for functional lung avoidance radiotherapy (AP-FLART).Approach.The AP-FLART integrates a dosimetric score-based beam angle selection method and a meta-optimization-based plan optimization method, both of which incorporate lung function information to guide dose redirection from high functional lung (HFL) to low functional lung (LFL). It is applicable to both contour-based FLART (cFLART) and voxel-based FLART (vFLART) optimization options. A cohort of 18 lung cancer patient cases underwent planning-CT and SPECT perfusion scans were collected. AP-FLART was applied to generate conventional RT (ConvRT), cFLART, and vFLART plans for all cases. We compared automatic against manual ConvRT plans as well as automatic ConvRT against FLART plans, to evaluate the effectiveness of AP-FLART. Ablation studies were performed to evaluate the contribution of function-guided beam angle selection and plan optimization to dose redirection.Main results.Automatic ConvRT plans generated by AP-FLART exhibited similar quality compared to manual counterparts. Furthermore, compared to automatic ConvRT plans, HFL mean dose,V20, andV5were significantly reduced by 1.13 Gy (p< .001), 2.01% (p< .001), and 6.66% (p< .001) respectively for cFLART plans. Besides, vFLART plans showed a decrease in lung functionally weighted mean dose by 0.64 Gy (p< .01),fV20by 0.90% (p= 0.099), andfV5by 5.07% (p< .01) respectively. Though inferior conformity was observed, all dose constraints were well satisfied. The ablation study results indicated that both function-guided beam angle selection and plan optimization significantly contributed to dose redirection.Significance.AP-FLART can effectively redirect doses from HFL to LFL without severely degrading conventional dose metrics, producing high-quality FLART plans. It has the potential to advance the research and clinical application of FLART by providing labor-free, consistent, and high-quality plans.

The safety and efficacy of five surgical treatments in prostate enucleation: a network meta-analysis.

PURPOSE: The aim of our study was to investigate the comparative outcomes of five different energy types on surgical efficacy and postoperative recovery in patients with benign prostate hyperplasia. METHODS: The literature was systematically reviewed on December 1st, 2023, encompassing studies retrieved from PubMed, Embase, Web of Science, and The Cochrane Library databases that incorporated clinical studies of holmium laser enucleation of the prostate (HoLEP), Thulium:YAG laser enucleation of the prostate (ThuLEP), transurethral plasmakinetic enucleation of prostate (PKEP), diode laser enucleation of the prostate (DiLEP) and thulium fiber laser enucleation of the prostate (ThuFLEP) in the treatment of prostatic hyperplasia. Two independent reviewers extracted study data and conducted quality assessments using the Cochrane Collaboration's Risk of Bias tool and Newcastle-Ottawa Scale (NOS). Network meta-analysis (NMA) was employed to indirectly analyze the outcomes of endoscopic enucleation of the prostate (EEP) techniques. RESULTS: The study included a total of 38 studies, comprising 21 non-randomized controlled trials (nRCTs) and 17 randomized controlled trials (RCTs), incorporating five distinct techniques: holmium laser, Thulium:YAG laser, bipolar plasma, diode laser and thulium fiber laser. In comparing treatment durations, ThuLEP and HoLEP had shorter overall hospital stays than PKEP, while the enucleation time of ThuLEP and HoLEP was shorter than that of ThuFLEP. Moreover, the enucleation tissue weight of both thulium fiber laser and holmium laser was heavier than bipolar plasma. However, the analysis did not reveal any statistically significant variation in complications among the various types of enucleation. In postoperative follow-up, the IPSS at 3 months post-operation was superior in the Thulium:YAG laser group compared to the holmium laser group. The thulium fiber laser technique demonstrated significant advantages over other enucleation methods in terms of QoL and PVR at 12 months after surgery. CONCLUSION: Theoretical properties may vary among different energy sources; however, there are no discernible clinical differences in operation-related parameters, postoperative complications, and postoperative follow-up. Therefore, the choice of laser does not significantly impact the outcome. However, due to the limited number of included studies, future research should focus on larger sample sizes and multicenter investigations to further validate the findings of this study.

First-line hepatic arterial infusion chemotherapy plus lenvatinib and PD-(L)1 inhibitors versus systemic chemotherapy alone or with PD-(L)1 inhibitors in unresectable intrahepatic cholangiocarcinoma.

PURPOSE: Limited treatment options exist for unresectable intrahepatic cholangiocarcinoma (ICC), with systemic chemotherapy (SC) serving as the primary approach. This study aimed to assess the effectiveness of first-line hepatic arterial infusion chemotherapy (HAIC) in combination with lenvatinib and PD-(L)1 inhibitors (HLP) compared to SC combined with PD-(L)1 inhibitors (SCP) or SC alone in treating unresectable ICC. METHODS: Patient with unresectable ICC who underwent first-line treatment with HLP, SCP or SC from January 2016 to December 2022 were retrospectively analyzed. The study evaluated and compared efficacy and safety outcomes across the three treatment groups. RESULTS: The study comprised 42, 49, and 50 patients in the HLP, SCP, and SC groups, respectively. Median progression-free survival (PFS) times were 30.0, 10.2, and 6.5 months for HLP, SCP, and SC groups. While the SC group had a median overall survival (OS) time of 21.8 months, the HLP and SCP groups hadn't reached median OS. The HLP group demonstrated significantly superior PFS (p < 0.001) and OS (p = 0.014) compared to the others. Moreover, the HLP group exhibited the highest objective response rate (ORR) at 50.0% and the highest disease control rate (DCR) at 88.1%, surpassing the SC group (ORR, 6.0%; DCR, 52.0%) and SCP group (ORR, 18.4%; DCR, 73.5%) (p < 0.05). Generally, the HLP group reported fewer grades 3-4 adverse events (AEs) compared with others. CONCLUSION: In contrast to systemic chemotherapy with or without PD-(L)1 inhibitors, the triple combination therapy incorporating HAIC, lenvatinib, and PD-(L)1 inhibitors showcased favorable survival benefits and manageable adverse events for unresectable ICC.

CDDO, an Anti-Inflammatory and Antioxidant Compound, Attenuates Vasospasm and Neuronal Cell Apoptosis in Rats Subjected to Experimental Subarachnoid Hemorrhage.

Subarachnoid hemorrhage (SAH) is a type of stroke caused by bleeding into the subarachnoid space. SAH is a medical emergency and requires prompt treatment to prevent complications such as seizures, stroke, or other brain damage. Treatment options may include surgery, medication, or a combination of both. 2-Cyano-3,12-dioxoolean-1,9-dien-28-oic acid (CDDO), a compound with anti-inflammatory and antioxidant properties, is currently being investigated as a potential treatment for various diseases, including chronic kidney disease and pulmonary arterial hypertension. In this study, the effects of CDDO on rats subjected to SAH were evaluated. Male Sprague-Dawley rats were divided into four groups (n = 6/group): (1) control group, (2) SAH group, (3) SAH + low-dose CDDO (10 mg/kg injected into the subarachnoid space at 24 h after SAH) group, and (4) SAH + high-dose CDDO (20 mg/kg) group. CDDO improved SAH-induced poor neurological outcomes and reduced vasospasm in the basal artery following SAH. It also decreased the SAH-induced expression of proinflammatory cytokines such as TNF-α, IL-1ß, and IL-6 in both the cerebrospinal fluid and serum samples as determined by ELISA. A Western blot analysis confirmed an increase in the p-NF-κB protein level after SAH, but it was significantly decreased with CDDO intervention. Immunofluorescence staining highlighted the proliferation of microglia and astrocytes as well as apoptosis of the neuronal cells after SAH, and treatment with CDDO markedly reduced the proliferation of these glial cells and apoptosis of the neuronal cells. The early administration of CDDO after SAH may effectively mitigate neuronal apoptosis and vasospasm by suppressing inflammation.

Health care-associated infections after surgical treatment of ruptured intracranial aneurysms.

BACKGROUND: Aneurysmal subarachnoid hemorrhage (SAH) is catastrophic, and microsurgery for ruptured intracranial aneurysms is one of the preventive modalities for rebleeding. However, patients remain at high risk of medical morbidities after surgery, one of the most important of which is health care-associated infections (HAIs). We analyzed the incidence and risk factors of HAIs, as well as their association with the outcomes after surgical treatment of ruptured aneurysms. METHODS: We retrospectively enrolled 607 patients with SAH who had undergone surgery for intracranial aneurysms. Information was retrieved from the database using codes of the International Classification of Diseases, Ninth Revision, Clinical Modification. RESULTS: Of the 607 patients, 203 were male and 404 were female. HAIs occurred in 113 patients, accounting for 18.6 % of the population. The independent risk factors for HAIs included age ((p = 0.035), hypertension ((p = 0.042), convulsion ((p = 0.023), external ventricular drain ((p = 0.035), ventricular shunt ((p = 0.033), and blood transfusion ((p = 0.001). The mean length of hospital stay was 25.3 ± 18.2 and 18.8 ± 15.3 days for patients with and without HAIs, respectively ((p = 0.001). The in-hospital mortality rates were 11.5 % in the HAIs group, and 14.0 % in the non-HAIs group ((p = 0.490). CONCLUSION: HAIs are a frequent complication in patients with SAH who underwent surgery for ruptured intracranial aneurysms. The length of hospital stay is remarkably longer for patients with HAIs, and to recognize and reduce the modifiable risks should be implemented to improve the quality of patient care.

Cost-effectiveness analysis of different anesthesia strategies for transperineal MRI/US fusion prostate biopsy.

This study aims to conduct a cost-effectiveness analysis of three different anesthesia strategies, namely chatting while under local anesthesia (Chat-LA), total intravenous anesthesia (TIVA), and general anesthesia with laryngeal mask airway (GA-LMA), employed in transperineal magnetic resonance imaging (MRI)/ultrasound (US) fusion prostate biopsy (TP-MUF-PB). A retrospective study was conducted involving 1202 patients who underwent TP-MUF-PB from June 2016 to April 2023 at The First Affiliated Hospital of Soochow University (Suzhou, China). Clinical data and outcomes, including total costs, complications, and quality-adjusted life years (QALYs), were compared. Probability sensitivity and subgroup analyses were also performed. Chat-LA was found to be the most cost-effective option, outperforming both TIVA and GA-LMA. However, subgroup analyses revealed that in younger patients (under 65 years old) and those with smaller prostate volumes (<40 ml), TIVA emerged as a more cost-effective strategy. While Chat-LA may generally be the most cost-effective and safer anesthesia method for TP-MUF-PB, personalization of anesthesia strategies is crucial, considering specific patient demographics such as age and prostate volume.

Deep learning-based bronchial tree-guided semi-automatic segmentation of pulmonary segments in computed tomography images.

Background: Pulmonary segments are valuable because they can provide more precise localization and intricate details of lung cancer than lung lobes. With advances in precision therapy, there is an increasing demand for the identification and visualization of pulmonary segments in computed tomography (CT) images to aid in the precise treatment of lung cancer. This study aimed to integrate multiple deep-learning models to accurately segment pulmonary segments in CT images using a bronchial tree (BT)-based approach. Methods: The proposed segmentation method for pulmonary segments using the BT-based approach comprised the following five essential steps: (I) segmentation of the lung using a U-Net (R231) (public access) model; (II) segmentation of the lobes using a V-Net (self-developed) model; (III) segmentation of the airway using a combination of a differential geometric approach method and a BronchiNet (public access) model; (IV) labeling of the BT branches based on anatomical position; and (V) segmentation of the pulmonary segments based on the distance of each voxel to the labeled BT branches. This five-step process was applied to 14 high-resolution breath-hold CT images and compared against manual segmentations for evaluation. Results: For the lung segmentation, the lung mask had a mean dice similarity coefficient (DSC) of 0.98±0.03. For the lobe segmentation, the V-Net model had a mean DSC of 0.94±0.06. For the airway segmentation, the average total length of the segmented airway trees per image scan was 1,902.8±502.1 mm, and the average number of the maximum airway tree generations was 8.5±1.3. For the segmentation of the pulmonary segments, the proposed method had a DSC of 0.73±0.11 and a mean surface distance of 6.1±2.9 mm. Conclusions: This study demonstrated the feasibility of combining multiple deep-learning models for the auxiliary segmentation of pulmonary segments on CT images using a BT-based approach. The results highlighted the potential of the BT-based method for the semi-automatic segmentation of the pulmonary segment.

Long-term survival after primary decompressive craniectomy for severe traumatic brain injury: an observational study from 1 to 17 years.

Primary decompressive craniectomy (DC) is carried out to prevent intracranial hypertension after removal of mass lesions resulting from traumatic brain injury (TBI). While primary DC can be a life-saving intervention, significant mortality risks persist during the follow-up period. This study was undertaken to investigate the long-term survival rate and ascertain the risk factors of mortality in TBI patients who underwent primary DC. We enrolled 162 head-injured patients undergoing primary DC in this retrospective study. The primary focus was on long-term mortality, which was monitored over a range of 12 to 209 months post-TBI. We compared the clinical parameters of survivors and non-survivors, and used a multivariate logistic regression model to adjust for independent risk factors of long-term mortality. For the TBI patients who survived the initial hospitalization period following surgery, the average duration of follow-up was 106.58 ± 65.45 months. The recorded long-term survival rate of all patients was 56.2% (91/162). Multivariate logistic regression analysis revealed that age (odds ratio, 95% confidence interval = 1.12, 1.07-1.18; p < 0.01) and the status of basal cisterns (absent versus normal; odds ratio, 95% confidence interval = 9.32, 2.05-42.40; p < 0.01) were the two independent risk factors linked to long-term mortality. In conclusion, this study indicated a survival rate of 56.2% for patients subjected to primary DC for TBI, with at least a one-year follow-up. Key risk factors associated with long-term mortality were advanced age and absent basal cisterns, critical considerations for developing effective TBI management strategies.

Epstein-Barr virus-based prognostic model in nodular sclerosis classic Hodgkin lymphoma.

The role of Epstein-Barr virus (EBV) in lymphoma cells of nodular sclerosis classic Hodgkin lymphoma (NScHL) is controversial. Our aim was to explore this and establish a clinically feasible model for risk stratification. We interrogated data from 542 consecutive subjects with NScHL receiving ABVD therapy and demonstrated EBV-infection in their lymphoma cells with EBV-encoded small RNAs (EBERs) in situ hybridization. Subjects were divided into training and validation datasets. As data from the training dataset suggested EBERs-positivity was the only independent prognostic factor for both progression-free survival (PFS) and overall survival (OS), we developed corresponding prognostic models based on it. Our models showed excellent performance in both training and validation cohort. These data indicate the close association of EBV infection and the outcomes of persons with NScHL receiving ABVD. Additionally, our newly developed models should help physicians estimate prognosis and select individualized therapy.

Response to programmed cell death protein 1 antibody in patients with Epstein-Barr virus-associated intrahepatic cholangiocarcinoma.

AIM: Epstein-Barr virus-associated intrahepatic cholangiocarcinoma (EBVaICC) has a distinct genomic profile and increased CD3+ and CD8+ T cells infiltration. However, the efficacy of immunotherapy in EBVaICC remains largely unknown. This study aimed to assess the efficacy of programmed cell death protein 1 (PD-1) antibody therapy in EBVaICC. METHODS: Patients with metastatic biliary tract cancer (BTC) diagnosed at Sun Yat-sen University Cancer Center from January 2016 to December 2021 were identified. In situ hybridisation was performed to detect EBV. Overall survival (OS) and progression-free survival (PFS) were measured. RESULTS: A total of 698 patients with metastatic BTC were identified, of whom 39 (5.6%) had EBVaICC. Among the 136 patients who were not administered PD-1 antibody, the OS was similar between patients with EBVaICC and EBV-negative ICC (median OS 12.5 versus 9.5 months, respectively; P = 0.692). For the 205 patients who were administered PD-1 antibody, patients with EBVaICC had significantly longer OS than patients with EBV-negative ICC (median OS 24.9 versus 11.9 months, respectively; P = 0.004). Seventeen patients with EBVaICC were administered PD-1 antibody. Eight patients (47%) achieved a partial response, and 17 patients achieved disease control. The median PFS was 17.5 months. CONCLUSIONS: This study identified a clinically actionable subset of patients with EBVaICC with a promising response to the PD-1 antibody.

Noninvasive imaging signatures of HER2 and HR using ADC in invasive breast cancer: repeatability, reproducibility, and association with pathological complete response to neoadjuvant chemotherapy.

BACKGROUND: The immunohistochemical test (IHC) of HER2 and HR can provide prognostic information and treatment guidance for invasive breast cancer patients. We aimed to develop noninvasive image signatures ISHER2 and ISHR of HER2 and HR, respectively. We independently evaluate their repeatability, reproducibility, and association with pathological complete response (pCR) to neoadjuvant chemotherapy. METHODS: Pre-treatment DWI, IHC receptor status HER2/HR, and pCR to neoadjuvant chemotherapy of 222 patients from the multi-institutional ACRIN 6698 trial were retrospectively collected. They were pre-separated for development, independent validation, and test-retest. 1316 image features were extracted from DWI-derived ADC maps within manual tumor segmentations. ISHER2 and ISHR were developed by RIDGE logistic regression using non-redundant and test-retest reproducible features relevant to IHC receptor status. We evaluated their association with pCR using area under receiver operating curve (AUC) and odds ratio (OR) after binarization. Their reproducibility was further evaluated using the test-retest set with intra-class coefficient of correlation (ICC). RESULTS: A 5-feature ISHER2 targeting HER2 was developed (AUC = 0.70, 95% CI 0.59 to 0.82) and validated (AUC = 0.72, 95% CI 0.58 to 0.86) with high perturbation repeatability (ICC = 0.92) and test-retest reproducibility (ICC = 0.83). ISHR was developed using 5 features with higher association with HR during development (AUC = 0.75, 95% CI 0.66 to 0.84) and validation (AUC = 0.74, 95% CI 0.61 to 0.86) and similar repeatability (ICC = 0.91) and reproducibility (ICC = 0.82). Both image signatures showed significant associations with pCR with AUC of 0.65 (95% CI 0.50 to 0.80) for ISHER2 and 0.64 (95% CI 0.50 to 0.78) for ISHER2 in the validation cohort. Patients with high ISHER2 were more likely to achieve pCR to neoadjuvant chemotherapy with validation OR of 4.73 (95% CI 1.64 to 13.65, P value = 0.006). Low ISHR patients had higher pCR with OR = 0.29 (95% CI 0.10 to 0.81, P value = 0.021). Molecular subtypes derived from the image signatures showed comparable pCR prediction values to IHC-based molecular subtypes (P value > 0.05). CONCLUSION: Robust ADC-based image signatures were developed and validated for noninvasive evaluation of IHC receptors HER2 and HR. We also confirmed their value in predicting treatment response to neoadjuvant chemotherapy. Further evaluations in treatment guidance are warranted to fully validate their potential as IHC surrogates.

Respiratory Invariant Textures From Static Computed Tomography Scans for Explainable Lung Function Characterization.

PURPOSE: The inherent characteristics of lung tissue independent of breathing maneuvers may provide fundamental information for function assessment. This paper attempted to correlate textural signatures from computed tomography (CT) with pulmonary function measurements. MATERIALS AND METHODS: Twenty-one lung cancer patients with thoracic 4-dimensional CT, DTPA-single-photon emission CT ventilation ( VNM ) scans, and available spirometry measurements (forced expiratory volume in 1 s, FEV 1 ; forced vital capacity, FVC; and FEV 1 /FVC) were collected. In subregional feature discovery, function-correlated candidates were identified from 79 radiomic features based on the statistical strength to differentiate defected/nondefected lung regions. Feature maps (FMs) of selected candidates were generated on 4-dimensional CT phases for a voxel-wise feature distribution study. Quantitative metrics were applied for validations, including the Spearman correlation coefficient (SCC) and the Dice similarity coefficient for FM- VNM spatial agreement assessments, intraclass correlation coefficient for FM interphase robustness evaluations, and FM-spirometry comparisons. RESULTS: At the subregion level, 8 function-correlated features were identified (effect size>0.330). The FMs of candidates yielded moderate-to-strong voxel-wise correlations with the reference VNM . The FMs of gray level dependence matrix dependence nonuniformity showed the highest robust (intraclass correlation coefficient=0.96 and P <0.0001) spatial correlation, with median SCCs ranging from 0.54 to 0.59 throughout the 10 breathing phases. Its phase-averaged FM achieved a median SCC of 0.60, a median Dice similarity coefficient of 0.60 (0.65) for high (low) functional lung volumes, and a correlation of 0.565 (0.646) between the spatially averaged feature values and FEV 1 (FEV 1 /FVC). CONCLUSIONS: The results provide further insight into the underlying association of specific pulmonary textures with both local ( VNM ) and global (FEV 1 /FVC, FEV 1 ) functions. Further validations of the FM generalizability and the standardization of implementation protocols are warranted before clinically relevant investigations.

A super-voxel-based method for generating surrogate lung ventilation images from CT.

Purpose: This study aimed to develop and evaluate CTVISVD , a super-voxel-based method for surrogate computed tomography ventilation imaging (CTVI). Methods and Materials: The study used four-dimensional CT (4DCT) and single-photon emission computed tomography (SPECT) images and corresponding lung masks from 21 patients with lung cancer obtained from the Ventilation And Medical Pulmonary Image Registration Evaluation dataset. The lung volume of the exhale CT for each patient was segmented into hundreds of super-voxels using the Simple Linear Iterative Clustering (SLIC) method. These super-voxel segments were applied to the CT and SPECT images to calculate the mean density values (D mean) and mean ventilation values (Vent mean), respectively. The final CT-derived ventilation images were generated by interpolation from the D mean values to yield CTVISVD. For the performance evaluation, the voxel- and region-wise differences between CTVISVD and SPECT were compared using Spearman's correlation and the Dice similarity coefficient index. Additionally, images were generated using two deformable image registration (DIR)-based methods, CTVIHU and CTVIJac, and compared with the SPECT images. Results: The correlation between the D mean and Vent mean of the super-voxel was 0.59 ± 0.09, representing a moderate-to-high correlation at the super-voxel level. In the voxel-wise evaluation, the CTVISVD method achieved a stronger average correlation (0.62 ± 0.10) with SPECT, which was significantly better than the correlations achieved with the CTVIHU (0.33 ± 0.14, p < 0.05) and CTVIJac (0.23 ± 0.11, p < 0.05) methods. For the region-wise evaluation, the Dice similarity coefficient of the high functional region for CTVISVD (0.63 ± 0.07) was significantly higher than the corresponding values for the CTVIHU (0.43 ± 0.08, p < 0.05) and CTVIJac (0.42 ± 0.05, p < 0.05) methods. Conclusion: The strong correlation between CTVISVD and SPECT demonstrates the potential usefulness of this novel method of ventilation estimation for surrogate ventilation imaging.

Clinicopathologic features, tumor immune microenvironment and genomic landscape of EBV-related and EBV-unrelated poorly differentiated nonkeratinizing squamous cell carcinoma of the thymus.

OBJECTIVES: Knowledge regarding thymic EBV-related poorly differentiated nonkeratinizing squamous cell carcinoma (PDNKSCC), also known as lymphoepithelial carcinoma (LEC), is extremely limited due to its rarity. MATERIALS AND METHODS: This multi-institutional study enrolled 85 patients with thymic PDNKSCC. DNA in situ hybridization was performed to evaluate the EBV status of all 85 cases. Immunohistochemistry and next generation sequencing were performed to compare the differences in the clinicopathological and molecular features between EBV-related and EBV-unrelated PDNKSCC. Tumor-infiltrating lymphocytes (TILs) were also analyzed by these methods. RESULTS: The 85 cases were classified into 27 EBV-related PDNKSCCs (31.8 %) and 58 EBV-unrelated PDNKSCCs (68.2 %) according to the EBV status, and 35 Lymphoepithelioma pattern (LP) (41.2 %) and 50 desmoplastic pattern (DP) (58.8 %) according to the histological characteristics. Compared to the EBV-unrelated PDNKSCC, EBV-related PDNKSCC showed a younger patient predominance and more commonly displayed a LP subtype. Additionally, LP-type cases were divided into two groups: Group 1 (EBV-related, 20/85) and Group 2 (EBV-unrelated, 15/85); the DP-type cases were divided into Group 3 (EBV-unrelated, 43/85) and Group 4 (EBV-related, 7/85). The four Groups showed a significant association with patients' OS and PFS. EBV-related PDNKSCC had significantly higher PD-L1 + tumor cells (TCs) and PD-L1 + and CD8 + immune cells (ICs) than EBV-unrelated PDNKSCC. The tumor microenvironment immune type (TMIT) I (PDL1-Tumor+/CD8-High) was more common in EBV-related PDNKSCC, especially in Group 1(LP and EBV related) with more than 90 % cases belonged to TMIT I. Molecular analysis demonstrated that EBV-related PDNKSCC had a significantly higher tumour mutational burden and frequency of somatic mutations than EBV-unrelated cases. CONCLUSIONS: EBV-related PDNKSCC, especially the Group 1, could be a candidate for immunotherapy and EBV positivity may provide an indication for the selection of targeted therapy due to their high tumour mutational burden.

TOMM40 Genetic Variants Cause Neuroinflammation in Alzheimer's Disease.

Translocase of outer mitochondrial membrane 40 (TOMM40) is located in the outer membrane of mitochondria. TOMM40 is essential for protein import into mitochondria. TOMM40 genetic variants are believed to increase the risk of Alzheimer's disease (AD) in different populations. In this study, three exonic variants (rs772262361, rs157581, and rs11556505) and three intronic variants (rs157582, rs184017, and rs2075650) of the TOMM40 gene were identified from Taiwanese AD patients using next-generation sequencing. Associations between the three TOMM40 exonic variants and AD susceptibility were further evaluated in another AD cohort. Our results showed that rs157581 (c.339T > C, p.Phe113Leu, F113L) and rs11556505 (c.393C > T, p.Phe131Leu, F131L) were associated with an increased risk of AD. We further utilized cell models to examine the role of TOMM40 variation in mitochondrial dysfunction that causes microglial activation and neuroinflammation. When expressed in BV2 microglial cells, the AD-associated mutant (F113L) or (F131L) TOMM40 induced mitochondrial dysfunction and oxidative stress-induced activation of microglia and NLRP3 inflammasome. Pro-inflammatory TNF-α, IL-1ß, and IL-6 released by mutant (F113L) or (F131L) TOMM40-activated BV2 microglial cells caused cell death of hippocampal neurons. Taiwanese AD patients carrying TOMM40 missense (F113L) or (F131L) variants displayed an increased plasma level of inflammatory cytokines IL-6, IL-18, IL-33, and COX-2. Our results provide evidence that TOMM40 exonic variants, including rs157581 (F113L) and rs11556505 (F131L), increase the AD risk of the Taiwanese population. Further studies suggest that AD-associated mutant (F113L) or (F131L) TOMM40 cause the neurotoxicity of hippocampal neurons by inducing the activation of microglia and NLRP3 inflammasome and the release of pro-inflammatory cytokines.

Deep learning attention-guided radiomics for COVID-19 chest radiograph classification.

Background: Accurate assessment of coronavirus disease 2019 (COVID-19) lung involvement through chest radiograph plays an important role in effective management of the infection. This study aims to develop a two-step feature merging method to integrate image features from deep learning and radiomics to differentiate COVID-19, non-COVID-19 pneumonia and normal chest radiographs (CXR). Methods: In this study, a deformable convolutional neural network (deformable CNN) was developed and used as a feature extractor to obtain 1,024-dimensional deep learning latent representation (DLR) features. Then 1,069-dimensional radiomics features were extracted from the region of interest (ROI) guided by deformable CNN's attention. The two feature sets were concatenated to generate a merged feature set for classification. For comparative experiments, the same process has been applied to the DLR-only feature set for verifying the effectiveness of feature concatenation. Results: Using the merged feature set resulted in an overall average accuracy of 91.0% for three-class classification, representing a statistically significant improvement of 0.6% compared to the DLR-only classification. The recall and precision of classification into the COVID-19 class were 0.926 and 0.976, respectively. The feature merging method was shown to significantly improve the classification performance as compared to using only deep learning features, regardless of choice of classifier (P value <0.0001). Three classes' F1-score were 0.892, 0.890, and 0.950 correspondingly (i.e., normal, non-COVID-19 pneumonia, COVID-19). Conclusions: A two-step COVID-19 classification framework integrating information from both DLR and radiomics features (guided by deep learning attention mechanism) has been developed. The proposed feature merging method has been shown to improve the performance of chest radiograph classification as compared to the case of using only deep learning features.

New model of PIRADS and adjusted prostatespecific antigen density of peripheral zone improves the detection rate of initial prostate biopsy: a diagnostic study.

This study explored a new model of Prostate Imaging Reporting and Data System (PIRADS) and adjusted prostate-specific antigen density of peripheral zone (aPSADPZ) for predicting the occurrence of prostate cancer (PCa) and clinically significant prostate cancer (csPCa). The demographic and clinical characteristics of 853 patients were recorded. Prostate-specific antigen (PSA), PSA density (PSAD), PSAD of peripheral zone (PSADPZ), aPSADPZ, and peripheral zone volume ratio (PZ-ratio) were calculated and subjected to receiver operating characteristic (ROC) curve analysis. The calibration and discrimination abilities of new nomograms were verified with the calibration curve and area under the ROC curve (AUC). The clinical benefits of these models were evaluated by decision curve analysis and clinical impact curves. The AUCs of PSA, PSAD, PSADPZ, aPSADPZ, and PZ-ratio were 0.669, 0.762, 0.659, 0.812, and 0.748 for PCa diagnosis, while 0.713, 0.788, 0.694, 0.828, and 0.735 for csPCa diagnosis, respectively. All nomograms displayed higher net benefit and better overall calibration than the scenarios for predicting the occurrence of PCa or csPCa. The new model significantly improved the diagnostic accuracy of PCa (0.945 vs 0.830, P < 0.01) and csPCa (0.937 vs 0.845, P < 0.01) compared with the base model. In addition, the number of patients with PCa and csPCa predicted by the new model was in good agreement with the actual number of patients with PCa and csPCa in high-risk threshold. This study demonstrates that aPSADPZ has a higher predictive accuracy for PCa diagnosis than the conventional indicators. Combining aPSADPZ with PIRADS can improve PCa diagnosis and avoid unnecessary biopsies.

Therapeutic Effect of Mitochondrial Division Inhibitor-1 (Mdivi-1) on Hyperglycemia-Exacerbated Early and Delayed Brain Injuries after Experimental Subarachnoid Hemorrhage.

Background: Neurological deficits following subarachnoid hemorrhage (SAH) are caused by early or delayed brain injuries. Our previous studies have demonstrated that hyperglycemia induces profound neuronal apoptosis of the cerebral cortex. Morphologically, we found that hyperglycemia exacerbated late vasospasm following SAH. Thus, our previous studies strongly suggest that post-SAH hyperglycemia is not only a response to primary insult, but also an aggravating factor for brain injuries. In addition, mitochondrial fusion and fission are vital to maintaining cellular functions. Current evidence also shows that the suppression of mitochondrial fission alleviates brain injuries after experimental SAH. Hence, this study aimed to determine the effects of mitochondrial dynamic modulation in hyperglycemia-related worse SAH neurological prognosis. Materials and methods: In vitro, we employed an enzyme-linked immunosorbent assay (ELISA) to detect the effect of mitochondrial division inhibitor-1 (Mdivi-1) on lipopolysaccharide (LPS)-induced BV-2 cells releasing inflammatory factors. In vivo, we produced hyperglycemic rats via intraperitoneal streptozotocin (STZ) injections. Hyperglycemia was confirmed using blood-glucose measurements (>300 mg/dL) 7 days after the STZ injection. The rodent model of SAH, in which fresh blood was instilled into the craniocervical junction, was used 7 days after STZ administration. We investigated the mechanism and effect of Mdivi-1, a selective inhibitor of dynamin-related protein (Drp1) to downregulate mitochondrial fission, on SAH-induced apoptosis in a hyperglycemic state, and evaluated the results in a dose−response manner. The rats were divided into the following five groups: (1) control, (2) SAH only, (3) Diabetes mellitus (DM) + SAH, (4) Mdivi-1 (0.24 mg/kg) + DM + SAH, and (5) Mdivi-1 (1.2 mg/kg) + DM + SAH. Results: In vitro, ELISA revealed that Mdivi-1 inhibited microglia from releasing inflammatory factors, such as tumor necrosis factor-α (TNF-α), interleukin (IL)-1ß, and IL-6. In vivo, neurological outcomes in the high-dose (1.2 mg/kg) Mdivi-1 treatment group were significantly reduced compared with the SAH and DM + SAH groups. Furthermore, immunofluorescence staining and ELISA revealed that a high dose of Mdivi-1 had attenuated inflammation and neuron cell apoptosis by inhibiting Hyperglycemia-aggravated activation, as well as microglia and astrocyte proliferation, following SAH. Conclusion: Mdivi-1, a Drp-1 inhibitor, attenuates cerebral vasospasm, poor neurological outcomes, inflammation, and neuron cell apoptosis following SAH + hyperglycemia.

A Transfer Learning Framework for Deep Learning-Based CT-to-Perfusion Mapping on Lung Cancer Patients.

Purpose: Deep learning model has shown the feasibility of providing spatial lung perfusion information based on CT images. However, the performance of this method on lung cancer patients is yet to be investigated. This study aims to develop a transfer learning framework to evaluate the deep learning based CT-to-perfusion mapping method specifically on lung cancer patients. Methods: SPECT/CT perfusion scans of 33 lung cancer patients and 137 non-cancer patients were retrospectively collected from two hospitals. To adapt the deep learning model on lung cancer patients, a transfer learning framework was developed to utilize the features learned from the non-cancer patients. These images were processed to extract features from three-dimensional CT images and synthesize the corresponding CT-based perfusion images. A pre-trained model was first developed using a dataset of patients with lung diseases other than lung cancer, and subsequently fine-tuned specifically on lung cancer patients under three-fold cross-validation. A multi-level evaluation was performed between the CT-based perfusion images and ground-truth SPECT perfusion images in aspects of voxel-wise correlation using Spearman's correlation coefficient (R), function-wise similarity using Dice Similarity Coefficient (DSC), and lobe-wise agreement using mean perfusion value for each lobe of the lungs. Results: The fine-tuned model yielded a high voxel-wise correlation (0.8142 ± 0.0669) and outperformed the pre-trained model by approximately 8%. Evaluation of function-wise similarity indicated an average DSC value of 0.8112 ± 0.0484 (range: 0.6460-0.8984) for high-functional lungs and 0.8137 ± 0.0414 (range: 0.6743-0.8902) for low-functional lungs. Among the 33 lung cancer patients, high DSC values of greater than 0.7 were achieved for high functional volumes in 32 patients and low functional volumes in all patients. The correlations of the mean perfusion value on the left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe were 0.7314, 0.7134, 0.5108, 0.4765, and 0.7618, respectively. Conclusion: For lung cancer patients, the CT-based perfusion images synthesized by the transfer learning framework indicated a strong voxel-wise correlation and function-wise similarity with the SPECT perfusion images. This suggests the great potential of the deep learning method in providing regional-based functional information for functional lung avoidance radiation therapy.