Predominance of hyperprogression in mucosal melanoma during anti-PD-1 monotherapy treatment.

BACKGROUND: A minority subset of immunotherapy patients manifests hyperprogressive disease (HPD), with the disparity in melanoma subtypes yet to be reported. This study aimed to delineate the proportion and prognosis of HPD in patients receiving anti-PD-1 monotherapy and to identify patient with HPD clinical characteristics across melanoma subtypes to inform clinical decision making. METHODS: Utilizing 4 established HPD definitions, the incidence of HPD in patients with advanced melanoma on anti-PD-1 monotherapy was determined. The incidence rates and prognostic abilities of various HPD definitions were compared to elect the most effective one. This facilitated a comparative analysis of subtypes and clinical features between patients with HPD and traditional progression. RESULTS: A total of 262 patients with advanced melanoma treated with anti-PD-1 monotherapy from 5 prospectively registered clinical trials were included in the study. The objective response rate (ORR) and disease control rate (DCR) was 21% and 58%, respectively, with 42% showcasing progression disease. The HPD incidences by 4 definitions were 13.2%, 16.8%, 10.8%, and 28.2%. All definitions effectively segregated HPD patients, with significantly poorer outcome than other progressive patients. The Delta TGR > 100 definition was the most indicative of a reduced overall survival, corroborated by the highest hazard ratio and statistical significance. The number of metastatic organs over 2 is a risk factor for HPD (OR = 4.18, P = .0103). Mucosal melanoma was the HPD prevalent subtype (OR = 3.13, P = .0489) in multivariable analysis, which is also indicated by RECIST criteria (P = .005). CONCLUSION: A delta TGR exceeding 100 best identified HPD patients in the advanced melanoma population treated with anti-PD-1 monotherapy. Hyperprogression was notably prevalent in mucosal melanoma patients with multiple metastatic organs. Caution against HPD is warranted when applying anti-PD-1 monotherapy in mucosal subtype.

Impact of different concentration iodinated contrast media on pain and comfort in abdominal computed tomography.

OBJECTIVES: To investigate whether high concentration iodinated contrast media (CM), compared with low concentration CM, could reduce pain and discomfort levels in patients who had level II and III venous conditions. METHODS: This prospective, single-center study enrolled patients who had level II and III venous conditions and underwent abdominal contrast-enhanced CT scan between July 2021 and February 2022. The venous condition to establish peripheral venous access for CM injection was graded using the Intravenous Access Scoring system, of which level II and III indicated poor venous condition and difficult venous access. Patients received iomeprol 400 in high concentration group and ioversol 320 in low group at an identical iodine delivery rate of 1.12 gI/s. The primary outcomes were pain and comfort levels. The secondary outcomes included adverse events and image quality. Patients rated pain intensity via Numerical Rating Scale and comfort level via Visual Analogue Scale with higher scores indicating higher levels of pain and discomfort. Quantitative and qualitative image assessment were compared between two groups. Continuous variables were compared using Student's t test or Mann-Whitney U test. Categorical variables were compared using χ2 test, χ2 test for trend or Fisher's exact test. RESULTS: A total of 206 patients (mean age, 60.13 ± 12.14 years; 81 males) were included with 99 in the high concentration group and 107 in the low concentration group. The high group had significantly lower pain scores (median 1 [IQR: 0-2] vs 2 (IQR 2-4), p < 0.001) and comfort scores (1 [IQR: 0-3] vs 3 [IQR: 2-5], p < 0.001) than the low group. Incidence of CM extravasation did not significantly differ (1.0 % vs 4.5 %, p = 0.214). No hypersensitivity reaction was observed. Qualitative assessment showed higher clarity scores of intrahepatic hepatic artery and portal vein in the high group. Quantitative assessment results were comparable between two groups. CONCLUSION: High concentration iodinated CM could lower pain intensity and improve comfort levels without comprising image quality of CT scan. High concentration CM is a preferable choice in patients with poor venous conditions during contrast-enhanced CT scan.

Pangenome and multi-tissue gene atlas provide new insights into the domestication and highland adaptation of yaks.

BACKGROUND: The genetic diversity of yak, a key domestic animal on the Qinghai-Tibetan Plateau (QTP), is a vital resource for domestication and breeding efforts. This study presents the first yak pangenome obtained through the de novo assembly of 16 yak genomes. RESULTS: We discovered 290 Mb of nonreference sequences and 504 new genes. Our pangenome-wide presence and absence variation (PAV) analysis revealed 5,120 PAV-related genes, highlighting a wide range of variety-specific genes and genes with varying frequencies across yak populations. Principal component analysis (PCA) based on binary gene PAV data classified yaks into three new groups: wild, domestic, and Jinchuan. Moreover, we proposed a 'two-haplotype genomic hybridization model' for understanding the hybridization patterns among breeds by integrating gene frequency, heterozygosity, and gene PAV data. A gene PAV-GWAS identified a novel gene (BosGru3G009179) that may be associated with the multirib trait in Jinchuan yaks. Furthermore, an integrated transcriptome and pangenome analysis highlighted the significant differences in the expression of core genes and the mutational burden of differentially expressed genes between yaks from high and low altitudes. Transcriptome analysis across multiple species revealed that yaks have the most unique differentially expressed mRNAs and lncRNAs (between high- and low-altitude regions), especially in the heart and lungs, when comparing high- and low-altitude adaptations. CONCLUSIONS: The yak pangenome offers a comprehensive resource and new insights for functional genomic studies, supporting future biological research and breeding strategies.

Classification of congenital cataracts based on multidimensional phenotypes and its association with visual outcomes.

AIM: To establish a classification for congenital cataracts that can facilitate individualized treatment and help identify individuals with a high likelihood of different visual outcomes. METHODS: Consecutive patients diagnosed with congenital cataracts and undergoing surgery between January 2005 and November 2021 were recruited. Data on visual outcomes and the phenotypic characteristics of ocular biometry and the anterior and posterior segments were extracted from the patients' medical records. A hierarchical cluster analysis was performed. The main outcome measure was the identification of distinct clusters of eyes with congenital cataracts. RESULTS: A total of 164 children (299 eyes) were divided into two clusters based on their ocular features. Cluster 1 (96 eyes) had a shorter axial length (mean±SD, 19.44±1.68 mm), a low prevalence of macular abnormalities (1.04%), and no retinal abnormalities or posterior cataracts. Cluster 2 (203 eyes) had a greater axial length (mean±SD, 20.42±2.10 mm) and a higher prevalence of macular abnormalities (8.37%), retinal abnormalities (98.52%), and posterior cataracts (4.93%). Compared with the eyes in Cluster 2 (57.14%), those in Cluster 1 (71.88%) had a 2.2 times higher chance of good best-corrected visual acuity [<0.7 logMAR; OR (95%CI), 2.20 (1.25-3.81); P=0.006]. CONCLUSION: This retrospective study categorizes congenital cataracts into two distinct clusters, each associated with a different likelihood of visual outcomes. This innovative classification may enable the personalization and prioritization of early interventions for patients who may gain the greatest benefit, thereby making strides toward precision medicine in the field of congenital cataracts.

Prevalence and risk evaluation of cardiovascular disease in the newly diagnosed prostate cancer population in China: A nationwide, multi-center, population-based cross-sectional study.

BACKGROUND: Cardiovascular disease (CVD) has emerged as the leading cause of death from prostate cancer (PCa) in recent decades, bringing a great disease burden worldwide. Men with preexisting CVD have an increased risk for major adverse cardiovascular events when treated with androgen deprivation therapy (ADT). The present study aimed to explore the prevalence and risk evaluation of CVD among people with newly diagnosed PCa in China. METHODS: Clinical data of newly diagnosed PCa patients were retrospectively collected from 34 centers in China from 2010 to 2022 through convenience sampling. CVD was defined as myocardial infarction, arrhythmia, heart failure, stroke, ischemic heart disease, and others. CVD risk was estimated by calculating Framingham risk scores (FRS). Patients were accordingly divided into low-, medium-, and high-risk groups. χ2 or Fisher's exact test was used for comparison of categorical variables. RESULTS: A total of 4253 patients were enrolled in the present study. A total of 27.0% (1147/4253) of patients had comorbid PCa and CVD, and 7.2% (307/4253) had two or more CVDs. The enrolled population was distributed in six regions of China, and approximately 71.0% (3019/4253) of patients lived in urban areas. With imaging and pathological evaluation, most PCa patients were diagnosed at an advanced stage, with 20.5% (871/4253) locally progressing and 20.5% (871/4253) showing metastasis. Most of them initiated prostatectomy (46.6%, 1983/4253) or regimens involving ADT therapy (45.7%, 1944/4253) for prostate cancer. In the present PCa cohort, 43.1% (1832/4253) of patients had hypertension, and half of them had poorly controlled blood pressure. With FRS stratification, as expected, a higher risk of CVD was related to aging and metabolic disturbance. However, we also found that patients with treatment involving ADT presented an originally higher risk of CVD than those without ADT. This was in accordance with clinical practice, i.e., aged patients or patients at advanced oncological stages were inclined to accept systematic integrative therapy instead of surgery. Among patients who underwent medical castration, only 4.0% (45/1118) received gonadotropin releasing hormone antagonists, in stark contrast to the grim situation of CVD prevalence and risk. CONCLUSIONS: PCa patients in China are diagnosed at an advanced stage. A heavy CVD burden was present at the initiation of treatment. Patients who accepted ADT-related therapy showed an original higher risk of CVD, but the awareness of cardiovascular protection was far from sufficient.

Post-secondary classroom teaching quality evaluation using small object detection model.

The classroom video has a complex background and dense targets. This study utilizes small object detection technology to analyze and evaluate students' behavior in the classroom, aiming to objectively and accurately assess classroom quality. Firstly, noise is removed from the images using a median filter, and the contrast of the images is enhanced through histogram equalization. Label smoothing is applied to reduce the model's sensitivity to labels. Then, features are extracted from the preprocessed images, and multi-scale feature fusion is employed to enhance semantic expression across multiple scales. Finally, a combination loss function is utilized to improve the accuracy of multi-object recognition tasks. Real-time detection of students' behaviors in the classroom is performed based on the small object detection model. The average head-up rate in the classroom is calculated, and the quality of teaching is evaluated and analyzed. This study explores the methods and applications of small object detection technology based on actual teaching cases and analyzes and evaluates its effectiveness in evaluating the quality of higher education classroom teaching. The research findings demonstrate the significant importance of small object detection technology in effectively evaluating students' learning conditions in higher education classrooms, leading to improved teaching quality and personalized education.

Resting state functional connectome in breast cancer patients with fear of cancer recurrence.

This study aimed to investigate network-level brain functional changes in breast cancer patients and their relationship with fear of cancer recurrence (FCR). Resting-state functional MRI was collected from 43 patients with breast cancer and 40 healthy controls (HCs). Graph theory analyses, whole-brain voxel-wise functional connectivity strength (FCS) analyses and seed-based functional connectivity (FC) analyses were performed to identify connection alterations in breast cancer patients. Correlations between brain functional connections (i.e. FCS and FC) and FCR level were assessed to further reveal the neural mechanisms of FCR in breast cancer patients. Graph theory analyses indicated a decreased clustering coefficient in breast cancer patients compared to HCs (P = 0.04). Patients with breast cancer exhibited significantly higher FCS in both higher-order function networks (frontoparietal, default mode, and dorsal attention systems) and primary somatomotor networks. Among the hyperconnected regions in breast cancer, the left inferior frontal operculum demonstrated a significant positive correlation with FCR. Our findings suggest that breast cancer patients exhibit less segregation of brain function, and the left inferior frontal operculum is a key region associated with FCR. This study offers insights into the neural mechanisms of FCR in breast cancer patients at the level of brain connectome.

A comparative study of mono-exponential and advanced diffusion-weighted imaging in differentiating stage IA endometrial carcinoma from benign endometrial lesions.

PURPOSE: The purpose of the current investigation is to compare the efficacy of different diffusion models and diffusion kurtosis imaging (DKI) in differentiating stage IA endometrial carcinoma (IAEC) from benign endometrial lesions (BELs). METHODS: Patients with IAEC, endometrial hyperplasia (EH), or a thickened endometrium confirmed between May 2016 and August 2022 were retrospectively enrolled. All of the patients underwent a preoperative pelvic magnetic resonance imaging (MRI) examination. The apparent diffusion coefficient (ADC) from the mono-exponential model, pure diffusion coefficient (D), pseudo-diffusion coefficient (D*), perfusion fraction (f) from the bi-exponential model, distributed diffusion coefficient (DDC), water molecular diffusion heterogeneity index from the stretched-exponential model, diffusion coefficient (Dk) and diffusion kurtosis (K) from the DKI model were calculated. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic efficiency. RESULTS: A total of 90 patients with IAEC and 91 patients with BELs were enrolled. The values of ADC, D, DDC and Dk were significantly lower and D* and K were significantly higher in cases of IAEC (p < 0.05). Multivariate analysis showed that K was the only predictor. The area under the ROC curve of K was 0.864, significantly higher compared with the ADC (0.601), D (0.811), D* (0.638), DDC (0.743) and Dk (0.675). The sensitivity, specificity and accuracy of K were 78.89%, 85.71% and 80.66%, respectively. CONCLUSION: Advanced diffusion-weighted imaging models have good performance for differentiating IAEC from EH and endometrial thickening. Among all of the diffusion parameters, K showed the best performance and was the only independent predictor. Diffusion kurtosis imaging was defined as the most valuable model in the current context.

Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework.

PURPOSE: Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor measurement, radiomics analysis, treatment planning, and operative strategy. In this study, we developed and evaluated segmentation potential exclusively on post-chemoradiation T2-weighted MRI using convolutional neural networks, with the aim of reducing the detection workload for radiologists and clinicians. METHODS: A total of 372 consecutive patients with LARC were retrospectively enrolled from October 2015 to December 2017. The standard-of-care neoadjuvant process included 22-fraction intensity-modulated radiation therapy and oral capecitabine. Further, 243 patients (3061 slices) were grouped into training and validation datasets with a random 80:20 split, and 41 patients (408 slices) were used as the test dataset. A symmetric eight-layer deep network was developed using the nnU-Net Framework, which outputs the segmentation result with the same size. The trained deep learning (DL) network was examined using fivefold cross-validation and tumor lesions with different TRGs. RESULTS: At the stage of testing, the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were applied to quantitatively evaluate the performance of generalization. Considering the test dataset (41 patients, 408 slices), the average DSC, HD95, and MSD were 0.700 (95% CI: 0.680-0.720), 17.73 mm (95% CI: 16.08-19.39), and 3.11 mm (95% CI: 2.67-3.56), respectively. Eighty-two percent of the MSD values were less than 5 mm, and fifty-five percent were less than 2 mm (median 1.62 mm, minimum 0.07 mm). CONCLUSIONS: The experimental results indicated that the constructed pipeline could achieve relatively high accuracy. Future work will focus on assessing the performances with multicentre external validation.

Extranodal Extension at Pretreatment MRI and the Prognostic Value for Patients with Rectal Cancer.

Background Detection of extranodal extension (ENE) at pathology is a poor prognostic indicator for rectal cancer, but whether ENE can be identified at pretreatment MRI is, to the knowledge of the authors, unknown. Purpose To evaluate the performance of pretreatment MRI in detecting ENE using a matched pathologic reference standard and to assess its prognostic value in patients with rectal cancer. Materials and Methods This single-center study included a prospective development data set consisting of participants with rectal adenocarcinoma who underwent pretreatment MRI and radical surgery (December 2021 to January 2023). MRI characteristics were identified by their association with ENE-positive nodes (χ2 test and multivariable logistic regression) and the performance of these MRI features was assessed (area under the receiver operating characteristic curve [AUC]). Interobserver agreement was assessed by Cohen κ coefficient. The prognostic value of ENE detected with MRI for predicting 3-year disease-free survival was assessed by Cox regression analysis in a retrospective independent validation cohort of patients with locally advanced rectal cancer (December 2019 to July 2020). Results The development data set included 147 participants (mean age, 62 years ± 11 [SD]; 87 male participants). The retrospective cohort included 110 patients (mean age, 60 years ± 9; 79 male participants). Presence of vessel interruption and fusion (both P < .001), heterogeneous internal structure, and the broken-ring and tail signs (odds ratio range, 4.10-23.20; P value range, <.001 to .002) were predictors of ENE at MRI, and together achieved an AUC of 0.91 (95% CI: 0.88, 0.93) in detecting ENE. Interobserver agreement was moderate for the presence of vessel interruption and fusion (κ = 0.46 for both) and substantial for others (κ = 0.61-0.67). The presence of ENE at pretreatment MRI was independently associated with worse 3-year disease-free survival (hazard ratio, 3.00; P = .02). Conclusion ENE can be detected at pretreatment MRI, and its presence was associated with worse prognosis for patients with rectal cancer. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Eberhardt in this issue.

Is tumor necrosis factor-α monoclonal therapy with proactive therapeutic drug monitoring optimized for inflammatory bowel disease? Network meta-analysis.

BACKGROUND: The efficacy and safety of anti-tumor necrosis factor-α (TNF-α) monoclonal antibody therapy [adalimumab (ADA) and infliximab (IFX)] with therapeutic drug monitoring (TDM), which has been proposed for inflammatory bowel disease (IBD) patients, are still controversial. AIM: To determine the efficacy and safety of anti-TNF-α monoclonal antibody therapy with proactive TDM in patients with IBD and to determine which subtype of IBD patients is most suitable for proactive TDM interventions. METHODS: As of July 2023, we searched for randomized controlled trials (RCTs) and observational studies in PubMed, Embase, and the Cochrane Library to compare anti-TNF-α monoclonal antibody therapy with proactive TDM with therapy with reactive TDM or empiric therapy. Pairwise and network meta-analyses were used to determine the IBD patient subtype that achieved clinical remission and to determine the need for surgery. RESULTS: This systematic review and meta-analysis yielded 13 studies after exclusion, and the baseline indicators were balanced. We found a significant increase in the number of patients who achieved clinical remission in the ADA [odds ratio (OR) = 1.416, 95% confidence interval (CI): 1.196-1.676] and RCT (OR = 1.393, 95%CI: 1.182-1.641) subgroups and a significant decrease in the number of patients who needed surgery in the proactive vs reactive (OR = 0.237, 95%CI: 0.101-0.558) and IFX + ADA (OR = 0.137, 95%CI: 0.032-0.588) subgroups, and the overall risk of adverse events was reduced (OR = 0.579, 95%CI: 0.391-0.858) according to the pairwise meta-analysis. Moreover, the network meta-analysis results suggested that patients with IBD treated with ADA (OR = 1.39, 95%CI: 1.19-1.63) were more likely to undergo TDM, especially in comparison with patients with reactive TDM (OR = 1.38, 95%CI: 1.07-1.77). CONCLUSION: Proactive TDM is more suitable for IBD patients treated with ADA and has obvious advantages over reactive TDM. We recommend proactive TDM in IBD patients who are treated with ADA.

Early Contrast-Enhanced MR for Diagnosing Complete Tumor Response of Locally Advanced Esophageal Squamous Cell Carcinoma After Neoadjuvant Therapy: A Retrospective Comparative Study.

BACKGROUND: This study assessed the performance of early contrast-enhanced magnetic resonance (ECE-MR) in the detecting of complete tumor response (ypT0) in patients with esophageal squamous cell carcinoma following neoadjuvant therapy. PATIENTS AND METHODS: Preoperative MR images of consecutive patients who underwent neoadjuvant therapy and surgical resection were reviewed retrospectively. The accuracy of ECE-MR and T2WI+DWI was evaluated by comparing the findings with pathological results. Receiver operating characteristic curve analysis was used to assess the diagnostic performance, and DeLong method was applied to compare the areas under the curves (AUC). Chi-squared analysis was conducted to explore the difference in pathological changes. RESULTS: A total of 198 patients (mean age 62.6 ± 7.8 years, 166 men) with 201 lesions were included. The AUC of ECE-MR was 0.85 (95% CI 0.79-0.90) for diagnosing ypT1-4, which was significantly higher than that of T2WI+DWI (AUC 0.69, 95% CI 0.63-0.76, p < 0.001). The diagnostic performance of both T2WI+DWI and ECE-MR improved with increasing tumor stage. The AUCs of ECE-MRI were higher in ypT1 and ypT2 tumors than T2WI+DWI. Degree 2-3 tumor-infiltrating lymphocytes and neutrophils were commonly seen in ypT0 tumors misdiagnosed by ECE-MR. CONCLUSIONS: Visual evaluation of ECE-MR is a promising diagnostic protocol for the detection of complete tumor response, especially for differentiation with early stage tumors. The accurate diagnosis of complete tumor response after neoadjuvant therapy using imaging modalities is of important significance for clinical decision-making for patients with esophageal squamous cell carcinoma. It is hoped that early contrast-enhanced MR will provide supportive advice for the development of individualized treatment options for patients.

Peri-lesion regions in differentiating suspicious breast calcification-only lesions specifically on contrast enhanced mammography.

PURPOSE: The explore the added value of peri-calcification regions on contrast-enhanced mammography (CEM) in the differential diagnosis of breast lesions presenting as only calcification on routine mammogram. METHODS: Patients who underwent CEM because of suspicious calcification-only lesions were included. The test set included patients between March 2017 and March 2019, while the validation set was collected between April 2019 and October 2019. The calcifications were automatically detected and grouped by a machine learning-based computer-aided system. In addition to extracting radiomic features on both low-energy (LE) and recombined (RC) images from the calcification areas, the peri-calcification regions, which is generated by extending the annotation margin radially with gradients from 1 mm to 9 mm, were attempted. Machine learning (ML) models were built to classify calcifications into malignant and benign groups. The diagnostic matrices were also evaluated by combing ML models with subjective reading. RESULTS: Models for LE (significant features: wavelet-LLL_glcm_Imc2_MLO; wavelet-HLL_firstorder_Entropy_MLO; wavelet-LHH_glcm_DifferenceVariance_CC; wavelet-HLL_glcm_SumEntropy_MLO;wavelet-HLH_glrlm_ShortRunLowGray LevelEmphasis_MLO; original_firstorder_Entropy_MLO; original_shape_Elongation_MLO) and RC (significant features: wavelet-HLH_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_10Percentile_CC; original_firstorder_Maximum_MLO; wavelet-HHH_glcm_Autocorrelation_MLO; original_shape_Elongation_MLO; wavelet-LHL_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_RootMeanSquared_MLO) images were set up with 7 features. Areas under the curve (AUCs) of RC models are significantly better than those of LE models with compact and expanded boundary (RC v.s. LE, compact: 0.81 v.s. 0.73, p < 0.05; expanded: 0.89 v.s. 0.81, p < 0.05) and RC models with 3 mm boundary extension yielded the best performance compared to those with other sizes (AUC = 0.89). Combining with radiologists' reading, the 3mm-boundary RC model achieved a sensitivity of 0.871 and negative predictive value of 0.937 with similar accuracy of 0.843 in predicting malignancy. CONCLUSIONS: The machine learning model integrating intra- and peri-calcification regions on CEM has the potential to aid radiologists' performance in predicting malignancy of suspicious breast calcifications.

Total Neoadjuvant Therapy With PD-1 Blockade for High-Risk Proficient Mismatch Repair Rectal Cancer.

Importance: Total neoadjuvant therapy (TNT) is the standard treatment for locally advanced rectal cancer, especially for patients with high-risk factors. However, the efficacy of TNT combined with immunotherapy for patients with proficient mismatch repair (pMMR) rectal cancer is unknown. Objectives: To evaluate the safety and efficacy of TNT with induction chemoimmunotherapy followed by long-course chemoradiation in patients with high-risk, pMMR rectal cancer and to identify potential molecular biomarkers associated with treatment efficacy. Design, Setting, and Participants: This cohort study was a single-arm phase 2 trial conducted at Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, from June 2020 to October 2021. Biopsies and plasma were collected before treatment for whole-exome sequencing and cell-free DNA sequencing, respectively. Data were analyzed from May 2022 to September 2022. Interventions: Participants received 3 cycles of induction oxaliplatin and capecitabine combined with camrelizumab and radiotherapy (50.6 Gy in 22 fractions) with concurrent capecitabine. Patients without disease progression received 2 cycles of consolidation oxaliplatin/capecitabine. Main Outcomes and Measures: The primary end point was pathologic complete response rate. Results: Of 25 patients enrolled (19 men [76%]; 6 women [24%]; median [IQR] age, 58 [48-64] years), 22 patients (88%) completed the TNT schedule. The pathologic complete response rate was 33.3% (7/21). Twelve patients (48%) achieved clinical complete response, and 4 patients (16%) chose to watch and wait. R0 resection was achieved in 21 of 21 patients, and the major pathologic response rate was 38.1% (8/21). The most common adverse event was nausea (80%, 20/25); grade 3 toxic effects occurred in 9 of 25 patients (36%). Patients with tumor shrinkage of 50% or greater after induction oxaliplatin/capecitabine and camrelizumab or clinical complete response had higher percentages of LRP1B mutation. Mutation of LRP1B was associated with high tumor mutation burden and tumor neoantigen burden. Patients with high tumor mutation burden all benefited from therapy. Conclusions and Relevance: This study found that TNT with induction chemoimmunotherapy followed by long-course chemoradiation was safe and effective for patients with high-risk rectal cancer with pMMR status. Longer follow-up and larger clinical studies are needed to validate this innovative regimen. There is also an urgent need to further validate the predictive value of LRP1B and discover other novel biomarkers with potential predictive value for rectal cancer.

UFPS: A unified framework for partially annotated federated segmentation in heterogeneous data distribution.

Partially supervised segmentation is a label-saving method based on datasets with fractional classes labeled and intersectant. Its practical application in real-world medical scenarios is, however, hindered by privacy concerns and data heterogeneity. To address these issues without compromising privacy, federated partially supervised segmentation (FPSS) is formulated in this work. The primary challenges for FPSS are class heterogeneity and client drift. We propose a unified federated partially labeled segmentation (UFPS) framework to segment pixels within all classes for partially annotated datasets by training a comprehensive global model that avoids class collision. Our framework includes unified label learning (ULL) and sparse unified sharpness aware minimization (sUSAM) for class and feature space unification, respectively. Through empirical studies, we find that traditional methods in partially supervised segmentation and federated learning often struggle with class collision when combined. Our extensive experiments on real medical datasets demonstrate better deconflicting and generalization capabilities of UFPS.

Value of multiple models of diffusion-weighted imaging to predict hepatic lymph node metastases in colorectal liver metastases patients.

BACKGROUND: About 10%-31% of colorectal liver metastases (CRLM) patients would concomitantly show hepatic lymph node metastases (LNM), which was considered as sign of poor biological behavior and a relative contraindication for liver resection. Up to now, there's still lack of reliable preoperative methods to assess the status of hepatic lymph nodes in patients with CRLM, except for pathology examination of lymph node after resection. AIM: To compare the ability of mono-exponential, bi-exponential, and stretched-exponential diffusion-weighted imaging (DWI) models in distinguishing between benign and malignant hepatic lymph nodes in patients with CRLM who received neoadjuvant chemotherapy prior to surgery. METHODS: In this retrospective study, 97 CRLM patients with pathologically confirmed hepatic lymph node status underwent magnetic resonance imaging, including DWI with ten b values before and after chemotherapy. Various parameters, such as the apparent diffusion coefficient from the mono-exponential model, and the true diffusion coefficient, the pseudo-diffusion coefficient, and the perfusion fraction derived from the intravoxel incoherent motion model, along with distributed diffusion coefficient (DDC) and α from the stretched-exponential model (SEM), were measured. The parameters before and after chemotherapy were compared between positive and negative hepatic lymph node groups. A nomogram was constructed to predict the hepatic lymph node status. The reliability and agreement of the measurements were assessed using the coefficient of variation and intraclass correlation coefficient. RESULTS: Multivariate analysis revealed that the pre-treatment DDC value and the short diameter of the largest lymph node after treatment were independent predictors of metastatic hepatic lymph nodes. A nomogram combining these two factors demonstrated excellent performance in distinguishing between benign and malignant lymph nodes in CRLM patients, with an area under the curve of 0.873. Furthermore, parameters from SEM showed substantial repeatability. CONCLUSION: The developed nomogram, incorporating the pre-treatment DDC and the short axis of the largest lymph node, can be used to predict the presence of hepatic LNM in CRLM patients undergoing chemotherapy before surgery. This nomogram was proven to be more valuable, exhibiting superior diagnostic performance compared to quantitative parameters derived from multiple b values of DWI. The nomogram can serve as a preoperative assessment tool for determining the status of hepatic lymph nodes and aiding in the decision-making process for surgical treatment in CRLM patients.

The differential diagnosis of lung precursor glandular lesions, micro-invasive adenocarcinoma, and invasive adenocarcinoma using low dose spectral computed tomography perfusion imaging.

Background: Few studies about the association between computed tomography (CT) perfusion imaging parameters and invasiveness in lung adenocarcinoma (LUAD) have been conducted using low dose spectral CT perfusion imaging. The purpose of this study was to investigate application of spectral revolution CT low-dose perfusion imaging in the differential diagnosis of different pathological subtypes of LUAD. Methods: This was a cross-sectional study based on historical data from January 2018 to May 2019 in Peking University Cancer Hospital & Institute. A total of 62 cases were enrolled, including 2 cases of atypical adenomatous hyperplasia (AAH), 3 cases of adenocarcinoma in situ (AIS), 4 cases of minimally invasive adenocarcinoma (MIA), and 53 cases of invasive adenocarcinoma (IAC), all confirmed with pathology. The inclusion and exclusion criteria were regulated. Using Revolution low-dose CT perfusion imaging (GE, USA), the CT perfusion parameters of hemodynamics were obtained: blood flow (BF), blood volume (BV), impulse residue function time of arrival (IRF TO), maximum slope of increase (MSI), mean transit time (MTT), permeability surface area product (PS), positive enhancement integral (PEI), and maximum enhancement time (Tmax). Univariate analysis of variance (ANOVA) or Kruskal-Wallis test was used to compare the differences of CT perfusion quantitative parameters among AAH, AIS, MIA, and IAC. Mann-Whitney test was used to compare the difference of CT perfusion imaging parameters between preinvasive lesions (AAH and AIS) and invasive lung cancer (MIA and IAC). Results: Statistically significant differences in IRF TO were observed in LUAD with different invasiveness, namely, among AIS, MIA, and IAC groups (0.56±0.74 vs. 0.54±1.08 vs. 4.39±2.19, P=0.004). Statistically significant differences in IRF TO were also observed between pre-invasive lesions group (AAH and AIS) and invasive lung cancer group (MIA and IAC) (1.12±1.27 vs. 3.75±2.79, P=0.031), and between AAH + AIS + MIA groups and IAC group (0.83±1.13 vs. 4.12±2.69, P<0.001). There were no statistically significant differences in other CT perfusion parameters of hemodynamics among different pathological subtypes of LUAD (P>0.05). Conclusions: The low-dose perfusion parameter IRF TO of revolution CT has the potential to be employed in the differential diagnosis of different pathological subtypes of LUAD.

CT radiomics in the identification of preoperative understaging in patients with clinical stage T1-2N0 esophageal squamous cell carcinoma.

Background: Predicting preoperative understaging in patients with clinical stage T1-2N0 (cT1-2N0) esophageal squamous cell carcinoma (ESCC) is critical to customizing patient treatment. Radiomics analysis can provide additional information that reflects potential biological heterogeneity based on computed tomography (CT) images. However, to the best of our knowledge, no studies have focused on identifying CT radiomics features to predict preoperative understaging in patients with cT1-2N0 ESCC. Thus, we sought to develop a CT-based radiomics model to predict preoperative understaging in patients with cT1-2N0 esophageal cancer, and to explore the value of the model in disease-free survival (DFS) prediction. Methods: A total of 196 patients who underwent radical surgery for cT1-2N0 ESCC were retrospectively recruited from two hospitals. Among the 196 patients, 134 from Peking University Cancer Hospital were included in the training cohort, and 62 from Henan Cancer Hospital were included in the external validation cohort. Radiomics features were extracted from patients' CT images. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and model construction. A clinical model was also built based on clinical characteristics, and the tumor size [the length, thickness and the thickness-to-length ratio (TLR)] was evaluated on the CT images. A radiomics nomogram was established based on multivariate logistic regression. The diagnostic performance of the models in predicting preoperative understaging was assessed by the area under the receiver operating characteristic curve (AUC). Kaplan-Meier curves with the log-rank test were employed to analyze the correlation between the nomogram and DFS. Results: Of the patients, 50.0% (67/134) and 51.6% (32/62) were understaged in the training and validation groups, respectively. The radiomics scores and the TLRs of the tumors were included in the nomogram. The AUCs of the nomogram for predicting preoperative understaging were 0.874 [95% confidence interval (CI): 0.815-0.933] in the training cohort and 0.812 (95% CI: 0.703-0.912) in the external validation cohort. The diagnostic performance of the nomogram was superior to that of the clinical model (P<0.05). The nomogram was an independent predictor of DFS in patients with cT1-2N0 ESCC. Conclusions: The proposed CT-based radiomics model could be used to predict preoperative understaging in patients with cT1-2N0 ESCC who have undergone radical surgery.

The ELIXIR Biodiversity Community: Understanding short- and long-term changes in biodiversity.

Biodiversity loss is now recognised as one of the major challenges for humankind to address over the next few decades. Unless major actions are taken, the sixth mass extinction will lead to catastrophic effects on the Earth's biosphere and human health and well-being. ELIXIR can help address the technical challenges of biodiversity science, through leveraging its suite of services and expertise to enable data management and analysis activities that enhance our understanding of life on Earth and facilitate biodiversity preservation and restoration. This white paper, prepared by the ELIXIR Biodiversity Community, summarises the current status and responses, and presents a set of plans, both technical and community-oriented, that should both enhance how ELIXIR Services are applied in the biodiversity field and how ELIXIR builds connections across the many other infrastructures active in this area. We discuss the areas of highest priority, how they can be implemented in cooperation with the ELIXIR Platforms, and their connections to existing ELIXIR Communities and international consortia. The article provides a preliminary blueprint for a Biodiversity Community in ELIXIR and is an appeal to identify and involve new stakeholders.

Prediction of the lymphatic, microvascular, and perineural invasion of pancreatic neuroendocrine tumors using preoperative magnetic resonance imaging.

BACKGROUND: Significant correlation between lymphatic, microvascular, and perineural invasion (LMPI) and the prognosis of pancreatic neuroendocrine tumors (PENTs) was confirmed by previous studies. There was no previous study reported the relationship between magnetic resonance imaging (MRI) parameters and LMPI. AIM: To determine the feasibility of using preoperative MRI of the pancreas to predict LMPI in patients with non-functioning PENTs (NFPNETs). METHODS: A total of 61 patients with NFPNETs who underwent MRI scans and lymphadenectomy from May 2011 to June 2018 were included in this retrospective study. The patients were divided into group 1 (n = 34, LMPI negative) and group 2 (n = 27, LMPI positive). The clinical characteristics and qualitative MRI features were collected. In order to predict LMPI status in NF-PNETs, a multivariate logistic regression model was constructed. Diagnostic performance was evaluated by calculating the receiver operator characteristic (ROC) curve with area under ROC, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy. RESULTS: There were significant differences in the lymph node metastasis stage, tumor grade, neuron-specific enolase levels, tumor margin, main pancreatic ductal dilatation, common bile duct dilatation, enhancement pattern, vascular and adjacent tissue involvement, synchronous liver metastases, the long axis of the largest lymph node, the short axis of the largest lymph node, number of the lymph nodes with short axis > 5 or 10 mm, and tumor volume between two groups (P < 0.05). Multivariate analysis showed that tumor margin (odds ratio = 11.523, P < 0.001) was a predictive factor for LMPI of NF-PNETs. The area under the receiver value for the predictive performance of combined predictive factors was 0.855. The sensitivity, specificity, PPV, NPV and accuracy of the model were 48.1% (14/27), 97.1% (33/34), 97.1% (13/14), 70.2% (33/47) and 0.754, respectively. CONCLUSION: Using preoperative MRI, ill-defined tumor margins can effectively predict LMPI in patients with NF-PNETs.