Prediction of clinically significant prostate cancer using radiomics models in real-world clinical practice: a retrospective multicenter study.

PURPOSE: To develop and evaluate machine learning models based on MRI to predict clinically significant prostate cancer (csPCa) and International Society of Urological Pathology (ISUP) grade group as well as explore the potential value of radiomics models for improving the performance of radiologists for Prostate Imaging Reporting and Data System (PI-RADS) assessment. MATERIAL AND METHODS: A total of 1616 patients from 4 tertiary care medical centers were retrospectively enrolled. PI-RADS assessments were performed by junior, senior, and expert-level radiologists. The radiomics models for predicting csPCa were built using 4 machine-learning algorithms. The PI-RADS were adjusted by the radiomics model. The relationship between the Rad-score and ISUP was evaluated by Spearman analysis. RESULTS: The radiomics models made using the random forest algorithm yielded areas under the receiver operating characteristic curves (AUCs) of 0.874, 0.876, and 0.893 in an internal testing cohort and external testing cohorts, respectively. The AUC of the adjusted_PI-RADS was improved, and the specificity was improved at a slight sacrifice of sensitivity. The participant-level correlation showed that the Rad-score was positively correlated with ISUP in all testing cohorts (r > 0.600 and p < 0.0001). CONCLUSIONS: This radiomics model resulted as a powerful, non-invasive auxiliary tool for accurately predicting prostate cancer aggressiveness. The radiomics model could reduce unnecessary biopsies and help improve the diagnostic performance of radiologists' PI-RADS. Yet, prospective studies are still needed to validate the radiomics models further. CRITICAL RELEVANCE STATEMENT: The radiomics model with MRI may help to accurately screen out clinically significant prostate cancer, thereby assisting physicians in making individualized treatment plans. KEY POINTS: • The diagnostic performance of the radiomics model using the Random Forest algorithm is comparable to the Prostate Imaging Reporting and Data System (PI-RADS) obtained by radiologists. • The performance of the adjusted Prostate Imaging Reporting and Data System (PI-RADS) was improved, which implied that the radiomics model could be a potential radiological assessment tool. • The radiomics model lowered the percentage of equivocal cases. Moreover, the Rad-scores can be used to characterize prostate cancer aggressiveness.

Identification of End-Binding 1 Protein as Novel α-4 Giardin-Binding Partners in Giardia lamblia Trophozoites.

BACKGROUND: Giardia lamblia (syn. G. intestinalis, G. duodenalis) is a primitive opportunistic protozoon, and one of the earliest differentiated eukaryotes. Despite its primitive nature, G. lamblia has a sophisticated cytoskeleton system, which is closely related to its proliferation and pathogenicity. Meanwhile, α giardin is a G. lamblia-specific cytoskeleton protein, which belongs to the annexin superfamily. Interestingly, G. lamblia has 21 annexin-like α giardins, i.e., more than higher eukaryotes. The functional differences among α giardin members are not fully understood. METHODS: We took α-4 giardin, a member of α giardin family, as a research object. A morpholino-mediated knockdown experiment was performed to identify the effect of α-4 giardin on G. lamblia trophozoites biological traits. A yeast two-hybrid cDNA library of G. lamblia strain C2 trophozoites was screened for interaction partners of α-4 giardin. Co-immunoprecipitation and fluorescent colocalization confirmed the relationship between G. lamblia EB1 (gEB1) and α-4 giardin. RESULTS: α-4 Giardin could inhibit the proliferation and adhesion of G. lamblia trophozoites. In addition, it interacted with G. lamblia EB1 (gEB1). CONCLUSIONS: α-4 Giardin was involved in proliferation and adhesion in G. lamblia trophozoites, and EB1, a crucial roles in mitosis, was an interacting partner of α-4 giardin.

Computed Diffusion-Weighted Images of Rectal Cancer: Image Quality, Restaging, and Treatment Response after Neoadjuvant Therapy.

BACKGROUND: Computed diffusion-weighted images (cDWI) of random b value could be derived from acquired DWI (aDWI) with at least two different b values. However, its comparison between aDWI and cDWI images in locally advanced rectal cancer (LARC) patients after neoadjuvant therapy (NT) is needed. PURPOSE: To compare the cDWI and aDWI in image quality, restaging, and treatment response of LARC after NT. STUDY TYPE: Retrospective. POPULATION: Eighty-seven consecutive patients. FIELD STRENGTH/SEQUENCE: 3.0 T/DWI. ASSESSMENT: All patients underwent two DWI sequences, including conventional acquisition with b = 0 and 1000 s/mm2 (aDWIb1000 ) and another with b = 0 and 700 s/mm2 on a 3.0-T MR scanner. The images of the latter were used to compute the diffusion images with b = 1000 s/mm2 (cDWIb1000 ). Four radiologists with 3, 4, 14, and 25 years of experience evaluated the images to compare the image quality, TN restaging performance, and treatment response between aDWIb1000 and cDWIb1000 . STATISTICAL TESTS: Interclass correlation coefficients, weighted κ coefficient, paired Wilcoxon, and McNemar or Fisher test were used. A significance level of 0.05 was used. RESULTS: The cDWIb1000 images were superior to the aDWIb1000 ones in both subjective and objective image quality. In T restaging, the overall diagnostic accuracy of cDWIb1000 images was higher than that of aDWIb1000 images (57.47% vs. 49.43%, P = 0.289 for the inexperienced radiologist; 77.01% vs. 63.22%, significant for the experienced radiologist), with better sensitivity in determining ypT0-Tis tumors. Additionally, it increased the sensitivity in detecting ypT2 tumors for the inexperienced radiologist and ypT3 tumors for the experienced radiologist. N restaging and treatment response were found to be similar between two sequences for both radiologists. DATA CONCLUSION: Compared to aDWIb1000 images, the computed ones might serve as a wise approach, providing comparable or better image quality, restaging performance, and treatment response assessment for LARC after NT. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 2.

Sintilimab-induced inflammatory myopathy in a patient with esophageal cancer: a case report.

The use of immune checkpoint inhibitors (ICIs) has shown remarkable efficacy in the treatment of various malignancies, significantly reshaping cancer treatment. However, as a result of the widespread use of ICIs, several immune-related adverse events (iRAEs) have emerged, some of which can be rare and potentially fatal. In this paper, we reported the earliest case of Sintilimab used in the treatment of esophageal cancer with severe inflammatory myopathy (involving the cardiac, respiratory, and skeletal muscles)in China. This patient was an elderly female who presented to our institution with progressive limb weakness and ptosis. Prior to the onset of symptoms, the patient had undergone a radical esophagectomy for esophageal cancer, experienced several cycles of of radiotherapy and chemotherapy, as well as two doses of Sintilimab treatment. Shortly after initiating immunotherapy, the patient developed symptoms including bilateral ptosis, limb weakness, and difficulty swallowing and breathing. The levels of creatine kinase and troponin I in the patient's blood were significantly elevated, and positive results were observed for anti-skeletal and anti-cardiac muscle antibodies, indicating that the patient might be developing ICIs-related inflammatory myopathy. Fortunately, the patient responded well to treatment including corticosteroids, plasmapheresis, intravenous immunoglobulin, and other supportive therapies. Here, we discuss the incidence, mechanisms, and management strategies of fatal iRAEs. Early detection and timely intervention may be critical in reducing the incidence and mortality rates of iRAEs and improving patient outcomes.

Detecting Adverse Pathology of Prostate Cancer With a Deep Learning Approach Based on a 3D Swin-Transformer Model and Biparametric MRI: A Multicenter Retrospective Study.

BACKGROUND: Accurately detecting adverse pathology (AP) presence in prostate cancer patients is important for personalized clinical decision-making. Radiologists' assessment based on clinical characteristics showed poor performance for detecting AP presence. PURPOSE: To develop deep learning models for detecting AP presence, and to compare the performance of these models with those of a clinical model (CM) and radiologists' interpretation (RI). STUDY TYPE: Retrospective. POPULATION: Totally, 616 men from six institutions who underwent radical prostatectomy, were divided into a training cohort (508 patients from five institutions) and an external validation cohort (108 patients from one institution). FIELD STRENGTH/SEQUENCES: T2-weighted imaging with a turbo spin echo sequence and diffusion-weighted imaging with a single-shot echo plane-imaging sequence at 3.0 T. ASSESSMENT: The reference standard for AP was histopathological extracapsular extension, seminal vesicle invasion, or positive surgical margins. A deep learning model based on the Swin-Transformer network (TransNet) was developed for detecting AP. An integrated model was also developed, which combined TransNet signature with clinical characteristics (TransCL). The clinical characteristics included biopsy Gleason grade group, Prostate Imaging Reporting and Data System scores, prostate-specific antigen, ADC value, and the lesion maximum cross-sectional diameter. STATISTICAL TESTS: Model and radiologists' performance were assessed using area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The Delong test was used to evaluate difference in AUC. P < 0.05 was considered significant. RESULTS: The AUC of TransCL for detecting AP presence was 0.813 (95% CI, 0.726-0.882), which was higher than that of TransNet (0.791 [95% CI, 0.702-0.863], P = 0.429), and significantly higher than those of CM (0.749 [95% CI, 0.656-0.827]) and RI (0.664 [95% CI, 0.566-0.752]). DATA CONCLUSION: TransNet and TransCL have potential to aid in detecting the presence of AP and some single adverse pathologic features. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY: Stage 4.

Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT.

This study is aimed to evaluate effects of deep learning image reconstruction (DLIR) on image quality in single-energy CT (SECT) and dual-energy CT (DECT), in reference to adaptive statistical iterative reconstruction-V (ASIR-V). The Gammex 464 phantom was scanned in SECT and DECT modes at three dose levels (5, 10, and 20 mGy). Raw data were reconstructed using six algorithms: filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) strength, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H), to generate SECT 120kVp images and DECT 120kVp-like images. Objective image quality metrics were computed, including noise power spectrum (NPS), task transfer function (TTF), and detectability index (d'). Subjective image quality evaluation, including image noise, texture, sharpness, overall quality, and low- and high-contrast detectability, was performed by six readers. DLIR-H reduced overall noise magnitudes from FBP by 55.2% in a more balanced way of low and high frequency ranges comparing to AV-40, and improved the TTF values at 50% for acrylic inserts by average percentages of 18.32%. Comparing to SECT 20 mGy AV-40 images, the DECT 10 mGy DLIR-H images showed 20.90% and 7.75% improvement in d' for the small-object high-contrast and large-object low-contrast tasks, respectively. Subjective evaluation showed higher image quality and better detectability. At 50% of the radiation dose level, DECT with DLIR-H yields a gain in objective detectability index compared to full-dose AV-40 SECT images used in daily practice.

Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers.

OBJECTIVES: To evaluate image quality, diagnostic acceptability, and lesion conspicuity in abdominal dual-energy CT (DECT) using deep learning image reconstruction (DLIR) compared to those using adaptive statistical iterative reconstruction-V (Asir-V) at 50% blending (AV-50), and to identify potential factors impacting lesion conspicuity. METHODS: The portal-venous phase scans in abdominal DECT of 47 participants with 84 lesions were prospectively included. The raw data were reconstructed to virtual monoenergetic image (VMI) at 50 keV using filtered back-projection (FBP), AV-50, and DLIR at low (DLIR-L), medium (DLIR-M), and high strength (DLIR-H). A noise power spectrum (NPS) was generated. CT number and standard deviation values of eight anatomical sites were measured. Signal-to-noise (SNR), and contrast-to-noise ratio (CNR) values were calculated. Five radiologists assessed image quality in terms of image contrast, image noise, image sharpness, artificial sensation, and diagnostic acceptability, and evaluated the lesion conspicuity. RESULTS: DLIR further reduced image noise (p < 0.001) compared to AV-50 while better preserved the average NPS frequency (p < 0.001). DLIR maintained CT number values (p > 0.99) and improved SNR and CNR values compared to AV-50 (p < 0.001). DLIR-H and DLIR-M showed higher ratings in all image quality analyses than AV-50 (p < 0.001). DLIR-H provided significantly better lesion conspicuity than AV-50 and DLIR-M regardless of lesion size, relative CT attenuation to surrounding tissue, or clinical purpose (p < 0.05). CONCLUSIONS: DLIR-H could be safely recommended for routine low-keV VMI reconstruction in daily contrast-enhanced abdominal DECT to improve image quality, diagnostic acceptability, and lesion conspicuity. KEY POINTS: • DLIR is superior to AV-50 in noise reduction, with less shifts of the average spatial frequency of NPS towards low frequency, and larger improvements of NPS noise, noise peak, SNR, and CNR values. • DLIR-M and DLIR-H generate better image quality in terms of image contrast, noise, sharpness, artificial sensation, and diagnostic acceptability than AV-50, while DLIR-H provides better lesion conspicuity than AV-50 and DLIR-M. • DLIR-H could be safely recommended as a new standard for routine low-keV VMI reconstruction in contrast-enhanced abdominal DECT to provide better lesion conspicuity and better image quality than the standard AV-50.

Automatic Classification of Mass Shape and Margin on Mammography with Artificial Intelligence: Deep CNN Versus Radiomics.

The purpose of this study is to test the feasibility for deep CNN-based artificial intelligence methods for automatic classification of the mass margin and shape, while radiomic feature-based machine learning methods were also implemented in this study as baseline and for comparison study. In this retrospective study, 596 patients with breast mass that underwent mammography from 4 hospitals were enrolled from January 2012 to October 2019. Margin and shape of each mass were annotated according to BI-RADS by 2 experienced radiologists. Deep CNN-based AI was implemented for the classification task based on Resnet50. Balanced sampler and CBAM were also used to improve the performance of the Deep CNNs. As comparison, image texture features were extracted and then dimensionality reduction methods (such as PCA, ICA) and classical classifiers (such as SVM, DT, KNN) were used for classification task. Based on Python programming software, accuracy (ACC) was used to evaluate the performance of the model, and the model with the highest ACC value was selected. Deep CNN based on Resnet50 with balanced sampler and CBAM achieved the best performance for both margin and shape classification, with ACC of 0.838 and 0.874, respectively. For the radiomics-based machine learning, the best performance for margin was achieved as 0.676 by the combination of FA + RF, while the best performance for shape was 0.802 by the combination of PCA + MLP. The feasibility for automatic classification with coarse labeling of the mass shape and margin was testified with the deep CNN-based AI methods, while radiomic feature-based machine learning methods achieved inferior classification results.

Clear Cell Adenocarcinoma Arising from Endometriosis in Abdominal Wall Cesarean Section Scar: A Case Report and Literature Review.

Background: Endometriosis developing in a cesarean section (CS) scar is an unusual event. Malignant transformation arising on the background of scar endometriosis in the abdominal wall is extremely rare. Herein we report a case of clear cell carcinoma (CCC) arising in the abdominal wall from endometriosis tissues following CS and review previous literature. Case Presentation: A 48-year-old gravida 2 para 1 female presented with an abdominal wall mass at her CS scar, which increased in size and became painful in the last 2 years. Physical examination showed a multilocular solid mass of about 13 cm, at the previous CS scar. Computed tomography (CT) and magnetic resonance imaging (MRI) revealed a 12.8cm × 7.7cm multi-septate cystic lesion on the anterior abdominal wall, and histological examination showed that CCC was caused by the transformation of abdominal wall endometriosis (AWE). Conclusion: An endometriosis-associated malignancy should be considered in the differential with any enlarging mass in the abdominal wall scar.

Predicting clinically significant prostate cancer with a deep learning approach: a multicentre retrospective study.

PURPOSE: This study aimed to develop deep learning (DL) models based on multicentre biparametric magnetic resonance imaging (bpMRI) for the diagnosis of clinically significant prostate cancer (csPCa) and compare the performance of these models with that of the Prostate Imaging and Reporting and Data System (PI-RADS) assessment by expert radiologists based on multiparametric MRI (mpMRI). METHODS: We included 1861 consecutive male patients who underwent radical prostatectomy or biopsy at seven hospitals with mpMRI. These patients were divided into the training (1216 patients in three hospitals) and external validation cohorts (645 patients in four hospitals). PI-RADS assessment was performed by expert radiologists. We developed DL models for the classification between benign and malignant lesions (DL-BM) and that between csPCa and non-csPCa (DL-CS). An integrated model combining PI-RADS and the DL-CS model, abbreviated as PIDL-CS, was developed. The performances of the DL models and PIDL-CS were compared with that of PI-RADS. RESULTS: In each external validation cohort, the area under the receiver operating characteristic curve (AUC) values of the DL-BM and DL-CS models were not significantly different from that of PI-RADS (P > 0.05), whereas the AUC of PIDL-CS was superior to that of PI-RADS (P < 0.05), except for one external validation cohort (P > 0.05). The specificity of PIDL-CS for the detection of csPCa was much higher than that of PI-RADS (P < 0.05). CONCLUSION: Our proposed DL models can be a potential non-invasive auxiliary tool for predicting csPCa. Furthermore, PIDL-CS greatly increased the specificity of csPCa detection compared with PI-RADS assessment by expert radiologists, greatly reducing unnecessary biopsies and helping radiologists achieve a precise diagnosis of csPCa.

Prognostic Index for Nonsmall Cell Lung Cancer Based on Immune-Related Genes Expression.

Immune system dysregulation is associated with tumor incidence and growth. Here, we established an RNA-based individualized immune signature associated with prognosis for nonsmall cell lung cancer (NSCLC) to guide adjuvant therapy. We downloaded publicly accessible data on RNA expression and clinical characteristics of NSCLC from the Cancer Genome Atlas (TCGA). From immune-related genes (IRGs) retrieved from the immunology database and analysis portal (ImmPort) database, we then screened differentially expressed immune-related genes (DEIRGs). Using overall survival (OS) as a clinical endpoint, we identified 26 prognostic DEIRGs via univariate and multivariate Cox regression analysis, and then developed a risk model based on these 26 IRGs with an area under the curve (AUC) of 0.701, and its predictive ability independent from other clinical factors. We also downloaded tumor immune infiltrate data and analyzed the correlations between lymphocytic infiltration with our risk scores, but found no significant association. Furthermore, we retrieved 86 differentially expressed transcription factors (TFs) to assess their regulatory relationships with the 26 prognostic DEIRGs. In summary, we developed a robust risk model to predict survival in patients with NSCLC, based on the expression of 26 IRGs. It provides novel predictive and therapeutic molecular targets.

A novel exfoliated manganese phosphoselenide as a high-performance anode material for lithium ions storage.

Layered manganese phosphoselenide (MnPSe3) is expected to be a potential anode for Li ions storage due to it combines the merits of phosphorus with metal selenide. It promotes charge transfer and ensures a high theoretical capacity of up to 746 mA h g-1. In this work, a comprehensive study clearly demonstrated that bulk MnPSe3 electrode is the inability to maintain the integrity of the structure with severe detectable fracture or pulverization after full lithiation/delithiation, resulting in poor rate capability and cycling stability. Additionally, exfoliated few-layered MnPSe3 nanoflakes by the ultrasonic method show enhanced electrical conductivity and resistance to volume expansion. It has a high initial discharge/charge capacity reaching to 524/796 mA h g-1 and outstanding cycling stability with charge capacities of 709 mA h g-1 after 100 cycles at 0.2 A g-1 within the potential window of 0.005-3 V vs. Li+/Li. While further improving the cycles, the retention rate was still held at ∼72% after 350 cycles. This work provides new insights into exploiting new novel layered materials, such as MnPSe3 as anodes for lithium-ion batteries.

Feasibility Study on Predicting Recurrence Risk of Bladder Cancer Based on Radiomics Features of Multiphase CT Images.

Background: Predicting the recurrence risk of bladder cancer is crucial for the individualized clinical treatment of patients with bladder cancer. Objective: To explore the radiomics based on multiphase CT images combined with clinical risk factors, and to further construct a radiomics-clinical model to predict the recurrence risk of bladder cancer within 2 years after surgery. Methods: Patients with bladder cancer who underwent surgical treatment at the First Affiliated Hospital of Soochow University from January 2016 to December 2019 were retrospectively included and followed up to record the disease recurrence. A total of 183 patients were included in the study, and they were randomly divided into training group and validation group in a ratio of 7: 3. The three basic models which are plain scan, corticomedullary phase, and nephrographic phase as well as two combination models, namely, corticomedullary phase + nephrographic phase and plain scan + corticomedullary phase + nephrographic phase, were built with the logistic regression algorithm, and we selected the model with higher performance and calculated the Rad-score (radiomics score) of each patient. The clinical risk factors and Rad-score were screened by Cox univariate and multivariate proportional hazard models in turn to obtain the independent risk factors, then the radiomics-clinical model was constructed, and their performance was evaluated. Results: Of the 183 patients included, 128 patients constituted the training group and 55 patients constituted the validation group. In terms of the radiomics-clinical model constructed by three independent risk factors-number of tumors, tumor grade, and Rad-score-the AUCs of the training group and validation group were 0.813 (95% CI 0.740-0.886) and 0.838 (95% CI 0.733-0.943), respectively. In the validation group, the diagnostic accuracy, sensitivity, and specificity were 0.727, 0.739, and 0.719, respectively. Conclusion: Combining with radiomics based on multiphase CT images and clinical risk factors, the radiomics-clinical model constructed to predict the recurrence risk of bladder cancer within 2 years after surgery had a good performance.

An Optimal Animal Model of Ischemic Stroke Established by Digital Subtraction Angiography-Guided Autologous Thrombi in Cynomolgus Monkeys.

Objective: Ischemic stroke seriously threatens human health, characterized by the high rates of incidence, disability, and death. Developing a reliable animal model that mimics most of the features of stroke is critical for pathological studies and clinical research. In this study, we aimed to establish and examine a model of middle cerebral artery occlusion (MCAO) guided by digital subtraction angiography (DSA) in cynomolgus monkeys. Materials and Methods: In this study, 15 adult male cynomolgus monkeys were enrolled. Under the guidance of DSA, a MCAO model was established by injecting an autologous venous clot into the middle cerebral artery (MCA) via femoral artery catheter. Thrombolytic therapy with alteplase (rt-PA) was given to eight of these monkeys at 3 h after the occlusion. Blood test and imaging examination, such as computed tomography angiography (CTA), CT perfusion (CTP), brain magnetic resonance imaging (MRI), and brain magnetic resonance angiography (MRA), were performed after the operation to identify the post-infarction changes. The behavioral performance of cynomolgus monkeys was continuously observed for 7 days after operation. The animals were eunthanized on the 8th day after operation, and then the brain tissues of monkeys were taken for triphenyltetrazolium chloride (TTC) staining. Results: Among the 15 cynomolgus monkeys, 12 of them were successfully modeled, as confirmed by the imaging findings and staining assessment. One monkey died of brain hernia resulted from intracranial hemorrhage confirmed by necropsy. DSA, CTA, and MRA indicated the presence of an arterial occlusion. CTP and MRI showed acute focal cerebral ischemia. TTC staining revealed infarct lesions formed in the brain tissues. Conclusion: Our study may provide an optimal non-human primate model for an in-depth study of the pathogenesis and treatment of focal cerebral ischemia.

Comparison of Computed and Acquired DWI in the Assessment of Rectal Cancer: Image Quality and Preoperative Staging.

Objective: The aim of the study was to evaluate the computed diffusion-weighted images (DWI) in image quality and diagnostic performance of rectal cancer by comparing with the acquired DWI. Methods: A total of 103 consecutive patients with primary rectal cancer were enrolled in this study. All patients underwent two DWI sequences, namely, conventional acquisition with b = 0 and 1,000 s/mm2 (aDWIb1,000) and another with b = 0 and 700 s/mm2 on a 3.0T MR scanner (MAGNETOM Prisma; Siemens Healthcare, Germany). The images (b = 0 and 700 s/mm2) were used to compute the diffusion images with b value of 1,000 s/mm2 (cDWIb1,000). Qualitative and quantitative analysis of both computed and acquired DWI images was performed, namely, signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and signal intensity ratio (SIR), and also diagnostic staging performance. Interclass correlation coefficients, weighted κ coefficient, Friedman test, Wilcoxon paired test, and McNemar or Fisher test were used for repeatability and comparison assessment. Results: Compared with the aDWIb1,000 images, the cDWIb1,000 ones exhibited significant higher scores of subjective image quality (all P <0.050). SNR, SIR, and CNR of the cDWIb1,000 images were superior to those of the aDWIb1,000 ones (P <0.001). The overall diagnostic accuracy of computed images was higher than that of the aDWIb1,000 images in T stage (P <0.001), with markedly better sensitivity and specificity in distinguishing T1-2 tumors from the T3-4 ones (P <0.050). Conclusion: cDWIb1,000 images from lower b values might be a useful alternative option and comparable to the acquired DWI, providing better image quality and diagnostic performance in preoperative rectal cancer staging.

Preoperative Prediction Power of Radiomics for Breast Cancer: A Systemic Review and Meta-Analysis.

Background: To evaluate the preoperative predictive value of radiomics in the diagnosis of breast cancer (BC). Methods: By searching PubMed and Embase libraries, our study identified 19 eligible studies. We conducted a meta-analysis to assess the differential value in the preoperative assessment of BC using radiomics methods. Results: Nineteen radiomics studies focusing on the diagnostic efficacy of BC and involving 5865 patients were enrolled. The integrated sensitivity and specificity were 0.84 (95% CI: 0.80-0.87, I 2 = 76.44%) and 0.83 (95% CI: 0.78-0.87, I 2 = 81.79%), respectively. The AUC based on the SROC curve was 0.91, indicating a high diagnostic value. Conclusion: Radiomics has shown excellent diagnostic performance in the preoperative prediction of BC and is expected to be a promising method in clinical practice.

A Patient with MELAS Syndrome Carried an M.3243A>G Mutation in Mitochondrial DNA and Multiple Nuclear Genetic Variants: A Case Report.

We discuss the involvement of nuclear genetic variants correlating to observed phenotype in this case study. In January 2020, the 19-year-old boy from Nantong, Jiangsu Province, China with epilepsy symptom was identified to have myelin loss in the motor and sensory nerves in the electromyogram examination. Brain magnetic resonance imaging (MRI) demonstrated high-intensity areas of small multifocal gray matter regions in the bilateral temporal, parietal, and occipital lobes. In the serum of the patient, the levels of lactate dehydrogenase (LDH) and lactic acid were higher than the normal range values in multiple tests. By subsequent whole exome sequencing (WES) including analysis of the mitochondrial genome, the patient was revealed to carry an m.3243A>G mutation in mitochondria MTTL1 gene which was confirmed by direct Sanger sequencing analysis. Thus, disease of the patient was diagnosed as mitochondrial myopathy, encephalopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome. According to WES analysis, the patient also carried multiple homozygous variants, which correlating to myelinloss and epilepsy in nuclear genes. The peripheral neuropathy of the patient carrying single mitochondrial m.3243A>G mutation could be caused by multiple nuclear DNA defect.

Value of the Application of CE-MRI Radiomics and Machine Learning in Preoperative Prediction of Sentinel Lymph Node Metastasis in Breast Cancer.

PURPOSE: To explore the value of machine learning model based on CE-MRI radiomic features in preoperative prediction of sentinel lymph node (SLN) metastasis of breast cancer. METHODS: The clinical, pathological and MRI data of 177 patients with pathologically confirmed breast cancer (81 with SLN positive and 96 with SLN negative) and underwent conventional DCE-MRI before surgery in the First Affiliated Hospital of Soochow University from January 2015 to May 2021 were analyzed retrospectively. The samples were randomly divided into the training set (n=123) and validation set (n= 54) according to the ratio of 7:3. The radiomic features were derived from DCE-MRI phase 2 images, and 1,316 original eigenvectors are normalized by maximum and minimum normalization. The optimal feature filter and selection operator (LASSO) algorithm were used to obtain the optimal features. Five machine learning models of Support Vector Machine, Random Forest, Logistic Regression, Gradient Boosting Decision Tree, and Decision Tree were constructed based on the selected features. Radiomics signature and independent risk factors were incorporated to build a combined model. The receiver operating characteristic curve and area under the curve were used to evaluate the performance of the above models, and the accuracy, sensitivity, and specificity were calculated. RESULTS: There is no significant difference between all clinical and histopathological variables in breast cancer patients with and without SLN metastasis (P >0.05), except tumor size and BI-RADS classification (P< 0.01). Thirteen features were obtained as optimal features for machine learning model construction. In the validation set, the AUC (0.86) of SVM was the highest among the five machine learning models. Meanwhile, the combined model showed better performance in sentinel lymph node metastasis (SLNM) prediction and achieved a higher AUC (0.88) in the validation set. CONCLUSIONS: We revealed the clinical value of machine learning models established based on CE-MRI radiomic features, providing a highly accurate, non-invasive, and convenient method for preoperative prediction of SLNM in breast cancer patients.

Macrophage-Mediated Porous Magnetic Nanoparticles for Multimodal Imaging and Postoperative Photothermal Therapy of Gliomas.

Because of the blood-brain barrier and the high infiltration of glioma cells, the diagnostic accuracy and treatment efficiency of gliomas are still facing challenges. There is an urgent need to explore the integration of diagnostic and therapeutic methods to achieve an accurate diagnosis, guide surgery, and inhibit postoperative recurrence. In this work, we developed a macrophage loaded with a photothermal nanoprobe (MFe3O4-Cy5.5), which is able to cross the blood-brain barrier and accumulate into deep gliomas to achieve multimodal imaging and guided glioma surgery purposes. With desirable probing depth and high signal-to-noise ratio, Fe3O4-Cy5.5 can perform fluorescence, photoacoustic, and magnetic resonance imaging, which can distinguish brain tumors from the surrounding normal tissues and accurately guide glioma resection. Meanwhile, Fe3O4-Cy5.5 can effectively induce local photothermal therapy and inhibit the recurrence of glioma after surgery. These results demonstrate that the macrophage-mediated Fe3O4-Cy5.5, which can achieve a multimodal diagnosis, accurate imaging-guided surgery, and effective photothermal therapy, is a promising nanoplatform for gliomas.