Pelvic target volume inter-fractional motion during radiotherapy for cervical cancer with daily iterative cone beam computed tomography.

BACKGROUND: Tumor regression and organ movements indicate that a large margin is used to ensure target volume coverage during radiotherapy. This study aimed to quantify inter-fractional movements of the uterus and cervix in patients with cervical cancer undergoing radiotherapy and to evaluate the clinical target volume (CTV) coverage. METHODS: This study analyzed 303 iterative cone beam computed tomography (iCBCT) scans from 15 cervical cancer patients undergoing external beam radiotherapy. CTVs of the uterus (CTV-U) and cervix (CTV-C) contours were delineated based on each iCBCT image. CTV-U encompassed the uterus, while CTV-C included the cervix, vagina, and adjacent parametrial regions. Compared with the planning CTV, the movement of CTV-U and CTV-C in the anterior-posterior, superior-inferior, and lateral directions between iCBCT scans was measured. Uniform expansions were applied to the planning CTV to assess target coverage. RESULTS: The motion (mean ± standard deviation) in the CTV-U position was 8.3 ± 4.1 mm in the left, 9.8 ± 4.4 mm in the right, 12.6 ± 4.0 mm in the anterior, 8.8 ± 5.1 mm in the posterior, 5.7 ± 5.4 mm in the superior, and 3.0 ± 3.2 mm in the inferior direction. The mean CTV-C displacement was 7.3 ± 3.2 mm in the left, 8.6 ± 3.8 mm in the right, 9.0 ± 6.1 mm in the anterior, 8.4 ± 3.6 mm in the posterior, 5.0 ± 5.0 mm in the superior, and 3.0 ± 2.5 mm in the inferior direction. Compared with the other tumor (T) stages, CTV-U and CTV-C motion in stage T1 was larger. A uniform CTV planning treatment volume margin of 15 mm failed to encompass the CTV-U and CTV-C in 11.1% and 2.2% of all fractions, respectively. The mean volume change of CTV-U and CTV-C were 150% and 51%, respectively, compared with the planning CTV. CONCLUSIONS: Movements of the uterine corpus are larger than those of the cervix. The likelihood of missing the CTV is significantly increased due to inter-fractional motion when utilizing traditional planning margins. Early T stage may require larger margins. Personal radiotherapy margining is needed to improve treatment accuracy.

Whole Genome Sequencing Highlights the Pathogenic Profile in Nocardia Keratitis.

Purpose: Nocardia keratitis is a serious and sight-threatening condition. This study aims to reveal the virulence and antimicrobial resistance gene profile of Nocardia strains using whole genome sequencing. Methods: Whole-genome sequencing was performed on 23 cornea-derived Nocardia strains. Together with genomic data from the respiratory tract and the environment, 141 genomes were then utilized for phylogenetic and pan-genome analyses, followed by virulence and antibiotic resistance analysis. The correlations between virulence genes and pathogenicity were experimentally validated, including the characteristics of Nocardia colonies and clinical and histopathological evaluations of Nocardia keratitis mice models. Results: Whole-genome sequencing of 141 Nocardia strains revealed a mean of 220 virulence genes contributed to bacterial pathogenesis. The mce gene family analysis led to the categorization of strains from the cornea into groups A, B, and C. The colonies of group C had the largest diameter, height, and fastest growth rate. The size of corneal ulcers and the clinical scores showed a significant increase in mouse models induced by group C. The relative expression levels of pro-inflammatory cytokines (CD4, IFN-γ, IL-6Rα, and TNF-α) in the lesion area exhibited an increasing trend from group A to group C. Antibiotic resistance genes (ARGs) spanned nine distinct drug classes, four resistance mechanisms, and seven primary antimicrobial resistance gene families. Conclusions: Whole genome sequencing highlights the pathogenic role of mce gene family in Nocardia keratitis. Its distribution pattern may contribute to the distinct characteristics of the growth of Nocardia colonies and the clinical severity of the mice models.

Acanthamoeba Keratitis in China: Genotypic and Clinical Correlations.

Purpose: To investigate the relationship between Acanthamoeba genotypes, clinical manifestations, and outcomes in Acanthamoeba keratitis (AK) patients. Methods: This retrospective study included 159 culture-confirmed AK patients. Patients' data were collected, including demographics, initial diagnosis, treatments, and clinical features. The genotype of Acanthamoeba was identified through sequencing the Diagnostic Fragment 3 (DF3) region in the small ribosomal subunit RNA genes. The phylogenetic tree was constructed using the ClustalW model and maximum likelihood method. Cases with "poor outcome" were defined based on specific clinical criteria, including corneal perforation, keratoplasty, other eye surgery, duration of anti-amoebic therapy ≥8.0 months, and final visual acuity ≤20/80. "Better outcome" cases were the remainder. The correlation between T4 subtypes, clinical phenotypes, and clinical prognosis were further analyzed. Results: In this study, AK was primarily attributed to the T4A genotype, with a positive correlation between geographical and genetic distances. The primary clinical associated with T4 subtypes was deep stromal infiltration. Results was also showed a significant association between T4 subtypes and clinical outcomes (P = 0.021). Further analysis revealed that T4C was closely associated with a better prognosis (P = 0.040) and T4D with worse outcomes (P = 0.013). Conclusions: In China, AK was predominantly caused by the T4A subtype. Geographical distance positively correlated with genetic distance. Clinical prognosis varied among different subtypes, notably in T4C and T4D. Translational Relevance: This study demonstrated the association between T4 subtypes and clinical phenotypes, as well as the effects of T4 subtypes on clinical prognosis.

Prospects for daily online adaptive radiotherapy for cervical cancer: Auto-contouring evaluation and dosimetric outcomes.

BACKGROUND: Training senior radiation therapists as "adapters" to manage influencers and target editing is critical in daily online adaptive radiotherapy (oART) for cervical cancer. The purpose of this study was to evaluate the accuracy and dosimetric outcomes of automatic contouring and identify the key areas for modification. METHODS: A total of 125 oART fractions from five postoperative cervical cancer patients and 140 oART fractions from five uterine cervical cancer patients treated with daily iCBCT-guided oART were enrolled in this prospective study. The same adaptive treatments were replanned using the Ethos automatic contours workflow without manual contouring edits. The clinical target volume (CTV) was subdivided into several separate regions, and the average surface distance dice (ASD), centroid deviation, dice similarity coefficient (DSC), and 95% Hausdorff distance (95% HD) were used to evaluate contouring for the above portions. Dosimetric results from automatic oART plans were compared to supervised oART plans to evaluate target volumes and organs at risk (OARs) dose changes. RESULTS: Overall, the paired CTV had high overlap rates, with an average DSC value greater than 0.75. The uterus had the largest consistency differences, with ASD, centroid deviation, and 95% HD being 2.67 ± 1.79 mm, 17.17 ± 12 mm, and 10.45 ± 5.68 mm, respectively. The consistency differences of the lower nodal CTVleft and nodal CTVright were relatively large, with ASD, centroid deviation, and 95% HD being 0.59 ± 0.53 mm, 3.6 ± 2.67 mm, and 5.41 ± 4.08 mm, and 0.59 ± 0.51 mm, 3.6 ± 2.54 mm, and 4.7 ± 1.57 mm, respectively. The automatic online-adapted plan met the clinical requirements of dosimetric coverage for the target volume and improved the OAR dosimetry. CONCLUSIONS: The accuracy of automatic contouring from the Ethos adaptive platform is considered clinically acceptable for cervical cancer, and the uterus, upper vaginal cuff, and lower nodal CTV are the areas that need to be focused on in training.

Evaluation of PTV margins with daily iterative online adaptive radiotherapy for postoperative treatment of endometrial and cervical cancer: a prospective single-arm phase 2 study.

BACKGROUND: To determine the optimal planning target volume (PTV) margins for adequate coverage by daily iterative cone-beam computed tomography (iCBCT)-guided online adaptive radiotherapy (oART) in postoperative treatment of endometrial and cervical cancer and the benefit of reducing PTV margins. METHODS: Fifteen postoperative endometrial and cervical cancer patients treated with daily iCBCT-guided oART were enrolled in this prospective phase 2 study. Pre- and posttreatment iCBCT images of 125 fractions from 5 patients were obtained as a training cohort, and clinical target volumes (CTV) were contoured separately. Uniform three-dimensional expansions were applied to the PTVpre to assess the minimum margin required to encompass the CTVpost. The dosimetric advantages of the proposed online adaptive margins were compared with conventional margin plans (7-15 mm) using an oART emulator in another cohort of 125 iCBCT scans. A CTV-to-PTV expansion was verified on a validation cohort of 253 fractions from 10 patients, and further margin reduction and acute toxicity were studied. RESULTS: The average time from pretreatment iCBCT to posttreatment iCBCT was 22 min. A uniform PTV margin of 5 mm could encompass nodal CTVpost in 100% of the fractions (175/175) and vaginal CTVpost in 98% of the fractions (172/175). The margin of 5 mm was verified in our validation cohort, and the nodal PTV margin could be further reduced to 4 mm if ≥ 95% CTV coverage was predicted to be achieved. The adapted plan with a 5 mm margin significantly improved pelvic organ-at-risk dosimetry compared with the conventional margin plan. Grade 3 toxicities were observed in only one patient with leukopenia, and no patients experienced acute urinary toxicity. CONCLUSION: In the postoperative treatment of endometrial and cervical cancer, oART could reduce PTV margins to 5 mm, which significantly decrease the dose to critical organs at risk and potentially lead to a lower incidence of acute toxicity.

Estimating Functional Brain Networks by Low-Rank Representation With Local Constraint.

The functional architecture undergoes alterations during the preclinical phase of Alzheimer's disease. Consequently, the primary research focus has shifted towards identifying Alzheimer's disease and its early stages by constructing a functional connectivity network based on resting-state fMRI data. Recent investigations show that as Alzheimer's Disease (AD) progresses, modular tissue and connections in the core brain areas of AD patients diminish. Sparse learning methods are powerful tools for understanding Functional Brain Networks (FBNs) with Regions of Interest (ROIs) and a connectivity matrix measuring functional coherence between them. However, these tools often focus exclusively on functional connectivity measures, neglecting the brain network's modularity. Modularity orchestrates dynamic activities within the FBN to execute intricate cognitive tasks. To provide a comprehensive delineation of the FBN, we propose a local similarity-constrained low-rank sparse representation (LSLRSR) method that encodes modularity information under a manifold-regularized network learning framework and further formulate it as a low-rank sparse graph learning problem, which can be solved by an efficient optimization algorithm. Specifically, for each modularity structure, the Schatten p-norm regularizer reduces the reconstruction error and provides a better approximation of the low-rank constraint. Furthermore, we adopt a manifold-regularized local similarity prior to infer the intricate relationship between subnetwork similarity and modularity, guiding the modeling of FBN. Additionally, the proximal average method approximates the joint solution's proximal map, and the resulting nonconvex optimization problems are solved using the alternating direction multiplier method (ADMM). Compared to state-of-the-art methods for constructing FBNs, our algorithm generates a more modular FBN. This lays the groundwork for further research into alterations in brain network modularity resulting from diseases.

The value of multiparametric MRI radiomics in predicting IDH genotype in glioma before surgery.

Objective: To explore the value of multiparametric magnetic resonance imaging(MRI) radiomics in the preoperative prediction of isocitrate dehydrogenase (IDH) genotype for gliomas. Methods: The preoperative routine MRI sequences of 114 patients with pathologically confirmed grade II-IV gliomas were retrospectively analysed. All patients were randomly divided into training cohort(n=79) and validation cohort(n=35) in the ratio of 7:3. After feature extraction, we eliminated covariance by calculating the linear correlation coefficients between features, and then identified the best features using the F-test. The Logistic regression was used to build the radiomics model and the clinical model, and to build the combined model. Assessment of these models by subject operating characteristic (ROC) curves, area under the curve (AUC), sensitivity and specificity. Results: The multiparametric radiomics model was built by eight selected radiomics features and yielded AUC values of 0.974 and 0.872 in the training and validation cohorts, which outperformed the conventional models. After incorporating the clinical model, the combined model outperformed the radiomics model, with AUCs of 0.963 and 0.892 for the training and validation cohorts. Conclusion: Radiomic models based on multiparametric MRI sequences could help to predict glioma IDH genotype before surgery.

A case report of anterior mediastinal angiomyolipoma that invaded the left thoracic cavity.

RATIONALE: Angiomyolipoma is a mesenchymal tumor composed of blood vessels, smooth muscle, and mature adipose tissue. It is most commonly found in the kidney, and is rare outside the kidney, especially in the mediastinum. Only about 12 cases have been reported worldwide so far. PATIENT CONCERNS: We report a young female patient who had been found with a left thoracic mass for 19 years. In the past 19 years, the patient had no chest pain, dyspnea and other symptoms, but this time she visited the doctor because of cough, and there were no other clinical signs. DIAGNOSES: The patient underwent computed tomography plain scan and enhanced scan after admission with imaging manifestations of a mixed density mass in the left chest cavity, calcification and fat density in the inside, and tortuous blood vessels after enhancement. Combined with imaging, the diagnosis was teratoma, not excluding hamartoma. INTERVENTIONS: The patient underwent a central open thoracic giant mass resection. OUTCOMES: The postoperative pathology confirmed that it was angiomyolipoma originating from anterior mediastinum invasion of the left chest cavity, and no clear recurrence was seen after 1 year of postoperative follow-up. LESSONS: Angiomyolipomas in the mediastinum are rare, especially those that invade the thorax. This article describes the clinical, imaging and pathological features of the patient in detail, which improves the understanding of the disease of clinical and imaging doctors, and provides a basis for the differential diagnosis of mediastinal lesions.

The value of ultrasound combined with CT in identifying early low-grade appendiceal mucinous neoplasm and appendicitis.

Objective: The aim of this study is to investigate the value of ultrasound combined with computed tomography (CT) in identifying early low-grade appendiceal mucinous neoplasm and appendicitis. Methods: Patients with early low-grade appendiceal mucinous neoplasm and appendicitis from September 2017 to September 2021, including 40 patients with low-grade appendiceal mucinous neoplasm and 40 patients with appendicitis, were collected in this study. Clinical data as well as ultrasound and CT findings of all patients were retrospectively analyzed. Univariate and multivariate logistic regression analyses were applied to establish the ultrasound model, the CT model, and the combined model. Results: The nomogram showed that specific characteristics of CT were dilated appendiceal diameter and clear surrounding fat space in the low-grade appendiceal mucinous neoplasm and that specific characteristics of ultrasound were thin or clear layer appendix wall and flocculent echo in the appendix cavity. These four features were used to construct a nomogram for predicting early low-grade appendiceal mucinous neoplasm, and the area under the curve value was 0.839. Conclusion: Ultrasound combined with CT for diagnosis of early low-grade appendiceal mucinous neoplasm has a significant value; when found significantly dilated appendix in the lower right abdomen, with thin wall, wall calcification, clear surrounding fat space, and progressive enhancement, especially non-specific symptoms similar to appendicitis, the physician should timely consider the possibility of low-grade appendiceal mucinous neoplasm.

Radiomics for preoperative prediction of early recurrence in hepatocellular carcinoma: a meta-analysis.

Background/Objective: Early recurrence (ER) affects the long-term survival prognosis of patients with hepatocellular carcinoma (HCC). Many previous studies have utilized CT/MRI-based radiomics to predict ER after radical treatment, achieving high predictive value. However, the diagnostic performance of radiomics for the preoperative identification of ER remains uncertain. Therefore, we aimed to perform a meta-analysis to investigate the predictive performance of radiomics for ER in HCC. Methods: A systematic literature search was conducted in PubMed, Web of Science (including MEDLINE), EMBASE and the Cochrane Central Register of Controlled Trials to identify studies that utilized radiomics methods to assess ER in HCC. Data were extracted and quality assessed for retrieved studies. Statistical analyses included pooled data, tests for heterogeneity, and publication bias. Meta-regression and subgroup analyses were performed to investigate potential sources of heterogeneity. Results: The analysis included fifteen studies involving 3,281 patients focusing on preoperative CT/MRI-based radiomics for the prediction of ER in HCC. The combined sensitivity, specificity, and area under the curve (AUC) of the receiver operating characteristic were 75% (95% CI: 65-82), 78% (95% CI: 68-85), and 83% (95% CI: 79-86), respectively. The combined positive likelihood ratio, negative likelihood ratio, diagnostic score, and diagnostic odds ratio were 3.35 (95% CI: 2.41-4.65), 0.33 (95% CI: 0.25-0.43), 2.33 (95% CI: 1.91-2.75), and 10.29 (95% CI: 6.79-15.61), respectively. Substantial heterogeneity was observed among the studies (I²=99%; 95% CI: 99-100). Meta-regression showed imaging equipment contributed to the heterogeneity of specificity in subgroup analysis (P= 0.03). Conclusion: Preoperative CT/MRI-based radiomics appears to be a promising and non-invasive predictive approach with moderate ER recognition performance.

Value of diffusion-weighted imaging in diagnosis and therapy response assessment of hepatic fungal infection in patients with acute leukemia.

OBJECTIVE: To investigate the role of diffusion-weighted imaging (DWI) for diagnosis and posttreatment assessment of hepatic fungal infection in patients with acute leukemia. METHODS: Patients with acute leukemia and highly suspected hepatic fungal infection were collected in the study. All the patients underwent MRI examination, including initial and follow-up DWI. The apparent diffusion coefficient (ADC) values of the lesions and the normal liver parenchyma were compared using Student's t-test. The ADC values of the hepatic fungal lesions of pretreatment and posttreatment were compared using paired t-test. RESULTS: A total of 13 patients with hepatic fungal infections have enrolled this study. Hepatic lesions were rounded or oval shaped, measured from 0.3 to 3 cm in diameter. The lesions showed significantly hyperintense signal on DWI and markedly hypointense signal on the ADC map, reflecting a marked restricted diffusion. The mean ADC values of the lesions were significantly lower than those of normal liver parenchyma (1.08 ± 0.34 × 10-3 vs. 1.98 ± 0.12 × 10-3 mm2 /s, p < 0.001). After treatment, the mean ADC values of the lesions were significantly increased when comparing with those of pretreatment (1.39 ± 0.29 × 10-3 vs. 1.06 ± 0.10 × 10-3 mm2 /s, p = .016). CONCLUSION: DWI can provide diffusion information of hepatic fungal infection in patients with acute leukemia, which could be taken as a valuable tool for diagnosis and therapy response assessment of these patients.

Classification of Alzheimer's disease based on hippocampal multivariate morphometry statistics.

BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disease characterized by progressive cognitive decline, and mild cognitive impairment (MCI) is associated with a high risk of developing AD. Hippocampal morphometry analysis is believed to be the most robust magnetic resonance imaging (MRI) markers for AD and MCI. Multivariate morphometry statistics (MMS), a quantitative method of surface deformations analysis, is confirmed to have strong statistical power for evaluating hippocampus. AIMS: We aimed to test whether surface deformation features in hippocampus can be employed for early classification of AD, MCI, and healthy controls (HC). METHODS: We first explored the differences in hippocampus surface deformation among these three groups by using MMS analysis. Additionally, the hippocampal MMS features of selective patches and support vector machine (SVM) were used for the binary classification and triple classification. RESULTS: By the results, we identified significant hippocampal deformation among the three groups, especially in hippocampal CA1. In addition, the binary classification of AD/HC, MCI/HC, AD/MCI showed good performances, and area under curve (AUC) of triple-classification model achieved 0.85. Finally, positive correlations were found between the hippocampus MMS features and cognitive performances. CONCLUSIONS: The study revealed significant hippocampal deformation among AD, MCI, and HC. Additionally, we confirmed that hippocampal MMS can be used as a sensitive imaging biomarker for the early diagnosis of AD at the individual level.

Pleomorphic adenoma of the tongue: A case report.

RATIONALE: Salivary gland tumors account for approximately 3% of all tumors, most of which are benign, with pleomorphic adenomas being the most common, occurring mostly in middle-aged women, mostly originating from the major salivary glands and, to a lesser extent, from the minor salivary glands, with the tongue being a very rare site of occurrence. To date, case reports of pleomorphic adenoma at the root of the tongue are also rare. PATIENT CONCERNS: A 56-year-old male patient with no obvious cause of foreign body sensation in the pharynx, sputum, no pain, no blood in the sputum, no dysphagia, and no difficulty in swallowing and breathing, which was significantly aggravated in the past 2 weeks, with difficulty in swallowing, breath-holding on lying down. DIAGNOSES: computed tomography and magnetic resonance imaging revealed a soft tissue mass at the root of the left tongue, which involved the tongue body in the forward direction. Electronic laryngopharyngoscopy showed a left-sided tongue root mass with a poorly smooth mucosa, covered with a mucous white pseudomembrane and a localized brownish-black crust without active bleeding. The final pathological findings showed a pleomorphic adenoma. INTERVENTIONS: Postoperative symptomatic treatment was given, and the patient recovered well. Eight days after surgery, the patient was discharged from the hospital, and the pharyngeal pain basically subsided at the time of discharge, with no fever and no pharyngeal discomfort. Postoperative laryngoscopy showed smooth mucosa of the pharyngeal cavity, good pseudomembrane formation in the operated area, no active bleeding, no purulent secretions, and normal blood routine on recheck. The medical advice after discharge was firstly, full rest for 1 week, secondly, continue the oral anti-inflammatory treatment, 1 week after the operation need to review the outpatient clinic, finally, if there are any uncomfortable symptoms, seek medical attention in time. OUTCOMES: At present, the patient has been followed up for half a year and has recovered well from the operation without any discomfort. LESSONS: It is very rare to find a pleomorphic adenoma of the tongue, and it occurs mostly in middle-aged women. In clinical diagnosis, it is sometimes difficult to distinguish it from malignant tumor of the tongue.

Time-Course Transcriptomic Analysis Reveals the Crucial Roles of PANoptosis in Fungal Keratitis.

Purpose: Fungal keratitis (FK) is a serious corneal infection with high morbidity. Host immune responses function as a double-edged sword by eradicating fungal pathogens while also causing corneal damage, dictating the severity, progression, and outcome of FK. However, the underlying immunopathogenesis remains elusive. Methods: Time-course transcriptome was performed to illustrate the dynamic immune landscape in a mouse model of FK. Integrated bioinformatic analyses included identification of differentially expressed genes, time series clustering, Gene Ontology enrichment, and inference of infiltrating immune cells. Gene expression was verified by quantitative polymerase chain reaction (qPCR), Western blot, or immunohistochemistry. Results: FK mice exhibited dynamic immune responses with concerted trends with clinical score, transcriptional alteration, and immune cell infiltration score peaking at 3 days post infection (dpi). Disrupted substrate metabolism, broad immune activation, and corneal wound healing occurred sequentially in early, middle, and late stages of FK. Meanwhile, dynamics of infiltrating innate and adaptive immune cells displayed distinct characteristics. Proportions of dendritic cells showed overall decreasing trend with fungal infection, whereas that of macrophages, monocytes, and neutrophils rose sharply in early stage and then gradually decreased as inflammation resolved. Activation of adaptive immune cells was also observed in late stage of infection. Furthermore, shared immune responses and activation of AIM2-, pyrin-, and ZBP1-mediated PANoptosis were revealed across different time points. Conclusions: Our study profiles the dynamic immune landscape and highlights the crucial roles of PANoptosis in FK pathogenesis. These findings provide novel insights into host responses to fungi and contribute to the development of PANoptosis-targeted therapeutics for patients with FK.

Development and multi-center validation of machine learning model for early detection of fungal keratitis.

BACKGROUND: Fungal keratitis (FK) is a leading cause of corneal blindness in developing countries due to poor clinical recognition and laboratory identification. Here, we aimed to identify the distinct clinical signature of FK and develop a diagnostic model to differentiate FK from other types of infectious keratitis. METHODS: We reviewed the electronic health records (EHRs) of all patients with suspected infectious keratitis in Beijing Tongren Hospital from January 2011 to December 2021. Twelve clinical signs of slit-lamp images were assessed by Lasso regression analysis and collinear variables were excluded. Three models based on binary logistic regression, random forest classification, and decision tree classification were trained for FK diagnosis and employed for internal validation. Independent external validation of the models was performed in a cohort of 420 patients from seven different ophthalmic centers to evaluate the accuracy, specificity, and sensitivity in real world. FINDINGS: Three diagnostic models of FK based on binary logistic regression, random forest classification, and decision tree classification were established and internal validation were achieved with the mean AUC of 0.916, 0.920, and 0.859, respectively. The models were well-calibrated by external validation using a prospective cohort including 210 FK and 210 non-FK patients from seven eye centers across China. The diagnostic model with the binary logistic regression algorithm classified the external validation dataset with a sensitivity of 0.907 (0.774, 1.000), specificity 0.899 (0.750, 1.000), accuracy 0.905 (0.805, 1.000), and AUC 0.903 (0.808, 0.998). INTERPRETATION: Our model enables rapid identification of FK, which will help ophthalmologists to establish a preliminary diagnosis and to improve the diagnostic accuracy in clinic. FUNDING: The Open Research Fund from the National Key Research and Development Program of China (2021YFC2301000) and the Open Research Fund from Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beijing Tongren Hospital, Beihang University &Capital Medical University (BHTR-KFJJ-202001) supported this study.

Tissues margin-based analytical anisotropic algorithm boosting method via deep learning attention mechanism with cervical cancer.

PURPOSE: Speed and accuracy are two critical factors in dose calculation for radiotherapy. Analytical Anisotropic Algorithm (AAA) is a rapid dose calculation algorithm but has dose errors in tissue margin area. Acuros XB (AXB) has high accuracy but takes long time to calculate. To improve the dose accuracy on the tissue margin area for AAA, we proposed a novel deep learning-based dose accuracy improvement method using Margin-Net combined with Margin-Loss. METHODS: A novel model 'Margin-Net' was designed with a Margin Attention Mechanism to generate special margin-related features. Margin-Loss was introduced to consider the dose errors and dose gradients in tissues margin area. Ninety-five VMAT cervical cancer cases with paired AAA and AXB dose were enrolled in our study: 76 cases for training and 19 cases for testing. Tissues Margin Masks were generated from RT contours with 6 mm extension. Tissues Margin Mask, AAA dose and CTs were input data; AXB dose was used as reference dose for model training and evaluation. Comparison experiments were performed to evaluated effectiveness of Margin-Net and Margin-Loss. RESULTS: Compared to AXB dose, the 3D gamma passing rate (1%/1 mm, 10% threshold) for 19 test cases 95.75 ± 1.05% using Margin-Net with Margin-Loss, which was significantly higher than the original AAA dose (73.64 ± 3.46%). The passing rate reduced to 94.07 ± 1.16% without Margin-Loss and 87.3 ± 1.18% if Margin-Net key structure 'MAM' was also removed. CONCLUSION: The proposed novel tissues margin-based dose conversion method can significantly improve the dose accuracy of Analytical Anisotropic Algorithm to be comparable to AXB algorithm. It can potentially improve the efficiency of treatment planning process with low demanding of computation resources.

Patient-specific quality assurance prediction models based on machine learning for novel dual-layered MLC linac.

BACKGROUND: Patient-specific quality assurance (PSQA) is an indispensable and essential procedure in radiotherapy workflow, and several studies have been done to develop prediction models based on the conventional C-arm linac of single-layered multileaf collimator (MLC) with machine learning (ML) and deep learning techniques to predict PSQA results and improve efficiency. Recently, a newly designed O-ring gantry linac "Halcyon" equipped with unique jawless stacked-and-staggered dual-layered MLC was released. However, few studies have focused on developing PSQA prediction models for this novel dual-layered MLC system. PURPOSE: To evaluate the performance of ML to predict PSQA results of fixed field intensity-modulated radiation therapy (FF-IMRT) plans for linac equipped with dual-layered MLC. METHODS AND MATERIALS: A total of 213 FF-IMRT treatment plans, including 1383 beams from various treatment sites, were selected and delivered with portal dosimetry verification on Halcyon linac. Gamma analysis was performed using 1%/1, 2%/2, and 3%/2 mm criteria with a 10% threshold. The training set (TS) of ML models consisted of 1106 beams, and an independent evaluation set (ES) consisted of 277 beams. For each beam, 33 complexity metrics were extracted as input data for training models. Three ML algorithms (gradient boosting decision tree/GBDT, random forest/RF, and Poisson Lasso/PL) were utilized to build the models and predict gamma passing rates (GPRs). To improve the prediction accuracy in the rare region, a method of reweighting for TS has been performed and compared to the unweighted results. The importance of complexity metrics was studied by permuted interesting features. RESULTS: The GBDT model had the best performance in this study. In ES, the minimal mean prediction error for unweighted results was 1.93%, 1.16%, 0.78% under 1%/1, 2%/2, and 3%/2 mm criteria, respectively, from GBDT model. Comparing to the unweighted results, the models after reweighting gained up to 30% improvement in the rare region, whereas the overall prediction error was slightly worse depending on the kind of models. For feature importance, 2 tree-based models (GBDT and RF) had in common the top 10 most important metrics as well as the same metric with the largest impact. CONCLUSION: For linac equipped with novel dual-layered MLC, the ML model based on GBDT algorithm shows a certain degree of accuracy for GPRs prediction. The specific ML model for dual-layered MLC configuration could be a useful tool for physicists detecting PSQA measurement failures.

Deep learning-based classification of infectious keratitis on slit-lamp images.

Background: Infectious keratitis (IK) is an ocular emergency caused by a variety of microorganisms, including bacteria, fungi, viruses, and parasites. Culture-based methods were the gold standard for diagnosing IK, but difficult biopsy, delaying report, and low positive rate limited their clinical application. Objectives: This study aims to construct a deep-learning-based auxiliary diagnostic model for early IK diagnosis. Design: A retrospective study. Methods: IK patients with pathological diagnosis were enrolled and their slit-lamp photos were collected. Image augmentation, normalization, and histogram equalization were applied, and five image classification networks were implemented and compared. Model blending technique was used to combine the advantages of single model. The performance of combined model was validated by 10-fold cross-validation, receiver operating characteristic curves (ROC), confusion matrix, Gradient-wright class activation mapping (Grad-CAM) visualization, and t-distributed Stochastic Neighbor Embedding (t-SNE). Three experienced cornea specialists were invited and competed with the combined model on making clinical decisions. Results: Overall, 4830 slit-lamp images were collected from patients diagnosed with IK between June 2010 and May 2021, including 1490 (30.8%) bacterial keratitis (BK), 1670 (34.6%) fungal keratitis (FK), 600 (12.4%) herpes simplex keratitis (HSK), and 1070 (22.2%) Acanthamoeba keratitis (AK). KeratitisNet, the combination of ResNext101_32x16d and DenseNet169, reached the highest accuracy 77.08%. The accuracy of KeratitisNet for diagnosing BK, FK, AK, and HSK was 70.27%, 77.71%, 83.81%, and 79.31%, and AUC was 0.86, 0.91, 0.96, and 0.98, respectively. KeratitisNet was mainly confused in distinguishing BK and FK. There were 20% of BK cases mispredicted into FK and 16% of FK cases mispredicted into BK. In diagnosing each type of IK, the accuracy of model was significantly higher than that of human ophthalmologists (p < 0.001). Conclusion: KeratitisNet demonstrates a good performance on clinical IK diagnosis and classification. Deep learning could provide an auxiliary diagnostic method to help clinicians suspect IK using different corneal manifestations.

Altered Brain Volume, Microstructure Metrics and Functional Connectivity Features in Multiple System Atrophy.

In order to deeply understand the specific patterns of volume, microstructure, and functional changes in Multiple System Atrophy patients with cerebellar ataxia syndrome (MSA-c), we perform the current study by simultaneously applying structural (T1-weighted imaging), Diffusion tensor imaging (DTI), functional (BOLD fMRI) and extended Network-Based Statistics (extended-NBS) analysis. Twenty-nine MSA-c type patients and twenty-seven healthy controls (HCs) were involved in this study. First, we analyzed the whole brain changes of volume, microstructure, and functional connectivity (FC) in MSA-c patients. Then, we explored the correlations between significant multimodal MRI features and the total Unified Multiple System Atrophy Rating Scale (UMSARS) scores. Finally, we searched for sensitive imaging biomarkers for the diagnosis of MSA-c using support vector machine (SVM) classifier. Results showed significant grey matter atrophy in cerebellum and white matter microstructural abnormalities in cerebellum, left fusiform gyrus, right precentral gyrus and lingual gyrus. Extended-NBS analysis found two significant different connected components, featuring altered functional connectivity related to left and right cerebellar sub-regions, respectively. Moreover, the reduced fiber bundle counts at right Cerebellum_3 (Cbe3) and decreased fractional anisotropy (FA) values at bilateral Cbe9 were negatively associated with total UMSARS scores. Finally, the significant features at left Cbe9, Cbe1, and Cbe7b were found to be useful as sensitive biomarkers to differentiate MSA-c from HCs according to the SVM analysis. These findings advanced our understanding of the neural pathophysiological mechanisms of MSA from the perspective of multimodal neuroimaging.

Investigation of Underlying Association Between Whole Brain Regions and Alzheimer's Disease: A Research Based on an Artificial Intelligence Model.

Alzheimer's disease (AD) is the most common form of dementia, causing progressive cognitive decline. Radiomic features obtained from structural magnetic resonance imaging (sMRI) have shown a great potential in predicting this disease. However, radiomic features based on the whole brain segmented regions have not been explored yet. In our study, we collected sMRI data that include 80 patients with AD and 80 healthy controls (HCs). For each patient, the T1 weighted image (T1WI) images were segmented into 106 subregions, and radiomic features were extracted from each subregion. Then, we analyzed the radiomic features of specific brain subregions that were most related to AD. Based on the selective radiomic features from specific brain subregions, we built an integrated model using the best machine learning algorithms, and the diagnostic accuracy was evaluated. The subregions most relevant to AD included the hippocampus, the inferior parietal lobe, the precuneus, and the lateral occipital gyrus. These subregions exhibited several important radiomic features that include shape, gray level size zone matrix (GLSZM), and gray level dependence matrix (GLDM), among others. Based on the comparison among different algorithms, we constructed the best model using the Logistic regression (LR) algorithm, which reached an accuracy of 0.962. Conclusively, we constructed an excellent model based on radiomic features from several specific AD-related subregions, which could give a potential biomarker for predicting AD.