Multi-task Adaptive Resolution Network for Lymph Node Metastasis Diagnosis from Whole Slide Images of Colorectal Cancer.

Automated detection of lymph node metastasis (LNM) holds great potential to alleviate the workload of doctors and reduce misinterpretations. Despite the practical successes achieved, effectively addressing the highly complex and heterogeneous tumor microenvironment remains an open and challenging problem, especially when tumor subtypes intermingle and are difficult to delineate. In this paper, we propose a multi-task adaptive resolution network, named MAR-Net, for LNM detection and subtyping in complex mixed-type cancers. Specifically, we construct a resolution-aware module to mine heterogeneous diagnostic information, which exploits the multi-scale pyramid information and adaptively combines multi-resolution structured features for comprehensive representation. Additionally, we adopt a multi-task learning approach that simultaneously addresses LNM detection and subtyping, reducing model instability during optimization and improving performance across both tasks. More importantly, to rectify the potential misclassification of tumor subtypes, we elaborately design a hierarchical subtying refinement (HSR) algorithm that leverages a generic segmentation model informed by pathologists' prior knowledge. Evaluations have been conducted on three private and one public cancer datasets (554 WSIs, 4.8 million patches). Our experimental results demonstrate that the proposed method consistently achieves superior performance compared to the state-of-the-art methods, achieving 0.5% to 3.2% higher AUC in LNM detection and 3.8% to 4.4% higher AUC in LNM subtyping.

Application of machine learning for predicting lymph node metastasis in T1 colorectal cancer: a systematic review and meta-analysis.

BACKGROUND: We review and analyze research on the application of machine learning (ML) and deep learning (DL) models to lymph node metastasis (LNM) prediction in patients with T1 colorectal cancer (CRC). Predicting LNM before radical surgery is important in patients with T1 CRC. However, current surgical treatment guidelines are limited. LNM prediction using ML or DL may improve predictive accuracy. The diagnostic accuracy of LNM prediction using ML- and DL-based models for patients with CRC was assessed. METHODS: We performed a comprehensive search of the PubMed, Embase, and Cochrane databases (inception to April 30th of 2022) for studies that applied ML or DL to LNM prediction in T1 CRC patients specifically to compare with histopathological findings and not related to radiological aspects. RESULTS: 33,199 T1 CRC patients enrolled across seven studies with a retrospective design were included. LNM was observed in 3,173 (9.6%) patients. Overall, the ML- and DL-based model exhibited a sensitivity of 0.944 and specificity of 0.877 for the prediction of LNM in patients with T1 CRC. Six different types of ML and DL models were used across the studies included in this meta-analysis. Therefore, a high degree of heterogeneity was observed. CONCLUSIONS: The ML and DL models provided high sensitivity and specificity for predicting LNM in patients with T1 CRC, and the heterogeneity between studies was significant. These results suggest the potential of ML or DL as diagnostic tools. However, more reliable algorithms should be developed for predicting LNM before surgery in patients with T1 CRC.

Synergistic prognostic impact of hemoglobin and skeletal muscle index in patients with colorectal cancer.

BACKGROUND & AIMS: Recent studies have indicated that comorbidities such as sarcopenia and anemia can influence the prognosis of patients with colorectal cancer (CRC). However, the synergistic effects of sarcopenia and anemia on the survival of CRC patients are not yet comprehensively understood. This study aimed to investigate the relationship between anemia and sarcopenia and their synergistic effect on survival in patients with CRC. METHODS: A total of 1629 patients who underwent colorectal surgery were retrospectively reviewed. Patients were categorized into four hemoglobin-sarcopenia combined classifications (HS grade) according to their hemoglobin and skeletal muscle index (SMI) levels: hemoglobin low/SMI low (HS1), hemoglobin low/SMI high (HS2), hemoglobin high/SMI low (HS3), and hemoglobin high/SMI high (HS4). Association with overall survival (OS) was analyzed using both univariable and multivariable analyses. RESULTS: In total, 1024 patients with stage I-III CRC were analyzed. Patient allocation according to HS grade was 124 (12.1%) in HS1, 298 (29.1%) in HS2, 135 (13.2%) in HS3, and 467 (45.6%) in HS4. The Kaplan-Meier curves of OS showed statistically significant differences according to anemia and sarcopenia status as well as to HS grade (all P < 0.001). Univariable analysis of factors associated with OS revealed statistical significance in absence of anemia (hazard ratio [HR] 0.550, 95% confidence interval [CI] 0.400-0.756, P < 0.001], absence of sarcopenia (HR 0.560, P < 0.001), and HS grade (HS2, HR 0.515, P = 0.002; HS3, HR 0.468, P = 0.006; HS4, HR 0.325, P < 0.001). Multivariable analysis showed that compared to the HS1 group, the HS2 and HS4 groups showed significantly better OS (HS2, HR 0.527, 95% CI 0.340-0.817, P = 0.004; HS4, HR 0.574, 95% CI 0.361-0.912, P = 0.018). CONCLUSIONS: Sarcopenia, characterized by a low SMI and the presence of anemia before surgery, was associated with reduced OS among patients with non-metastatic CRC.

Sarcopenia diagnosis in patients with colorectal cancer: a review of computed tomography-based assessments and emerging ways to enhance practicality.

Traditionally, cancer treatment has focused on the stages of the disease; however, recent studies have highlighted the importance of considering the overall health status of patients in the prognosis of cancer. Loss of skeletal muscle, known as sarcopenia, has been found to significantly affect outcomes in many different types of cancers, including colorectal cancer. In this review, we discuss the guidelines for diagnosing sarcopenia, with a specific focus on CT-based assessments. Many groups worldwide, including those in Europe and Asia, have introduced their own diagnostic guidelines for sarcopenia. Seemingly similar yet subtle discrepancies, particularly in the cutoff values used, limit the use of these guidelines in the general population, warranting a more universal guideline. Although CT-based measurements, such as skeletal muscle index and radiodensity, have shown promise in predicting outcomes, the lack of standardized values in these measurements hinders their universal adoption. To overcome these limitations, innovative approaches are being developed to assess changes in muscle mass trajectories and introduce new indices, such as skeletal and appendicular muscle gauges. Additionally, machine learning models have shown superior performance in predicting sarcopenic status, providing an alternative to CT-based diagnosis, particularly after surgery. CT has tremendous benefits and a significant role in visually as well as quantitatively retrieving information on patient body composition. In order to compensate for the limitation of standard cutoff value, 3-dimensional analysis of the CT, artificial intelligence-based body composition analysis, as well as machine learning algorithms for data interpretation and analysis have been proposed and are being utilized. In conclusion, despite the varying definitions of sarcopenia, CT-based measurements coupled with machine-learning models are promising for evaluating patients with cancer. Standardization efforts can improve diagnostic accuracy, reduce the reliance on CT examinations, and make sarcopenia assessments more accessible in clinical settings.

Autoimmunity and epithelial dysplasia in patients with oral lichenoid diseases.

OBJECTIVES: The primary objective of this study was to explore relationship between autoimmunity and epithelial dysplasia in patients with oral lichenoid diseases. MATERIALS AND METHODS: A total of 66 patients with oral lichen planus (OLP), 35 with oral lichenoid lesion (OLL), and 85 with oral lichenoid drug reaction (OLDR) were enrolled. OLP, OLL, and OLDR were diagnosed following the definitions of the modified World Health Organization criteria, except for the absence of epithelial dysplasia. All patients underwent diagnostic incisional biopsy and adjunctive direct immunofluorescence assays. An indirect immunofluorescence assay was conducted to determine the antinuclear antibody (ANA) positivity. RESULTS: OLP and OLDR patients with epithelial dysplasia demonstrated higher prevalence of serum ANA positivity compared to those without epithelial dysplasia. Elevated serum levels of high sensitivity-C reactive proteins were observed in the OLP, OLL, and OLDR patients with epithelial dysplasia. In the DIF analysis, patients with epithelial dysplasia in the OLP exhibited a higher prevalence of C3 deposition in the basement membrane zone. CONCLUSIONS: This study proposed that autoimmunity may contribute to elevating levels of focal and chronic systemic inflammation, potentially influencing abnormal wound healing and development of dysplastic changes in the oral epithelium among patients with oral lichenoid disease.

Varying clinical relevance of sarcopenia and myosteatosis according to age among patients with postoperative colorectal cancer.

OBJECTIVES: The present retrospective study reviewed the association among sarcopenia, myosteatosis, and overall survival (OS) in patients with postoperative colorectal cancer (CRC) with regard to age. DESIGN: A retrospective study was conducted with a five-year follow-up. SETTING: Data from all patients with CRC, who underwent surgery between February 2005 and April 2014, were reviewed. PARTICIPANTS: Data from 1053 patients (622 male [59.1%], 431 female [40.9%]; mean [± SD] age, 62.8 ± 11.8 years) were analyzed. MEASUREMENTS: Patients were divided into three groups according to age: ≤50, 51-74, and ≥75 years. Data, including perioperative parameters, and the presence of sarcopenia and myosteatosis according to skeletal muscle index (SMI) and skeletal muscle radiodensity (SMD), respectively, were collected. Sarcopenia was evaluated using CT by calculating the SMI at the L3 level by dividing the area of the skeletal muscle by height squared (cm2/m2). SMD was also calculated using CT at the L3 level, but by evaluating fat attenuation according to Hounsfield units (HU). RESULTS: Patient allocation according to age group was as follows: ≤50 years, n = 147 (14.0%); 51-74 years, n = 742 (70.5%); and ≥75 years, n = 164 (15.5%). The presence of sarcopenia and myosteatosis were statistically significant with increasing age (P = 0.004 and P < 0.001, respectively). The 51-74 years age group exhibited a significant association in OS for myosteatosis (P < 0.001) while the ≥75 years group was significantly associated with sarcopenia (P = 0.04) with regard to OS. Multivariable analysis also revealed a statistically significant association between myosteatosis in the 51-74 years age group (P = 0.033) and sarcopenia in the ≥75 years age group (P = 0.005) even when adjusted for recurrence status. CONCLUSION: Different age groups exhibited significantly variable skeletal muscle indices. Although an abundance of irrefutable results demonstrated a correlation between CT-defined sarcopenia, myosteatosis, and clinical prognosis, data regarding age-dependent correlations are scarce. Results of this study demonstrated that sarcopenia and myosteatosis did not influence the prognosis of young patients with postoperative CRC (≤50 years of age), inferring the existence of significantly different skeletal muscle-related parameters according to age. Patients over 75 years of age showed significant association with sarcopenia while those in the 51-74 age group displayed significant link to myosteatosis. Clinicians should consider the impact of sarcopenia and myosteatosis on patient prognosis and should also be aware that the effect may differ according to patient age.

Validation of the Korean Academy of Geriatric Dentistry screening questionnaire and oral frailty diagnostic criteria in community-dwelling older adults.

OBJECTIVES: This study aimed to establish the validity-specifically, the sensitivity and specificity-of the screening questionnaire and diagnostic criteria for oral frailty proposed by the Korean Academy of Geriatric Dentistry (KAGD) among community-dwelling older adults. METHODS: This study enrolled 100 participants. Among various definitions of oral frailty, this study used the criteria proposed by Tanaka as the reference test. The screening questionnaire consisted of 11 items for screening physical frailty, chewing ability, swallowing difficulties, oral dryness, and tongue and lip motor function. Each question had a different scoring weight, and if the total score was 1 or higher, an oral frailty diagnostic examination proposed by the KAGD would be recommended. The diagnostic test was the oral frailty diagnostic criteria proposed by the KAGD including 6 measures: chewing ability, occlusal force, tongue pressure, oral dryness, swallowing difficulty, and oral hygiene. If a participant exhibited 2 or more positive measures, this participant was classified as "oral frail." The screening questionnaire was analyzed using a cut-off value of 1 or higher, while the diagnostic criteria utilized a cut-off of 2 or more positive measures. Sensitivity and specificity were calculated. RESULTS: The screening questionnaire showed significant power for screening oral frailty (area under the receiver operating characteristic curve, 0.783; sensitivity, 87.8%; specificity, 52.5%). The diagnostic accuracy of the newly proposed diagnostic criteria was acceptable (sensitivity, 95.1%; specificity, 42.4%). CONCLUSIONS: The newly proposed screening questionnaire and diagnostic criteria in Korea appear to be a useful tool to identify oral frailty in community-dwelling older adults.

Intracorporeal versus extracorporeal anastomosis in minimally invasive right hemicolectomy: systematic review and meta-analysis of randomized controlled trials.

Purpose: Compared with extracorporeal anastomosis (ECA), intracorporeal anastomosis (ICA) is expected to provide some benefits, including a shorter operation time and less intraoperative bleeding. Nevertheless, the benefits of ICA have mainly been evaluated in nonrandomized studies. Owing to the recent update of randomized controlled trials (RCTs) for minimally invasive surgery (MIS) of right hemicolectomy (RHC), the need to measure the actual effect by synthesizing the outcomes of these studies has emerged. Methods: We performed a comprehensive search of the PubMed, Embase, and Cochrane databases (from inception to January 30, 2023) for studies that applied ICA and ECA for RHC with MIS. We included 7 RCTs. The operation time, intraoperative blood loss, conversion rate, length of incision, and postoperative outcomes such as ileus, anastomosis leakage, length of hospitalization, and postoperative pain were compared between ICA and ECA. Results: A total of 740 patients were included in the study. Among them, 377 and 373 underwent ICA and ECA, respectively. There were significant differences in age (P = 0.003) and incision type (P < 0.001) between ICA and ECA. ICA was associated with a significantly longer operation time (P = 0.033). Although the postoperative pain associated with ICA was significantly lower than that associated with ECA on postoperative day 2 (POD 2) (P = 0.003), it was not different on POD 3 between the groups. Other perioperative outcomes were similar between the 2 groups. Conclusion: In this meta-analysis, ICA did not significantly improve short-term outcomes compared to ECA; other advantages to overcome ICA's longer operation time are not clear.

Transcriptional Profiling of Muscle in Females With Distal Radius Fracture and Functional Sarcopenia.

Skeletal muscle and bone interact with each other in mechanical and biochemical ways. This study aimed to investigate the molecular mechanisms of interaction between muscle and bone by analyzing the transcriptional profiles of total RNA from the muscle tissue of females with distal radius fractures. A total of 30 female participants (mean age 71.1 ±â€…8.9 years) with distal radius fractures were recruited. Participants were categorized into 2 groups: the NORM group consisted of participants with T score of the areal bone mineral density (aBMD) of the femoral neck higher than -1.0, handgrip strength greater than 18 kg, and gait speed faster than 1.0 m/s (n = 10). Otherwise, participants with T score of the aBMD of the femoral neck equal to or less than -1.0, handgrip strength lower than 18 kg, and gait speed slower than 1.0 m/s (n = 20) were categorized into EXP group. Pronator quadratus muscle samples were obtained from all participants. Total RNA was extracted from frozen muscle samples and sequenced. The gene ontology analysis demonstrated that the potential interactions between attached muscle function and the density of the associated bone would be linked with collagen biosynthetic activity and maintenance of extracellular matrix structures. The analysis of the pathway, network, and protein class exhibited that integrin signaling, inflammatory reactions, matrix metalloproteinase (MMP) activity, and extracellular matrix protein structure had possible associations with the molecular background of muscle-bone interaction. Through integrin signaling, MMP activity, inflammatory reactions, and collagen biosynthesis, muscle and bone may mutually interact with one another.

Complex interplay of oral health, muscle and bone metabolism, and frailty in older individuals.

OBJECTIVES: To investigate molecular and clinical background of associations among oral health, muscle and bone metabolism, and frailty incidence in patients with fall and fracture history. MATERIALS AND METHODS: In total, 88 elderly participants (mean age 71.9 ± 5.8 years) with the distal radius fractures were included. Participants were divided into three groups based on an Oral Health Assessment Tool score. Fried criteria and Mini-nutritional assessments were adopted to diagnose frailty and malnutrition, respectively. Blood samples were collected and analyzed for serum levels of bone turnover markers, proteins, insulin-like growth factor-1, 25-hydroxyvitamin D, and inflammatory cytokines. The mRNA levels of markers of inflammation, muscle synthesis and wasting, and muscle homeostasis regulator in the pronator quadratus muscle were analyzed. RESULTS: Patients with deteriorated oral health demonstrated a higher prevalence of frailty and malnutrition. Significantly lower serum levels of total protein and higher concentrations of tumor necrosis factor-α (TNF-α) and interleukin-1ß (IL-1ß) were detected in patients with poor oral health. Significant interaction effects between oral health and frailty level in gait speed, serum TNF-α, IL-1ß, and total protein levels were exhibited. Significantly different mRNA expression levels in the pronator quadratus muscle of TNF-α, IL-1ß, NF kB, MYOG, and FOXO1 following the oral health were detected. CONCLUSION: This study highlights relationship between oral health, nutritional uptake, systemic inflammation, and their combined impact on muscle and bone metabolism, ultimately affecting frailty development in the aging populations. CLINICAL RELEVANCE: A comprehensive understanding of mutual interactions among oral health, nutrition, and inflammation is essential for managing frailty.

Vision Transformers for Computational Histopathology.

Computational histopathology is focused on the automatic analysis of rich phenotypic information contained in gigabyte whole slide images, aiming at providing cancer patients with more accurate diagnosis, prognosis, and treatment recommendations. Nowadays deep learning is the mainstream methodological choice in computational histopathology. Transformer, as the latest technological advance in deep learning, learns feature representations and global dependencies based on self-attention mechanisms, which is increasingly gaining prevalence in this field. This article presents a comprehensive review of state-of-the-art vision transformers that have been explored in histopathological image analysis for classification, segmentation, and survival risk regression applications. We first overview preliminary concepts and components built into vision transformers. Various recent applications including whole slide image classification, histological tissue component segmentation, and survival outcome prediction with tailored transformer architectures are then discussed. We finally discuss key challenges revolving around the use of vision transformers and envisioned future perspectives. We hope that this review could provide an elaborate guideline for readers to explore vision transformers in computational histopathology, such that more advanced techniques assisting in the precise diagnosis and treatment of cancer patients could be developed.

Effects of Zinc Compounds on Lysozyme, Peroxidase, and α-Amylase from the Perspective of Oral Health: a Scoping Review.

Zinc has been proposed as a topical therapeutic agent for the prevention and treatment of various oral diseases. The purpose of this scoping review was to investigate the effects of zinc on the enzymatic activities of lysozyme, peroxidase, and α-amylase from the perspective of developing oral health care products and therapeutic agents for oral diseases. A comprehensive review of the scientific literature was conducted on the direct interactions of zinc with lysozyme, peroxidase, and α-amylase from various sources. Most of the reports on the effects of zinc on the enzymatic activities of lysozyme, peroxidase, and α-amylase involved enzymes derived from bacteria, fungi, animals, and plants. Studies of human salivary enzymes were scarce. Zinc was found to inhibit the enzymatic activities of lysozyme, peroxidase, and α-amylase under diverse experimental conditions. The suggested mechanism was ionic interactions between zinc and enzyme molecules. The possibility that zinc causes structural changes to enzyme molecules has also been suggested. In conclusion, for zinc to be used as an effective topical therapeutic agent for oral health, further studies on the activity of human salivary enzymes are warranted, and additional information regarding the type and concentration of effective zinc compounds is also required.

Combined impact of myosteatosis and liver steatosis on prognosis in stage I-III colorectal cancer patients.

BACKGROUND: Myosteatosis and liver steatosis (LS) have been recognized as patient-derived image biomarkers that correlate with prognosis in colorectal cancer (CRC) patients. However, the significance of considering fat deposition in multiple body areas simultaneously has been underestimated. This study aimed to investigate the combined effect of myosteatosis and LS in stage I-III CRC patients. METHODS: A total of 616 stage I-III CRC patients were included in the study. Myosteatosis was assessed using skeletal muscle radiodensity (SMD), and LS was estimated by calculating the Hounsfield unit of the liver and spleen ratio (LSR). Cox proportional hazard models were utilized to evaluate disease-free survival (DFS). A combination of myosteatosis and LS was proposed, and its discriminatory performance was compared using the C-index. RESULTS: Among the 616 participants, the median (interquartile) age was 64 (55-72) years, and 240 (38.9%) were female. The median and interquartile range of LSR were determined as 1.106 (0.967-1.225). The optimal cutoff value for LSR was identified as 1.181, leading to the classification of patients into low (410, 66.5%) and high LSR (206, 33.4%) groups. Among the patients, 200 were categorized into the low SMD group, while 416 were allocated to the high SMD group. Both myosteatosis and LS were identified as independent prognostic factors in the multivariable analysis. The combination of these two variables resulted in a three-group classification: high SMD with low LSR group, high SMD with high LSR group, and low SMD group. When comparing the C-index values, the three-group classification exhibited superior discriminatory performance compared with considering myosteatosis and LS separately. CONCLUSIONS: Myosteatosis was associated with poorer survival, while the presence of LS was linked to a better prognosis in non-metastatic CRC patients. Simultaneously considering fat infiltration can serve as a more effective prognosticator in non-metastatic CRC patients.

Machine-Learning Algorithms Using Systemic Inflammatory Markers to Predict the Oncologic Outcomes of Colorectal Cancer After Surgery.

BACKGROUND: This study aimed to investigate the clinical significance of machine-learning (ML) algorithms based on serum inflammatory markers to predict survival outcomes for patients with colorectal cancer (CRC). METHODS: The study included 941 patients with stages I to III CRC. Based on random forest algorithms using 15 compositions of inflammatory markers, four different prediction scores (DFS score-1, DFS score-2, DFS score-3, and DFS score-4) were developed for the Yonsei cohort (training set, n = 803) and tested in the Ulsan cohort (test set, n = 138). The Cox proportional hazards model was used to determine correlation between prediction scores and disease-free survival (DFS). Harrell's concordance index (C-index) was used to compare the predictive ability of prediction scores for each composition. RESULTS: The multivariable analysis showed the DFS score-4 to be an independent prognostic factor after adjustment for clinicopathologic factors in both the training and test sets (hazard ratio [HR], 8.98; 95% confidence interval [CI] 6.7-12.04; P < 0.001 for the training set and HR, 2.55; 95% CI 1.1-5.89; P = 0.028 for the test set]. With regard to DFS, the highest C-index among single compositions was observed in the lymphocyte-to-C-reactive protein ratio (LCR) (0.659; 95% CI 0.656-0.662), and the C-index of DFS score-4 (0.727; 95% CI 0.724-0.729) was significantly higher than that of LCR in the test set. The C-index of DFS score-3 (0.725; 95% CI 0.723-0.728) was similar to that of DFS score-4, but higher than that of DFS score-2 (0.680; 95% CI 0.676-0.683). CONCLUSIONS: The ML-based approaches showed prognostic utility in predicting DFS. They could enhance clinical use of inflammatory markers in patients with CRC.

Skeletal muscle gauge prediction by a machine learning model in patients with colorectal cancer.

OBJECTIVES: Skeletal muscle gauge (SMG) was recently introduced as an imaging indicator of sarcopenia. Computed tomography is essential for measuring SMG; thus, the use of SMG is limited to patients who undergo computed tomography. We aimed to develop a machine learning algorithm using clinical and inflammatory markers to predict SMG in patients with colorectal cancer. METHODS: The least absolute shrinkage and selection operator regression model was applied for variable selection and predictive signature building in the training set. The predictive accuracy of the least absolute shrinkage and selection operator model, defined as linear predictor (LP)-SMG, was compared using the area under the receiver operating characteristic curve and decision curve analysis in the test set. RESULTS: A total of 1094 patients with colorectal cancer were enrolled and randomly categorized into training (n = 656) and test (n = 438) sets. Low SMG was identified in 142 (21.6%) and 90 (20.5%) patients in the training and test sets, respectively. According to multivariable analysis of the test sets, LP-SMG was identified as an independent predictor of low SMG (odds ratio = 1329.431; 95% CI, 271.684-7667.996; P < .001). Its predictive performance was similar in the training and test sets (area under the receiver operating characteristic curve = 0.846 versus 0.869; P = .427). In the test set, LP-SMG had better outcomes in predicting SMG than single clinical variables, such as sex, height, weight, and hemoglobin. CONCLUSIONS: LP-SMG had superior performance than single variables in predicting low SMG. This machine learning model can be used as a screening tool to detect sarcopenic status without using computed tomography during the treatment period.

Prediction of Microsatellite Instability in Colorectal Cancer Using a Machine Learning Model Based on PET/CT Radiomics.

PURPOSE: We investigated the feasibility of preoperative 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET)/computed tomography (CT) radiomics with machine learning to predict microsatellite instability (MSI) status in colorectal cancer (CRC) patients. MATERIALS AND METHODS: Altogether, 233 patients with CRC who underwent preoperative FDG PET/CT were enrolled and divided into training (n=139) and test (n=94) sets. A PET-based radiomics signature (rad_score) was established to predict the MSI status in patients with CRC. The predictive ability of the rad_score was evaluated using the area under the receiver operating characteristic curve (AUROC) in the test set. A logistic regression model was used to determine whether the rad_score was an independent predictor of MSI status in CRC. The predictive performance of rad_score was compared with conventional PET parameters. RESULTS: The incidence of MSI-high was 15 (10.8%) and 10 (10.6%) in the training and test sets, respectively. The rad_score was constructed based on the two radiomic features and showed similar AUROC values for predicting MSI status in the training and test sets (0.815 and 0.867, respectively; p=0.490). Logistic regression analysis revealed that the rad_score was an independent predictor of MSI status in the training set. The rad_score performed better than metabolic tumor volume when assessed using the AUROC (0.867 vs. 0.794, p=0.015). CONCLUSION: Our predictive model incorporating PET radiomic features successfully identified the MSI status of CRC, and it also showed better performance than the conventional PET image parameters.

Clinical Significance of Combining Preoperative and Postoperative Albumin-Bilirubin Score in Colorectal Cancer.

PURPOSE: Albumin-bilirubin (ALBI) score is a well-known prognostic factor for various diseases, including colorectal cancer (CRC). However, little is known about the significance of postoperative ALBI score changes in patients with CRC. MATERIALS AND METHODS: A total of 723 patients who underwent surgery were enrolled. Preoperative ALBI (ALBI-pre) and postoperative ALBI (ALBI-post) scores were divided into low and high score groups. ALBI-trend was defined as a combination of four groups comprising the low and high ALBI-pre and ALBI-post score groups. Kaplan-Meier survival curves were used to compare the overall survival (OS) between the different ALBI groups. The Cox proportional hazards model was used to examine the independent relevant factors of OS. Stratification performance was compared between the different ALBI groupings using Harrell's concordance index (C-index). RESULTS: ALBI-pre, ALBI-post, and ALBI-trend score groups were significant prognostic factors of OS in the univariable analysis. However, multivariable analysis showed that ALBI-trend was an independent prognostic factor while ALBI-pre and ALBI-post were not. The C-index of ALBI-trend (0.622; 95% confidence interval [CI], 0.587 to 0.655) was higher than that of ALBI-pre (0.589; 95% CI, 0.557 to 0.621; bootstrap mean difference, 0.033; 95% CI, 0.013 to 0.057) and ALBI-post (0.575; 95% CI, 0.545 to 0.605; bootstrap mean difference, 0.047; 95% CI, 0.024 to 0.074). CONCLUSION: Combining ALBI-pre and ALBI-post scores is an independent prognostic factor of OS and shows superior predictive power compared to ALBI-pre or ALBI-post alone in patients with CRC.

Quality and readability of online information on hand osteoarthritis.

Objectives: This study aimed to evaluate the quality and readability of web pages providing information about hand osteoarthritis using several authorized methods.Methods: A web page exploration was performed using the Google internet search engine. The three search terms, "hand osteoarthritis," "finger osteoarthritis," and "hand OA," were used and the top 100 ranked websites were selected and divided into six categories. The Health on the Net Foundation (HON) grade scale, an instrument for judging the quality of written consumer health information on treatment choice (DISCERN instrument), and the Ensuring Quality Information for Patients (EQIP) score were used to evaluate the quality of each website. The Flesch-Kincaid reading ease (FRE) score, Flesch-Kincaid grade (FKG) level, Gunning-Fog index, and Simple Measure of Gobbledygook grade level were used to evaluate website readability.Results: Among 300 websites, 57 websites were selected following exclusion criteria. News portal websites, including the online version of newspapers and periodicals, showed the highest score in all three quality evaluation tools. Only four websites were regarded as high-quality websites based on the HON grade scale (n = 3) and the EQIP score (n = 1). Each type of website showed an average FKG level higher than 7th grade and obtained an average FRE score of less than 80 points, indicating an inappropriate level for a layperson to read.Conclusions: The online information about hand osteoarthritis is low quality and difficult to read for the general public. There is a need to enhance the quality and readability of web-based information related to hand osteoarthritis for patients to obtain credible information and receive proper treatment for the disease.