BACKGROUND: Proliferative hepatocellular carcinomas (HCCs) is a class of aggressive tumors with poor prognosis. We aimed to construct a computed tomography (CT)-based radiomics nomogram to predict proliferative HCC, stratify clinical outcomes and explore the tumor microenvironment.
METHODS: Patients with pathologically diagnosed HCC following a hepatectomy were retrospectively collected from two medical centers. A CT-based radiomics nomogram incorporating radiomics model and clinicoradiological features to predict proliferative HCC was constructed using the training cohort (n = 184), and validated using an internal test cohort (n = 80) and an external test cohort (n = 89). The predictive performance of the nomogram for clinical outcomes was evaluated for HCC patients who underwent surgery (n = 201) or received transarterial chemoembolization (TACE, n = 104). RNA sequencing data and histological tissue slides from The Cancer Imaging Archive database were used to perform transcriptomics and pathomics analysis.
RESULTS: The areas under the receiver operating characteristic curve of the radiomics nomogram to predict proliferative HCC were 0.84, 0.87, and 0.85 in the training, internal test, and external test cohorts, respectively. The radiomics nomogram could stratify early recurrence-free survivals in the surgery outcome cohort (hazard ratio [HR] = 2.25; P < 0.001) and progression-free survivals in the TACE outcome cohort (HR = 2.21; P = 0.03). Transcriptomics and pathomics analysis indicated that the radiomics nomogram was associated with carbon metabolism, immune cells infiltration, TP53 mutation, and heterogeneity of tumor cells.
CONCLUSIONS: The CT-based radiomics nomogram could predict proliferative HCC, stratify clinical outcomes, and measure a pro-tumor microenvironment.

Lung function decline varies significantly in patients with lymphangioleiomyomatosis (LAM), impeding individualized clinical decision-making.
Can we aid individualized decision-making in LAM by developing a dynamic prediction model that can estimate the probability of clinically relevant FEV1 decline in patients with LAM before treatment initiation?
Patients observed in the US National Heart, Lung, and Blood Institute (NHLBI) Lymphangioleiomyomatosis Registry were included. Using routinely available variables such as age at diagnosis, menopausal status, and baseline lung function (FEV1 and diffusing capacity of the lungs for carbon monoxide [Dlco]), we used novel stochastic modeling and evaluated predictive probabilities for clinically relevant drops in FEV1. We formed predictive probabilities of transplant-free survival by jointly modeling longitudinal FEV1 and lung transplantation or death events. External validation used the UK Lymphangioleiomyomatosis Natural History cohort.
Analysis of the NHLBI Lymphangioleiomyomatosis Registry and UK Lymphangioleiomyomatosis Natural History cohorts consisted of 216 and 185 individuals, respectively. We derived a joint model that accurately estimated the risk of future lung function decline and 5-year probabilities of transplant-free survival in patients with LAM not taking sirolimus (area under the receiver operating characteristic curve [AUC], approximately 0.80). The prediction model provided estimates of forecasted FEV1, rate of FEV1 decline, and probabilities for risk of prolonged drops in FEV1 for untreated patients with LAM with a high degree of accuracy (AUC > 0.80) for the derivation cohort as well as the validation cohort. Our tool is freely accessible at: https://anushkapalipana.shinyapps.io/testapp_v2/.
Longitudinal modeling of routine clinical data can allow individualized LAM prognostication and assist in decision-making regarding the timing of treatment initiation.

UNASSIGNED: The incidence of nasopharyngeal cancer (NPC) is high, with new cases accounting for 5.2% of all malignancies in Indonesia. Most cases are detected at an advanced stage, and recurrences are common. Vascular endothelial growth factor (VEGF) and osteopontin (OPN) are important markers in tumorigenesis that serve as prognostic predictors. This study aims to determine the correlation of VEGF and OPN expression with 3-year progression-free survival (PFS).
UNASSIGNED: This retrospective cohort study analyzed 155 patients with locally advanced NPC. Data were obtained from medical records between 2015 and 2017. The locally advanced sample of this disease that met the inclusion criteria was stained with H&E before being prepared in a paraffin block. Furthermore, the immunohistochemistry staining results for VEGF and OPN were observed with ImageJ 1.50i and calculated semi-quantitatively using the histoscore.
UNASSIGNED: The 3-year PFS obtained was 39%, with a median of 23 months. Vascular endothelial growth factor expression was detected in 113 of 155 samples (72.9%), while positive OPN expression was discovered in 99 of 155 samples (63.8%). There was a correlation between VEGF (p = 0.747) and OPN expression (p = 0.584) and 3-year PFS. Positive VEGF and OPN expression in the subgroup of patients with stage IVB and N3 tumors was related to improved 3-year PFS (p < 0.05). This was similar to the positive VEGF expression in the subgroup of patients receiving neoadjuvant chemotherapy (p < 0.05).
UNASSIGNED: Vascular endothelial growth factor and OPN remained potential prognostic predictors in NPC. Patients with positive VEGF and OPN expression in N3, IVB, and neoadjuvant treatment had significantly improved 3-year PFS.

Root caries prevalence is increasing as populations age and retain more of their natural dentition. However, there is generally no accepted practice to identify individuals at risk of disease. There is a need for the development of a root caries prediction model to support clinicians to guide targeted prevention strategies. The aim of this study was to develop a prediction model for root caries in a population of regular dental attenders. Clinical and patient-reported predictors were collected at baseline by routine clinical examination and patient questionnaires. Clinical examinations were conducted at the 4-year timepoint by trained outcome assessors blind to baseline data to record root caries data at two thresholds - root caries present on any teeth (RC > 0) and root caries present on three or more teeth (RC ≥ 3). Multiple logistic regression analyses were performed with the number of participants with root caries at each outcome threshold utilized as the outcome and baseline predictors as the candidate predictors. An automatic backwards elimination process was conducted to select predictors for the final model at each threshold. The sensitivity, specificity, and c-statistic of each model\'s performance was assessed. A total of 1,432 patient participants were included within this prediction model, with 324 (22.6%) presenting with at least one root caries lesion, and 97 (6.8%) with lesions on three or more teeth. The final prediction model at the RC >0 threshold included increasing age, having ≥9 restored teeth at baseline, smoking, lack of knowledge of spitting toothpaste without rinsing following toothbrushing, decreasing dental anxiety, and worsening OHRQoL. The model sensitivity was 71.4%, specificity 69.5%, and c-statistic 0.79 (95% CI: 0.76, 0.81). The predictors included in the final prediction model at the RC ≥ 3 threshold included increasing age, smoking, and lack of knowledge of spitting toothpaste without rinsing following toothbrushing. The model sensitivity was 76.5%, specificity 73.6%, and c-statistic 0.81 (95% CI: 0.77, 0.86). To the authors\' knowledge, this is the largest published root caries prediction model, with statistics indicating good model fit and providing confidence in its robustness. The performance of the risk model indicates that adults at risk of developing root caries can be accurately identified, with superior performance in the identification of adults at risk of multiple lesions.

OBJECTIVE: The wide application of low-dose computed tomography (CT) has led to an increase in the detection of small lung cancer lesions. Moreover, surgical recommendations for second primary non-small cell lung cancer (NSCLC) lesions ≤ 2 cm are obscure. This study compares the efficacy of wedge resection, lobectomy, and segmentectomy for small second primary NSCLC lesions.
METHODS: The cohort was established based on the SEER database. Univariate and multivariate cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression, and restricted mean survival time (RMST) values were applied to identify prognostic factors. We used the Kaplan-Meier method to plot the survival curves of the different subgroups according to propensity score matching (PSM) analysis to then compare the therapeutic efficacy of the surgical procedures.
RESULTS: A total of 568 patients were enrolled in this study. Age, sex, grade, and lymph node ratio were selected as independent prognostic factors (p < 0.05). No significant differences were observed in survival probabilities among the groups of patients who underwent segmentectomy, wedge resection, or lobectomy (p > 0.05). We also established a nomogram model based on the four prognostic factors to guide clinical treatment.
CONCLUSIONS: Based on the findings of our study, segmentectomy was more appropriate than lobectomy for patients with a second primary NSCLC lesion ≤ 2 cm in diameter. The evidence to support other recommendations is insufficient.

The current gold standard for COVID-19 diagnosis, the rRT-PCR test, is hampered by long turnaround times, probable reagent shortages, high false-negative rates and high prices. As a result, machine learning (ML) methods have recently piqued interest, particularly when applied to digital imagery (X-rays and CT scans). In this review, the literature on ML-based diagnostic and prognostic studies grounded on hematochemical parameters has been considered. By doing so, a gap in the current literature was addressed concerning the application of machine learning to laboratory medicine. Sixty-eight articles have been included that were extracted from the Scopus and PubMed indexes. These studies were marked by a great deal of heterogeneity in terms of the examined laboratory test and clinical parameters, sample size, reference populations, ML algorithms, and validation approaches. The majority of research was found to be hampered by reporting and replicability issues: only four of the surveyed studies provided complete information on analytic procedures (units of measure, analyzing equipment), while 29 provided no information at all. Only 16 studies included independent external validation. In light of these findings, we discuss the importance of closer collaboration between data scientists and medical laboratory professionals in order to correctly characterise the relevant population, select the most appropriate statistical and analytical methods, ensure reproducibility, enable the proper interpretation of the results, and gain actual utility by using machine learning methods in clinical practice.

BACKGROUND: Previous researches have associated Helicobacter pylori (H. pylori) with a prognosis of gastric cancer (GC), however, without a concert conclusion. This study aimed to study this issue further by a prospective cohort study and a meta-analysis.
METHODS: Histologically diagnosed gastric cancer (GC) patients were recruited into the primary prospective cohort study between January 2009 to December 2013. All the patients were followed-up periodically to record information on post-surgery therapy and overall survival status. The pre-surgery status of H. pylori was measured by enzyme-linked immunosorbent assay. A meta-analysis was conducted after retrieving related researches in the databases of PubMed and Embase up to April 2020. Pooled hazard ratios (HRs) and 95% confidence intervals (CIs) were summarized to validate the relationship between H. pylori infection and the survival time of GC patients. I2 statistics and Q test were used to assess the heterogeneity. Sensitivity analyses were performed using Galbraith\'s plot, leave-one-out analysis, subgroup analyses and meta-regression to explore the sources of heterogeneity and the stability of the summary results.
RESULTS: A total of 743 GC patients with radical tumorectomy were included prospectively and 516 (69.4%) were positive on H. pylori. H. pylori-positive patients tended to survive longer than -negative ones (HR 0.92, 95%CI: 0.74-1.15), though the tendency was not statistically significant. Cohort studies on the prognosis of GC were retrieved comprehensively by assessing the full-text and 59 published studies, together with the result of our study, were included in the further meta-analysis. The summarized results related the positive status of H. pylori to better overall survival (HR 0.81, 95%CI: 0.72-0.90) and disease-free survival (HR 0.83, 95%CI: 0.67-0.99). Results from subgroup analyses indicated that the pooled magnitude of this association was relatively lower in studies not referring to H. pylori in title and abstract.
CONCLUSIONS: In conclusion, gastric cancer patients with H. pylori have a better prognosis than patients of H. pylori negative. More stringent surveillance strategies may be necessary for patients with H. pylori negative at cancer diagnosis.

OBJECTIVE: To calculate unstratified and patient-specific meaningful improvement (MI) and patient acceptable symptom states (PASS) for the WOMAC total score in patients after total hip (THR) or total knee replacement (TKR).
METHODS: A retrospective observational cohort study. Anchor-based receiver operator characteristics curves were used to estimate MI and PASS thresholds.
RESULTS: Recovery paths were specific to individual characteristics of patients. An unstratified 12-months MI threshold of 28.1 (PASS: 13.3) and 17.8 (PASS: 15.8) for patients after THR and TKR, respectively, would unfairly detect critical recovery paths.
CONCLUSIONS: Thresholds for treatment success need to be as patient-specific as possible.

The aim of this retrospective monocentric study was to report the outcomes following the correction of post enucleation socket syndrome (PESS) as well as the factors predicting for a multistep management. Patients were included, if they had received surgery to manage PESS. Demographics and patient history were collected. The symptoms of PESS were clinically rated by the same surgeon and ocularist, preoperatively and after each procedure. The surgery was considered as successful when no PESS clinical symptoms were observed. A prognostic study was performed to investigate the surgical outcomes. RESULTS: Thirty-six patients were included and eight of them had their PESS corrected after the first surgery. In the multivariate analysis (MVA), the deep upper lid sulcus symptom remained the main significant factor associated with an incomplete correction after the first surgery (OR 45.5, IC 95% (3.481-594.6), p = 0.004). For further corrections, the ptosis was the only significant prognostic factor requiring several surgeries (p = 0.005). At the end of the management, 94.4% of the patients had satisfying outcomes. CONCLUSIONS: The management of a PESS involves the correction of both the orbital cavity and the soft tissues. Although its correction is complex and time consuming in the majority of cases, the patients should be informed that the final cosmetic outcomes remain good at the expense of several surgeries.

OBJECTIVE: To develop and internally validate a preoperative clinical prediction model for acute adjacent vertebral fracture (AVF) after vertebral augmentation to support preoperative decision-making, named the after vertebral augmentation (AVA) score.
METHODS: In this prognostic study, a multicentre, retrospective single-level vertebral augmentation cohort of 377 patients from six Japanese hospitals was used to derive an AVF prediction model. Backward stepwise selection (p < 0.05) was used to select preoperative clinical and imaging predictors for acute AVF after vertebral augmentation for up to one month, from 14 predictors. We assigned a score to each selected variable based on the regression coefficient and developed the AVA scoring system. We evaluated sensitivity and specificity for each cut-off, area under the curve (AUC), and calibration as diagnostic performance. Internal validation was conducted using bootstrapping to correct the optimism.
RESULTS: Of the 377 patients used for model derivation, 58 (15%) had an acute AVF postoperatively. The following preoperative measures on multivariable analysis were summarized in the five-point AVA score: intravertebral instability (≥ 5 mm), focal kyphosis (≥ 10°), duration of symptoms (≥ 30 days), intravertebral cleft, and previous history of vertebral fracture. Internal validation showed a mean optimism of 0.019 with a corrected AUC of 0.77. A cut-off of ≤ one point was chosen to classify a low risk of AVF, for which only four of 137 patients (3%) had AVF with 92.5% sensitivity and 45.6% specificity. A cut-off of ≥ four points was chosen to classify a high risk of AVF, for which 22 of 38 (58%) had AVF with 41.5% sensitivity and 94.5% specificity.
CONCLUSIONS: In this study, the AVA score was found to be a simple preoperative method for the identification of patients at low and high risk of postoperative acute AVF. This model could be applied to individual patients and could aid in the decision-making before vertebral augmentation. Cite this article: Bone Joint J 2022;104-B(1):97-102.