Prediction of knee pain improvement over two years for knee osteoarthritis using a dynamic nomogram based on MRI-derived radiomics: a proof-of-concept study

Predicting rapid kidney function decline in middle-aged and elderly Chinese adults using machine learning techniques

The rapid decline of kidney function in middle-aged and elderly people has become an increasingly serious public health problem. Machine learning (ML) technology has substantial potential to disease prediction. The present study use dataset from the Chinese Health and Retirement Longitudinal Study (CHARLS) and utilizes advanced Gradient Boosting algorithms to develop predictive models. Least Absolute Shrinkage and Selection Operator (LASSO) regression was used to identify the key predictors, and multivariate logistic regression was utilized to validate the independent predictive power of the variables. Furthermore, the study integrated SHapley Additive exPlanations (SHAP) to boost the interpretability of the model. The findings show that the Gradient Boosting Model demonstrated robust performance across both the training and test datasets. Specifically, it attained AUC values of 0.8 and 0.765 in the training and test sets, respectively, while achieving accuracy scores of 0.736 and 0.728 in these two datasets. LASSO regression identified key influencing factors, including estimated glomerular filtration rate (eGFR), age, hemoglobin (Hb), glucose, and systolic blood pressure (SBP). Multivariate linear regression further confirmed the independent associations between these variables and rapid kidney function deterioration (P < 0.05). This study developed a risk assessment model for rapid kidney function deterioration that is applicable to middle-aged and elderly populations in China.

Integrating radiomics into predictive models for low nuclear grade DCIS using machine learning

Predicting low nuclear grade DCIS before surgery can improve treatment choices and patient care, thereby reducing unnecessary treatment. Due to the high heterogeneity of DCIS and the limitations of biopsies in fully characterizing tumors, current diagnostic methods relying on invasive biopsies face challenges. Here, we developed an ensemble machine learning model to assist in the preoperative diagnosis of low nuclear grade DCIS. We integrated preoperative clinical data, ultrasound images, mammography images, and Radiomic scores from 241 DCIS cases. The ensemble model, based on Elastic Net, Generalized Linear Models with Boosting (glmboost), and Ranger, improved the ability to predict low nuclear grade DCIS preoperatively, achieving an AUC of 0.92 on the validation set, outperforming the model using clinical data alone. The comprehensive model also demonstrated notable enhancements in integrated discrimination improvement and net reclassification improvement (p < 0.001). Furthermore, the Radiomic ensemble model effectively stratified DCIS patients by risk based on disease-free survival. Our findings emphasize the importance of integrating Radiomic into DCIS prediction models, offering fresh perspectives for personalized treatment and clinical management of DCIS.

Progress in multi-omics studies of osteoarthritis

Osteoarthritis (OA), a ubiquitous degenerative joint disorder, is marked by pain and disability, profoundly impacting patients' quality of life. As the population ages, the global prevalence of OA is escalating. Omics technologies have become instrumental in investigating complex diseases like OA, offering comprehensive insights into its pathogenesis and progression by uncovering disease-specific alterations across genomics, transcriptomics, proteomics, and metabolomics levels. In this review, we systematically analyzed and summarized the application and recent achievements of omics technologies in OA research by scouring relevant literature in databases such as PubMed. These studies have shed light on new potential therapeutic targets and biomarkers, charting fresh avenues for OA diagnosis and treatment. Furthermore, in our discussion, we highlighted the immense potential of spatial omics technologies in unraveling the molecular mechanisms of OA and in the development of novel therapeutic strategies, proposing future research directions and challenges. Collectively, this study encapsulates the pivotal advances in current OA research and prospects for future investigation, providing invaluable references for a deeper understanding and treatment of OA. This review aims to synthesize the recent progress of omics technologies in the realm of OA, aspiring to furnish theoretical foundations and research orientations for more profound studies of OA in the future.

Computed tomography radiomics in predicting patient satisfaction after robotic-assisted total knee arthroplasty

PURPOSE

After robotic-assisted total knee arthroplasty (RA-TKA) surgery, some patients still experience joint discomfort. We aimed to establish an effective machine learning model that integrates radiomic features extracted from computed tomography (CT) scans and relevant clinical information to predict patient satisfaction three months postoperatively following RA-TKA.

MATERIALS AND METHODS

After careful selection, data from 142 patients were randomly divided into a training set (n = 99) and a test set (n = 43), approximately in a 7:3 ratio. A total of 1329 radiomic features were extracted from the regions of interest delineated in CT scans. The features were standardized using normalization algorithms, and the least absolute shrinkage and selection operator regression model was employed to select radiomic features with ICC > 0.75 and P < 0.05, generating the Rad-score as feature markers. Univariate and multivariate logistic regression was then used to screen clinical information (age, body mass index, operation time, gender, surgical side, comorbidities, preoperative KSS score, preoperative range of motion (ROM), preoperative and postoperative HKA angle, preoperative and postoperative VAS score) as potential predictive factors. The satisfaction scale ≥ 20 indicates patient satisfaction. Finally, three prediction models were established, focusing on radiomic features, clinical features, and their fusion. Model performance was evaluated using Receiver Operating Characteristic curves and decision curve analysis.

RESULTS

In the training set, the area under the curve (AUC) of the clinical model was 0.793 (95% CI 0.681-0.906), the radiomic model was 0.854 (95% CI 0.743-0.964), and the combined radiomic-clinical model was 0.899 (95% CI 0.804-0.995). In the test set, the AUC of the clinical model was 0.908 (95% CI 0.814-1.000), the radiomic model was 0.709 (95% CI 0.541-0.878), and the combined radiomic-clinical model was 0.928 (95% CI 0.842-1.000). The AUC of the radiomic-clinical model was significantly higher than the other two models. The decision curve analysis indicated its clinical application value.

CONCLUSION

We developed a radiomic-based nomogram model using CT imaging to predict the satisfaction of RA-TKA patients at 3 months postoperatively. This model integrated clinical and radiomic features and demonstrated good predictive performance and excellent clinical application potential.

Interpretable machine learning and radiomics in hip MRI diagnostics: comparing ONFH and OA predictions to experts

Purpose

Distinguishing between Osteonecrosis of the femoral head (ONFH) and Osteoarthritis (OA) can be subjective and vary between users with different backgrounds and expertise. This study aimed to construct and evaluate several Radiomics-based machine learning models using MRI to differentiate between those two disorders and compare their efficacies to those of medical experts.

Methods

140 MRI scans were retrospectively collected from the electronic medical records. They were split into training and testing sets in a 7:3 ratio. Handcrafted radiomics features were harvested following the careful manual segmentation of the regions of interest (ROI). After thoroughly selecting these features, various machine learning models have been constructed. The evaluation was carried out using receiver operating characteristic (ROC) curves. Then NaiveBayes (NB) was selected to establish our final Radiomics-model as it performed the best. Three users with different expertise and backgrounds diagnosed and labeled the dataset into either OA or ONFH. Their results have been compared to our Radiomics-model.

Results

The amount of handcrafted radiomics features was 1197 before processing; after the final selection, only 12 key features were retained and used. User 1 had an AUC of 0.632 (95% CI 0.4801-0.7843), User 2 recorded an AUC of 0.565 (95% CI 0.4102-0.7196); while User 3 was on top with an AUC of 0.880 (95% CI 0.7753-0.9843). On the other hand, the Radiomics model attained an AUC of 0.971 (95% CI 0.9298-1.0000); showing greater efficacy than all other users. It also demonstrated a sensitivity of 0.937 and a specificity of 0.885. DCA (Decision Curve Analysis displayed that the radiomics-model had a greater clinical benefit in differentiating OA and ONFH.

Conclusion

We have successfully constructed and evaluated an interpretable radiomics-based machine learning model that could distinguish between OA and ONFH. This method has the ability to aid both junior and senior medical professionals to precisely diagnose and take prompt treatment measures.

Osteoarthritis year in review 2024: Imaging

OBJECTIVE

To review recent literature evidence describing imaging of osteoarthritis (OA) and to identify the current trends in research on OA imaging.

METHOD

This is a narrative review of publications in English, published between April, 2023, and March, 2024. A Pubmed search was conducted using the following search terms: osteoarthritis/OA, radiography, ultrasound/US, computed tomography/CT, magnetic resonance imaging/MRI, DXA/DEXA, and artificial intelligence/AI/deep learning. Most publications focus on OA imaging in the knee and hip. Imaging of OA in other joints and OA imaging with artificial intelligence (AI) are also reviewed.

RESULTS

Compared to the same period last year (April 2022 - March 2023), there has been no significant change in the number of publications utilizing CT, MRI, and AI. A notable reduction in the number of OA research papers using radiography and ultrasound is noted. There were several observational studies focusing on imaging of knee OA, such as the Multicenter Osteoarthritis Study, Rotterdam Study, Strontium ranelate efficacy in knee OA (SEKOIA) study, and the Osteoarthritis Initiative FNIH Biomarker study. Hip OA observational studies included, but not limited to, Cohort Hip and Cohort Knee study and UK Biobank study. Studies on emerging applications of AI in OA imaging were also covered. A small number of OA clinical trials were published with a focus on imaging-based outcomes.

CONCLUSION

MRI-based OA imaging research continues to play an important role compared to other modalities. Usage of various AI tools as an adjunct to human assessment is increasingly applied in OA imaging research.

Magnetic resonance imaging-based biomarkers for knee osteoarthritis outcomes: A narrative review of prediction but not association studies

BACKGROUND

Magnetic Resonance Imaging (MRI) is frequently used in recent studies on knee osteoarthritis (KOA), focusing on developing innovative MRI-based biomarkers to predict KOA outcomes. The growing volume of publications devoted to this subject highlights the need for an up-to-date review.

METHODS

In this narrative review, we utilized the PubMed database to identify studies examining MRI-based biomarkers for the prediction of knee osteoarthritis (KOA), focusing on those reporting relevant prediction, not association, metrics. The identified articles were subsequently categorized into three distinct outcomes: Prediction of KOA incidence (KOAi), KOA progression (KOAp) and total knee arthroplasty risk (TKAr). Within each category, results were organized by the nature of biomarker(s) used, as either quantitative, semi-quantitative or compound.

RESULTS

Due to the lack of predictive metrics such as the area under the ROC curve (AUC) scores, sensitivity or specificity, 27 studies were excluded. A final set of 23 studies were deemed eligible for our analysis. The mean AUC scores reported ranged from 0.67 to 0.83 for predicting KOAi, 0.54 to 0.84 for KOAp and 0.55 to 0.94 for TKAr. Excellent predictive performance (AUC>0.8) was observed for the prediction of radiographic KOAi, KOAp and TKAr when using cartilage and meniscal-based measures, osteophyte scores and infrapatellar fat pad texture, and bone marrow lesions, respectively.

CONCLUSION

The results showed that numerous studies highlighted the importance of MRI-based biomarkers as promising predictors of the three key outcomes. In addition, this narrative review also emphasized the necessity for KOA prediction studies to include adequate reporting of predictive metrics.

Interconnected Pathways: Exploring Inflammation, Pain, and Cognitive Decline in Osteoarthritis

The relationship among inflammation, pain, and cognitive decline in osteoarthritis (OA) patients is complex and has not been sufficiently explored; therefore, we undertook this research to evaluate how OA-related inflammation and pain affect cognitive functions, as well as to examine the potential of urinary markers as indicators of these conditions. This study examined fifty OA patients through clinical and cognitive assessments, morphological analyses, urinary biomarkers, and bioinformatics. Morphologically, 24% of patients had moderate to high synovial inflammation, which was significantly correlated with depressive symptoms, pain intensity, and self-reported anxiety. The Montreal Cognitive Assessment indicated minimal decline in most patients but showed negative correlations with age and inflammation severity. Urinary TNF-α and TGF-β1 levels positively correlated with body mass index and pain and synovitis score and immune cell infiltration, respectively. In contrast, cartilage oligomeric matrix protein and C-telopeptides of type II collagen showed inverse correlations with pain duration and cognitive function, respectively. Distinct patient clusters with higher inflammation were identified and were associated with reported pain and depressive symptoms. Urinary TNF-α and TGF-β1 can serve as biomarkers reflecting inflammation and disease severity in OA. This study suggests that synovial inflammation may be linked to mental and cognitive health in some patient cohorts.

The use of radiomic analysis of magnetic resonance imaging findings in predicting features of early osteoarthritis of the knee-a systematic review and meta-analysis

The primary aim of this study was to systematically review current literature evaluating the use of radiomics in establishing the role of magnetic resonance imaging (MRI) findings in native knees in predicting features of osteoarthritis (OA). A systematic review was performed with respect to PRISMA guidelines in search of studies reporting radiomic analysis of magnetic resonance imaging (MRI) to analyse patients with native knee OA. Sensitivity and specificity of radiomic analyses were included for meta-analysis. Following our initial literature search of 1271 studies, only 5 studies met our inclusion criteria. This included 1730 patients (71.5% females) with a mean age of 55.4 ± 15.6 years (range 24-66). The mean RQS of included studies was 16.6 (11-21). Meta-analysis demonstrated the pooled sensitivity and specificity for MRI in predicting features of OA in patients with native knees were 0.74 (95% CI 0.71, 0.78) and 0.85 (95% CI 0.83, 0.87), respectively. The results of this systematic review suggest that the high sensitivities and specificity of MRI-based radiomics may represent potential biomarker in the early identification and classification of native knee OA. Such analysis may inform surgeons to facilitate earlier non-operative management of knee OA in the select pre-symptomatic patients, prior to clinical or radiological evidence of degenerative change.

A Retrospective Study of Biological Risk Factors Associated with Primary Knee Osteoarthritis and the Development of a Nomogram Model

Aim

A high percentage of the elderly suffer from knee osteoarthritis (KOA), which imposes a certain economic burden on them and on society as a whole. The purpose of this study is to examine the risk of KOA and to develop a KOA nomogram model that can timely intervene in this disease to decrease patient psychological burdens.

Methods

Data was collected from patients with KOA and without KOA at our hospital from February 2021 to February 2023. Initially, a comparison was conducted between the variables, identifying statistical differences between the two groups. Subsequently, the risk of KOA was evaluated using the Least Absolute Shrinkage and Selection Operator method and multivariate logistic regression to determine the most effective predictive index and develop a prediction model. The examination of the disease risk prediction model in KOA includes the corresponding nomogram, which encompasses various potential predictors. The assessment of disease risk entails the application of various metrics, including the consistency index (C index), the area under the curve (AUC) of the receiver operating characteristic curve, the calibration chart, the GiViTi calibration band, and the model for predicting KOA. Furthermore, the potential clinical significance of the model is explored through decision curve analysis (DCA) and clinical influence curve analysis.

Results

The study included a total of 582 patients, consisting of 392 patients with KOA and 190 patients without KOA. The nomogram utilized age, haematocrit, platelet count, apolipoprotein a1, potassium, magnesium, hydroxybutyrate dehydrogenase, creatine kinase, and estimated glomerular filtration rate as predictors. The C index, AUC, calibration plot, Giviti calibration band, DCA and clinical influence KOA indicated the ability of nomogram model to differentiate KOA.

Conclusion

Using nomogram based on disease risk, high-risk KOA can be identified directly without imaging.

Applications of Artificial Intelligence in Pain Medicine

PURPOSE OF REVIEW

This review explores the current applications of artificial intelligence (AI) in the field of pain medicine with a focus on machine learning.

RECENT FINDINGS

Utilizing a literature search conducted through the PubMed database, several current trends were identified, including the use of AI as a tool for diagnostics, predicting pain progression, predicting treatment response, and performance of therapy and pain management. Results of these studies show promise for the improvement of patient outcomes. Current gaps in the research and subsequent directions for future study involve AI in optimizing and improving nerve stimulation and more thoroughly predicting patients' responses to treatment.

Radiomics signature of osteoarthritis: Current status and perspective

Osteoarthritis (OA) is one of the fast-growing disability-related diseases worldwide, which has significantly affected the quality of patients' lives and brings about substantial socioeconomic burdens in medical expenditure. There is currently no cure for OA once the bone damage is established. Unfortunately, the existing radiological examination is limited to grading the disease's severity and is insufficient to precisely diagnose OA, detect early OA or predict OA progression. Therefore, there is a pressing need to develop novel approaches in medical image analysis to detect subtle changes for identifying early OA development and rapid progressors. Recently, radiomics has emerged as a unique approach to extracting high-dimensional imaging features that quantitatively characterise visible or hidden information from routine medical images. Radiomics data mining via machine learning has empowered precise diagnoses and prognoses of disease, mainly in oncology. Mounting evidence has shown its great potential in aiding the diagnosis and contributing to the study of musculoskeletal diseases. This paper will summarise the current development of radiomics at the crossroads between engineering and medicine and discuss the application and perspectives of radiomics analysis for OA diagnosis and prognosis.

The translational potential of this article

Radiomics is a novel approach used in oncology, and it may also play an essential role in the diagnosis and prognosis of OA. By transforming medical images from qualitative interpretation to quantitative data, radiomics could be the solution for precise early OA detection, progression tracking, and treatment efficacy prediction. Since the application of radiomics in OA is still in the early stages and primarily focuses on fundamental studies, this review may inspire more explorations and bring more promising diagnoses, prognoses, and management results of OA.

Survival analysis and clinicopathological features of patients with stage IA lung adenocarcinoma

Background

With the development of medical technology and change of life habits, early-stage lung adenocarcinoma (LUAD) has become more common. This study aimed to systematically analyzed clinicopathological factors associated to the overall survival (OS) of patients with Stage IA LUAD.

Methods

A total of 5942 Stage IA LUAD patients were obtained from the Surveillance, Epidemiology, and End Results (SEER) database. Kaplan-Meier methods and log-rank tests were used to compare the differences in OS. A nomogram constructed based on the Cox regression was evaluated by Concordance index (C index), calibration curve, decision curve analysis (DCA) and area under curve (AUC). And 136 patients were recruited from Shandong Province Hospital for external validation.

Results

Cox analysis regression indicated that 12 factors, such as Diagnosis to Treatment Interval (DTI) and Income Level, were independent prognostic factors and were included to establish the nomogram. The C-index of our novel model was 0.702, 0.724 and 0.872 in the training, internal and external validation cohorts, respectively. The 3-year and 5-year survival AUCs and calibration curves showed excellent agreement in each cohort. Some new factors in the SEER database, including DTI and Income Level, were firstly confirmed as independent prognostic factors of Stage IA LUAD patients. The distribution of these factors in the T1a, T1b, and T1c subgroups differed and had different effects on survival.

Conclusion

We summarized 12 factors that affect prognosis and constructed a nomogram to predict OS of Stage IA LUAD patients who underwent operation. For the first time, new SEER database parameters, including DTI and Income Level, were proved to be survival-related.

Osteoarthritis year in review 2023: Imaging

PURPOSE

This narrative review summarizes the original research in the field of in vivo osteoarthritis (OA) imaging between 1 January 2022 and 1 April 2023.

METHODS

A PubMed search was conducted using the following several terms pertaining to OA imaging, including but not limited to "Osteoarthritis / OA", "Magnetic resonance imaging / MRI", "X-ray" "Computed tomography / CT", "artificial intelligence /AI", "deep learning", "machine learning". This review is organized by topics including the anatomical structure of interest and modality, AI, challenges of OA imaging in the context of clinical trials, and imaging biomarkers in clinical trials and interventional studies. Ex vivo and animal studies were excluded from this review.

RESULTS

Two hundred and forty-nine publications were relevant to in vivo human OA imaging. Among the articles included, the knee joint (61%) and MRI (42%) were the predominant anatomical area and imaging modalities studied. Marked heterogeneity of structural tissue damage in OA knees was reported, a finding of potential relevance to clinical trial inclusion. The use of AI continues to rise rapidly to be applied in various aspect of OA imaging research but a lack of generalizability beyond highly standardized datasets limit interpretation and wide-spread application. No pharmacologic clinical trials using imaging data as outcome measures have been published in the period of interest.

CONCLUSIONS

Recent advances in OA imaging continue to heavily weigh on the use of AI. MRI remains the most important modality with a growing role in outcome prediction and classification.

Development and evaluation of nomograms for predicting osteoarthritis progression based on MRI cartilage parameters: data from the FNIH OA biomarkers Consortium

BACKGROUND

Osteoarthritis (OA) is a leading cause of disability worldwide. However, the existing methods for evaluating OA patients do not provide enough comprehensive information to make reliable predictions of OA progression. This retrospective study aimed to develop prediction nomograms based on MRI cartilage that can predict disease progression of OA.

METHODS

A total of 600 subjects with mild-to-moderate osteoarthritis from the Foundation for National Institute of Health (FNIH) project of osteoarthritis initiative (OAI). The MRI cartilage parameters of the knee at baseline were measured, and the changes in cartilage parameters at 12- and 24-month follow-up were calculated. The least absolute shrinkage and selection operator (LASSO) regression analysis was used to extract the valuable characteristic parameters at different time points including cartilage thickness, cartilage volume, subchondral bone exposure area and uniform cartilage thickness in different sub regions of the knee, and the MRI cartilage parameters score0, scoreΔ12, and scoreΔ24 at baseline, 12 months, and 24 months were constructed. ScoreΔ12, and scoreΔ24 represent changes between 12 M vs. baseline, and 24 M vs. baseline, respectively. Logistic regression analysis was used to construct the nomogram0, nomogramΔ12, and nomogramΔ24, including MRI-based score and risk factors. The area under curve (AUC) was used to evaluate the differentiation of nomograms in disease progression and subgroup analysis. The calibration curve and Hosmer-Lemeshow (H-L) test were used to verify the calibration of the nomograms. Clinical usefulness of each prediction nomogram was verified by decision curve analysis (DCA). The nomograms with predictive efficacy were analyzed by secondary analysis. Internal verification was assessed using bootstrapping validation.

RESULTS

Each nomogram included cartilage score, KL grade, WOMAC pain score, WOMAC disability score, and minimum joint space width. The AUC of nomogram0, nomogramΔ12, and nomogramΔ24 in predicing the progression of radiology and pain were 0.69, 0.64, and 0.71, respectively. All three nomograms had good calibration. Analysis by DCA showed that the clinical effectiveness of nomogramΔ24 was higher than others. Secondary analysis showed that nomogram0 and nomogramΔ24 were more capable of predicting OA radiologic progression than pain progression.

CONCLUSION

Nomograms based on MRI cartilage change were useful for predicting the progression of mild to moderate OA.