Assessment and risk prediction of frailty using texture-based muscle ultrasound image analysis and machine learning techniques

The purpose of this study was to evaluate texture-based muscle ultrasound image analysis for the assessment and risk prediction of frailty phenotype. This retrospective study of prospectively acquired data included 101 participants who underwent ultrasound scanning of the anterior thigh. Participants were subdivided according to frailty phenotype and were followed up for two years. Primary and secondary outcome measures were death and comorbidity, respectively. Forty-three texture features were computed from the rectus femoris and the vastus intermedius muscles using statistical methods. Model performance was evaluated by computing the area under the receiver operating characteristic curve (AUC) while outcome prediction was evaluated using regression analysis. Models developed achieved a moderate to good AUC (0.67 ≤ AUC ≤ 0.79) for categorizing frailty. The stepwise multiple logistic regression analysis demonstrated that they correctly classified 70-87% of the cases. The models were associated with increased comorbidity (0.01 ≤ p ≤ 0.18) and were predictive of death for pre-frail and frail participants (0.001 ≤ p ≤ 0.016). In conclusion, texture analysis can be useful to identify frailty and assess risk prediction (i.e. mortality) using texture features extracted from muscle ultrasound images in combination with a machine learning approach.

Head and neck cancer predictive risk estimator to determine control and therapeutic outcomes of radiotherapy (HNC-PREDICTOR): development, international multi-institutional validation, and web implementation of clinic-ready model-based risk stratification for head and neck cancer

BACKGROUND

Personalised radiotherapy can improve treatment outcomes of patients with head and neck cancer (HNC), where currently a 'one-dose-fits-all' approach is the standard. The aim was to establish individualised outcome prediction based on multi-institutional international 'big-data' to facilitate risk-based stratification of patients with HNC.

METHODS

The data of 4611 HNC radiotherapy patients from three academic cancer centres were split into four cohorts: a training (n = 2241), independent test (n = 786), and external validation cohorts 1 (n = 1087) and 2 (n = 497). Tumour- and patient-related clinical variables were considered in a machine learning pipeline to predict overall survival (primary end-point) and local and regional tumour control (secondary end-points); serially, imaging features were considered for optional model improvement. Finally, patients were stratified into high-, intermediate-, and low-risk groups.

RESULTS

Performance score, AJCC^8thstage, pack-years, and Age were identified as predictors for overall survival, demonstrating good performance in both the training cohort (c-index = 0.72 [95% CI, 0.66-0.77]) and in all three validation cohorts (c-indices: 0.76 [0.69-0.83], 0.73 [0.68-0.77], and 0.75 [0.68-0.80]). Excellent stratification of patients with HNC into high, intermediate, and low mortality risk was achieved; with 5-year overall survival rates of 17-46% for the high-risk group compared to 92-98% for the low-risk group. The addition of morphological image feature further improved the performance (c-index = 0.73 [0.64-0.81]). These models are integrated in a clinic-ready interactive web interface: https://uic-evl.github.io/hnc-predictor/ CONCLUSIONS: Robust model-based prediction was able to stratify patients with HNC in distinct high, intermediate, and low mortality risk groups. This can effectively be capitalised for personalised radiotherapy, e.g., for tumour radiation dose escalation/de-escalation.

Multi-scale tissue architecture analysis of favorable-risk prostate cancer: Correlation with biochemical recurrence

Purpose

Prostate cancer (PCa) with biopsy-based grade group (GG) 1 or 2 characteristics has a favorable outcome, yet some cases still progress after radical prostatectomy and present with biochemical recurrence (BCR). We hypothesized that the multi-scale tissue architecture (MSTA) analysis score would correlate with the aggressive PCa phenotype and could be used as a tool for risk assessment to improve the management of patients with favorable-risk PCa.

Materials and Methods

MSTA was evaluated in needle-biopsy samples from 115 patients with favorable-risk PCa, as defined by GG1 and GG2, a prostate-specific antigen (PSA) level of <10 ng/mL, a clinical stage of cT1c to cT2b, and general Gleason GG (GGG) and expert pathologist-assessed GG (EGG). Algorithms based on Voronoi diagrams were applied to all Feulgen-thionin-stained diagnostic areas. One hundred tissue architecture features were calculated and an MSTA score, a linear combination of the most discriminant features, was generated. Correlation of MSTA score with BCR and other clinical variables was investigated.

Results

In a univariate regression model, EGG, clinical stage, and MSTA were significant predictors of BCR (respective p-values: 0.0016, 0.016, and 0.028). Survival analysis showed that patients with a high MSTA score were more likely to experience BCR than were patients with a low MSTA score (odds ratio, 2.9). Combining MSTA with GG assessment resulted in a significant stratification of risk for BCR.

Conclusions

MSTA score could be used as an objective adjunct risk stratification tool to pathologist assessments and could improve the management of patients with favorable-risk PCa.

Radiomics and deep learning in lung cancer

Lung malignancies have been extensively characterized through radiomics and deep learning. By providing a three-dimensional characterization of the lesion, models based on radiomic features from computed tomography (CT) and positron-emission tomography (PET) have been developed to detect nodules, distinguish malignant from benign lesions, characterize their histology, stage, and genotype. Deep learning models have been applied to automatically segment organs at risk in lung cancer radiotherapy, stratify patients according to the risk for local and distant recurrence, and identify patients candidate for molecular targeted therapy and immunotherapy. Moreover, radiomics has also been applied successfully to predict side effects such as radiation- and immunotherapy-induced pneumonitis and differentiate lung injury from recurrence. Radiomics could also untap the potential for further use of the cone beam CT acquired for treatment image guidance, four-dimensional CT, and dose-volume data from radiotherapy treatment plans. Radiomics is expected to increasingly affect the clinical practice of treatment of lung tumors, optimizing the end-to-end diagnosis-treatment-follow-up chain. The main goal of this article is to provide an update on the current status of lung cancer radiomics.

Improving the diagnosis of common parotid tumors via the combination of CT image biomarkers and clinical parameters

Background

Our study aims to develop and validate diagnostic models of the common parotid tumors based on whole-volume CT textural image biomarkers (IBMs) in combination with clinical parameters at a single institution.

Methods

The study cohort was composed of 51 pleomorphic adenoma (PA) patients and 42 Warthin tumor (WT) patients. Clinical parameters and conventional image features were scored retrospectively and textural IBMs were extracted from CT images of arterial phase. Independent-samples t test or Chi-square test was used for evaluating the significance of the difference among clinical parameters, conventional CT image features, and textural IBMs. The diagnostic performance of univariate model and multivariate model was evaluated via receiver operating characteristic (ROC) curve and area under ROC curve (AUC).

Results

Significant differences were found in clinical parameters (age, gender, disease duration, smoking), conventional image features (site, maximum diameter, time-density curve, peripheral vessels sign) and textural IBMs (mean, uniformity, energy, entropy) between PA group and WT group (P<0.05). ROC analysis showed that clinical parameter (age) and quantitative textural IBMs (mean, energy, entropy) were able to categorize the patients into PA group and WT group, with the AUC of 0.784, 0.902, 0.910, 0.805, respectively. When IBMs were added in clinical model, the multivariate models including age-mean and age-energy performed significantly better than the univariate models with the improved AUC of 0.940, 0.944, respectively (P<0.001).

Conclusions

Both clinical parameter and CT textural IBMs can be used for the preoperative, noninvasive diagnosis of parotid PA and WT. The diagnostic performance of textural IBM model was obviously better than that of clinical model and conventional image model in this study. While the multivariate model consisted of clinical parameter and textural IBM had the optimal diagnostic performance, which would contribute to the better selection of individualized surgery program.

Parotid gland fat related Magnetic Resonance image biomarkers improve prediction of late radiation-induced xerostomia

PURPOSE

This study investigated whether Magnetic Resonance image biomarkers (MR-IBMs) were associated with xerostomia 12 months after radiotherapy (Xer_12m) and to test the hypothesis that the ratio of fat-to-functional parotid tissue is related to Xer_12m. Additionally, improvement of the reference Xer_12m model based on parotid gland dose and baseline xerostomia, with MR-IBMs was explored.

METHODS

Parotid gland MR-IBMs of 68 head and neck cancer patients were extracted from pre-treatment T1-weighted MR images, which were normalized to fat tissue, quantifying 21 intensity and 43 texture image characteristics. The performance of the resulting multivariable logistic regression models after bootstrapped forward selection was compared with that of the logistic regression reference model. Validity was tested in a small external cohort of 25 head and neck cancer patients.

RESULTS

High intensity MR-IBM P90 (the 90th intensity percentile) values were significantly associated with a higher risk of Xer_12m. High P90 values were related to high fat concentration in the parotid glands. The MR-IBM P90 significantly improved model performance in predicting Xer_12m (likelihood-ratio-test; p = 0.002), with an increase in internally validated AUC from 0.78 (reference model) to 0.83 (P90). The MR-IBM P90 model also outperformed the reference model (AUC = 0.65) on the external validation cohort (AUC = 0.83).

CONCLUSION

Pre-treatment MR-IBMs were associated to radiation-induced xerostomia, which supported the hypothesis that the amount of predisposed fat within the parotid glands is associated with Xer_12m. In addition, xerostomia prediction was improved with MR-IBMs compared to the reference model.

CT image biomarkers to improve patient-specific prediction of radiation-induced xerostomia and sticky saliva

BACKGROUND AND PURPOSE

Current models for the prediction of late patient-rated moderate-to-severe xerostomia (XER_12m) and sticky saliva (STIC_12m) after radiotherapy are based on dose-volume parameters and baseline xerostomia (XER_base) or sticky saliva (STIC_base) scores. The purpose is to improve prediction of XER_12m and STIC_12m with patient-specific characteristics, based on CT image biomarkers (IBMs).

METHODS

Planning CT-scans and patient-rated outcome measures were prospectively collected for 249 head and neck cancer patients treated with definitive radiotherapy with or without systemic treatment. The potential IBMs represent geometric, CT intensity and textural characteristics of the parotid and submandibular glands. Lasso regularisation was used to create multivariable logistic regression models, which were internally validated by bootstrapping.

RESULTS

The prediction of XER_12m could be improved significantly by adding the IBM "Short Run Emphasis" (SRE), which quantifies heterogeneity of parotid tissue, to a model with mean contra-lateral parotid gland dose and XER_base. For STIC_12m, the IBM maximum CT intensity of the submandibular gland was selected in addition to STIC_base and mean dose to submandibular glands.

CONCLUSION

Prediction of XER_12m and STIC_12m was improved by including IBMs representing heterogeneity and density of the salivary glands, respectively. These IBMs could guide additional research to the patient-specific response of healthy tissue to radiation dose.

T2 texture index of cartilage can predict early symptomatic OA progression: data from the osteoarthritis initiative

OBJECTIVE

There is an interest in using Magnetic Resonance Imaging (MRI) to identify pre-radiographic changes in osteoarthritis (OA) and features that indicate risk for disease progression. The purpose of this study is to identify image features derived from MRI T2 maps that can accurately predict onset of OA symptoms in subjects at risk for incident knee OA.

METHODS

Patients were selected from the Osteoarthritis Initiative (OAI) control cohort and incidence cohort and stratified based on the change in total Western Ontario and McMaster Universities Arthritis (WOMAC) score from baseline to 3-year follow-up (80 non-OA progression and 88 symptomatic OA progression patients). For each patient, a series of image texture features were measured from the baseline cartilage T2 map. A linear discriminant function and feature reduction method was then trained to quantify a texture metric, the T2 texture index of cartilage (TIC), based on 22 image features, to identify a composite marker of T2 heterogeneity.

RESULTS

Statistically significant differences were seen in the baseline T2 TIC between the non-progression and symptomatic OA progression populations. The baseline T2 TIC differentiates subjects that develop worsening of their WOMAC score OA with an accuracy between 71% and 76%. The T2 TIC differences were predominantly localized to a dominant knee compartment that correlated with the mechanical axis of the knee.

CONCLUSION

Baseline heterogeneity in cartilage T2 as measured with the T2 TIC index is able to differentiate and predict individuals that will develop worsening of their WOMAC score at 3-year follow-up.