Decision support systems for personalized and participative radiation oncology

Radiomics in Diagnosis, Grading, and Treatment Response Assessment of Soft Tissue Sarcomas: A Systematic Review and Meta-analysis

RATIONALE AND OBJECTIVES

To evaluate radiomics in soft tissue sarcomas (STSs) for diagnostic accuracy, grading, and treatment response assessment, with a focus on clinical relevance.

METHODS

In this diagnostic accuracy study, radiomics was applied using multiple MRI sequences and AI classifiers, with histopathological diagnosis as the reference standard. Statistical analysis involved meta-analysis, random-effects model, and Deeks' funnel plot asymmetry test.

RESULTS

Among 579 unique titles and abstracts, 24 articles were included in the systematic review, with 21 used for meta-analysis. Radiomics demonstrated a pooled sensitivity of 84% (95% CI: 80-87) and specificity of 63% (95% CI: 56-70), AUC of 0.93 for diagnosis, sensitivity of 84% (95% CI: 82-87) and specificity of 73% (95% CI: 68-77), AUC of 0.91 for grading, and sensitivity of 83% (95% CI: 67-94) and specificity of 67% (95% CI: 59-74), AUC of 0.87 for treatment response assessment.

CONCLUSION

Radiomics exhibits potential for accurate diagnosis, grading, and treatment response assessment in STSs, emphasizing the need for standardization and prospective trials.

CLINICAL RELEVANCE STATEMENT

Radiomics offers precise tools for STS diagnosis, grading, and treatment response assessment, with implications for optimizing patient care and treatment strategies in this complex malignancy.

Does FDG PET-Based Radiomics Have an Added Value for Prediction of Overall Survival in Non-Small Cell Lung Cancer?

Objectives: Radiomics and machine learning are innovative approaches to improve the clinical management of NSCLC. However, there is less information about the additive value of FDG PET-based radiomics compared with clinical and imaging variables. Methods: This retrospective study included 320 NSCLC patients who underwent PET/CT with FDG at initial staging. VOIs were placed on primary tumors only. We included a total of 94 variables, including 87 textural features extracted from PET studies, SUVmax, MTV, TLG, TNM stage, histology, age, and gender. We used the least absolute shrinkage and selection operator (LASSO) regression to select variables with the highest predictive value. Although several radiomics variables are available, the added value of these predictors compared with clinical and imaging variables is still under evaluation. Three hundred and twenty NSCLC patients were included in this retrospective study and underwent 18F-FDG PET/CT at initial staging. In this study, we evaluated 94 variables, including 87 textural features, SUVmax, MTV, TLG, TNM stage, histology, age, and gender. Image-based predictors were extracted from a volume of interest (VOI) positioned on the primary tumor. The least absolute shrinkage and selection operator (LASSO) Cox regression was used to reduce the number of variables and select only those with the highest predictive value. The predictive model implemented with the variables selected using the LASSO analysis was compared with a reference model using only a tumor stage and SUVmax. Results: NGTDM coarseness, SUVmax, and TNM stage survived the LASSO analysis and were used for the radiomic model. The AUCs obtained from the reference and radiomic models were 80.82 (95%CI, 69.01-92.63) and 81.02 (95%CI, 69.07-92.97), respectively (p = 0.98). The median OS in the reference model was 17.0 months in high-risk patients (95%CI, 11-21) and 113 months in low-risk patients (HR 7.47, p < 0.001). In the radiomic model, the median OS was 16.5 months (95%CI, 11-20) and 113 months in high- and low-risk groups, respectively (HR 9.64, p < 0.001). Conclusions: Our results indicate that a radiomic model composed using the tumor stage, SUVmax, and a selected radiomic feature (NGTDM_Coarseness) predicts survival in NSCLC patients similarly to a reference model composed only by the tumor stage and SUVmax. Replication of these preliminary results is necessary.

Multi-parametric MRI-based machine learning model for prediction of WHO grading in patients with meningiomas

OBJECTIVE

The purpose of this study was to develop and validate a nomogram combined multiparametric MRI and clinical indicators for identifying the WHO grade of meningioma.

MATERIALS AND METHODS

Five hundred and sixty-eight patients were included in this study, who were diagnosed pathologically as having meningiomas. Firstly, radiomics features were extracted from CE-T1, T2, and 1-cm-thick tumor-to-brain interface (BTI) images. Then, difference analysis and the least absolute shrinkage and selection operator were orderly used to select the most representative features. Next, the support vector machine algorithm was conducted to predict the WHO grade of meningioma. Furthermore, a nomogram incorporated radiomics features and valuable clinical indicators was constructed by logistic regression. The performance of the nomogram was assessed by calibration and clinical effectiveness, as well as internal validation.

RESULTS

Peritumoral edema volume and gender are independent risk factors for predicting meningioma grade. The multiparametric MRI features incorporating CE-T1, T2, and BTI features showed the higher performance for prediction of meningioma grade with a pooled AUC = 0.885 (95% CI, 0.821-0.946) and 0.860 (95% CI, 0.788-0.923) in the training and test groups, respectively. Then, a nomogram with a pooled AUC = 0.912 (95% CI, 0.876-0.961), combined radiomics score, peritumoral edema volume, and gender improved diagnostic performance compared to radiomics model or clinical model and showed good calibration as the true results. Moreover, decision curve analysis demonstrated satisfactory clinical effectiveness of the proposed nomogram.

CONCLUSIONS

A novel nomogram is simple yet effective in differentiating WHO grades of meningioma and thus can be used in patients with meningiomas.

CLINICAL RELEVANCE STATEMENT

We proposed a nomogram that included clinical indicators and multi-parameter radiomics features, which can accurately, objectively, and non-invasively differentiate WHO grading of meningioma and thus can be used in clinical work.

KEY POINTS

• The study combined radiomics features and clinical indicators for objectively predicting the meningioma grade. • The model with CE-T1 + T2 + brain-to-tumor interface features demonstrated the best predictive performance by investigating seven different radiomics models. • The nomogram potentially has clinical applications in distinguishing high-grade and low-grade meningiomas.

Research on multi-model imaging machine learning for distinguishing early hepatocellular carcinoma

OBJECTIVE

To investigate the value of differential diagnosis of hepatocellular carcinoma (HCC) and non-hepatocellular carcinoma (non-HCC) based on CT and MR multiphase radiomics combined with different machine learning models and compare the diagnostic efficacy between different radiomics models.

BACKGROUND

Primary liver cancer is one of the most common clinical malignancies, hepatocellular carcinoma (HCC) is the most common subtype of primary liver cancer, accounting for approximately 90% of cases. A clear diagnosis of HCC is important for the individualized treatment of patients with HCC. However, more sophisticated diagnostic modalities need to be explored.

METHODS

This retrospective study included 211 patients with liver lesions: 97 HCC and 124 non-hepatocellular carcinoma (non-HCC) who underwent CT and MRI. Imaging data were used to obtain imaging features of lesions and radiomics regions of interest (ROI). The extracted imaging features were combined to construct different radiomics models. The clinical data and imaging features were then combined with radiomics features to construct the combined models. Support Vector Machine (SVM), K-nearest Neighbor (KNN), RandomForest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Multilayer Perceptron (MLP) six machine learning models were used for training. Five-fold cross-validation was used to train the models, and ROC curves were used to analyze the diagnostic efficacy of each model and calculate the accuracy rate. Model training and efficacy test were performed as before.

RESULTS

Statistical analysis showed that some clinical data (gender and concomitant cirrhosis) and imaging features (presence of envelope, marked enhancement in the arterial phase, rapid contouring in the portal phase, uniform density/signal and concomitant steatosis) were statistical differences (P < 0.001). The results of machine learning models showed that KNN had the best diagnostic efficacy. The results of the combined model showed that SVM had the best diagnostic efficacy, indicating that the combined model (accuracy 0.824) had better diagnostic efficacy than the radiomics-only model.

CONCLUSIONS

Our results demonstrate that the radiomic features of CT and MRI combined with machine learning models enable differential diagnosis of HCC and non-HCC (malignant, benign). The diagnostic model with dual radiomic had better diagnostic efficacy. The combined model was superior to the radiomic model alone.

A Machine Learning Model to Predict Patients' Adherence Behavior and a Decision Support System for Patients With Metastatic Breast Cancer: Protocol for a Randomized Controlled Trial

BACKGROUND

Adherence to oral anticancer treatments is critical in the disease trajectory of patients with breast cancer. Given the impact of nonadherence on clinical outcomes and the associated economic burden for the health care system, finding ways to increase treatment adherence is particularly relevant.

OBJECTIVE

The primary end point is to evaluate the effectiveness of a decision support system (DSS) and a machine learning web application in promoting adherence to oral anticancer treatments among patients with metastatic breast cancer. The secondary end point is to collect a set of new physical, psychological, social, behavioral, and quality of life predictive variables that could be used to refine the preliminary version of the machine learning model to predict patients' adherence behavior.

METHODS

This prospective, randomized controlled study is nested in a large-scale international project named "Enhancing therapy adherence among metastatic breast cancer patients" (Pfizer 65080791), aimed to develop a predictive model of nonadherence and associated DSS and guidelines to foster patients' engagement and therapy adherence. A web-based DSS named TREAT (treatment adherence support) was developed using a patient-driven approach, with 4 sections, that is, Section A: Metastatic Breast Cancer; Section B: Adherence to Cancer Therapies; Section C: Promoting Adherence; and Section D: My Adherence Diary. Moreover, a machine learning-based web application was developed to predict patients' risk factors of adherence to anticancer treatment, specifically pertaining to physical status and comorbid conditions, as well as short and long-term side effects. Overall, 100 patients consecutively admitted at the European Institute of Oncology (IEO) at the Division of Medical Senology will be enrolled; 50 patients with metastatic breast cancer will be exposed to the DSS and machine learning web application for 3 months (experimental group), and 50 patients will not be exposed to the intervention (control group). Each participant will fill a weekly medication diary and a set of standardized self-reports evaluating psychological and quality of life variables (Adherence Attitude Inventory, Beck Depression Inventory-II, Brief Pain Inventory, 13-item Sense of Coherence scale, Brief Italian version of Cancer Behavior Inventory, European Organization for Research and Treatment of Cancer Quality of Life 23-item Breast Cancer-specific Questionnaire, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire, 8-item Morisky Medication Adherence Scale, State-Trait Anxiety Inventory forms I and II, Big Five Inventory, and visual analogue scales evaluating risk perception). The 3 assessment time points are T0 (baseline), T1 (1 month), T2 (2 months), and T3 (3 months). This study was approved by the IEO ethics committee (R1786/22-IEO 1907).

RESULTS

The recruitment process started in May 2023 and is expected to conclude on December 2023.

CONCLUSIONS

The contribution of machine learning techniques through risk-predictive models integrated into DSS will enable medication adherence by patients with cancer.

TRIAL REGISTRATION

ClinicalTrials.gov NCT06161181; https://clinicaltrials.gov/study/NCT06161181.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/48852.

External Validation of Robust Radiomic Signature to Predict 2-Year Overall Survival in Non-Small-Cell Lung Cancer

Lung cancer is the second most fatal disease worldwide. In the last few years, radiomics is being explored to develop prediction models for various clinical endpoints in lung cancer. However, the robustness of radiomic features is under question and has been identified as one of the roadblocks in the implementation of a radiomic-based prediction model in the clinic. Many past studies have suggested identifying the robust radiomic feature to develop a prediction model. In our earlier study, we identified robust radiomic features for prediction model development. The objective of this study was to develop and validate the robust radiomic signatures for predicting 2-year overall survival in non-small cell lung cancer (NSCLC). This retrospective study included a cohort of 300 stage I-IV NSCLC patients. Institutional 200 patients' data were included for training and internal validation and 100 patients' data from The Cancer Image Archive (TCIA) open-source image repository for external validation. Radiomic features were extracted from the CT images of both cohorts. The feature selection was performed using hierarchical clustering, a Chi-squared test, and recursive feature elimination (RFE). In total, six prediction models were developed using random forest (RF-Model-O, RF-Model-B), gradient boosting (GB-Model-O, GB-Model-B), and support vector(SV-Model-O, SV-Model-B) classifiers to predict 2-year overall survival (OS) on original data as well as balanced data. Model validation was performed using 10-fold cross-validation, internal validation, and external validation. Using a multistep feature selection method, the overall top 10 features were chosen. On internal validation, the two random forest models (RF-Model-O, RF-Model-B) displayed the highest accuracy; their scores on the original and balanced datasets were 0.81 and 0.77 respectively. During external validation, both the random forest models' accuracy was 0.68. In our study, robust radiomic features showed promising predictive performance to predict 2-year overall survival in NSCLC.

Harnessing the power of clinical decision support systems: challenges and opportunities

Clinical decision support systems (CDSSs) are increasingly integrated into healthcare settings to improve patient outcomes, reduce medical errors and enhance clinical efficiency by providing clinicians with evidence-based recommendations at the point of care. However, the adoption and optimisation of these systems remain a challenge. This review aims to provide an overview of the current state of CDSS, discussing their development, implementation, benefits, limitations and future directions. We also explore the potential for enhancing their effectiveness and provide an outlook for future developments in this field. There are several challenges in CDSS implementation, including data privacy concerns, system integration and clinician acceptance. While CDSS have demonstrated significant potential, their adoption and optimisation remain a challenge.

A cloud-based consultation and collaboration system for radiotherapy: Remote decision support services for community radiotherapy centers

PURPOSE

This study aimed to establish a cloud-based radiotherapy consultation and collaboration system, then investigated the practicability of remote decision support for community radiotherapy centers using the system.

METHODS AND MATERIALS

A cloud-based consultation and collaboration system for radiotherapy, OncoEvidance®, was developed to provide remote services of LINAC modeling, simulation CT data import/export, target volume and organ-at-risk delineation, prescription, and treatment planning. The system was deployed on a hybrid cloud. A federate of public nodes, each corresponding to a medical institution, are managed by a central node where a group of consultants have registered. Users can access the system through network using computing devices. The system has been tested at three community radiotherapy centers. One accelerator was modeled. 12 consultants participated the remote radiotherapy decision support and 77 radiation treatment plans had been evaluated remotely.

RESULTS

All the passing rates of per-beam dose verification are > 94% and all the passing rates of composite beam dose verification are > 99%. The average downloading time for one set of simulation CT data for one patient from Internet was within 1 min under the cloud download bandwidth of 8 Mbps and local network bandwidth of 100 Mbps. The average response time for one consultant to contour target volumes and make prescription was about 24 h. And that for one consultant to design and optimize a IMRT treatment plan was about 36 h. 100% of the remote plans passed the dosimetric criteria and could be imported into the local TPS for further verification.

CONCLUSION

The cloud-based consultation and collaboration system saved the travel time for consultants and provided high quality radiotherapy to patients in community centers. The under-staffed community radiotherapy centers could benefit from the remote system with lower cost and better treatment quality control.

Decision Support System and Web-Application Using Supervised Machine Learning Algorithms for Easy Cancer Classifications

Using a decision support system (DSS) that classifies various cancers provides support to the clinicians/researchers to make better decisions that can aid in early cancer diagnosis, thereby reducing chances of incorrect disease diagnosis. Thus, this work aimed at designing a classification model that can predict accurately for 5 different cancer types comprising of 20 cancer exomes, using the mutations identified from whole exome cancer analysis. Initially, a basic model was designed using supervised machine learning classification algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), decision tree, naïve bayes and random forest (RF), among which decision tree and random forest performed better in terms of preliminary model accuracy. However, output predictions were incorrect due to less training scores. Thus, 16 essential features were then selected for model improvement using 2 approaches. All imbalanced datasets were balanced using SMOTE. In the first approach, all features from 20 cancer exome datasets were trained and models were designed using decision tree and random forest. Balanced datasets for decision tree model showed an accuracy of 77%, while with the RF model, the accuracy improved to 82% where all 5 cancer types were predicted correctly. Area under the curve for RF model was closer to 1, than decision tree model. In the second approach, all 15 datasets were trained, while 5 were tested. However, only 2 cancer types were predicted correctly. To cross validate RF model, Matthew's correlation co-efficient (MCC) test was performed. For method 1, the MCC test and MCC cross validation was found to be 0.7796 and 0.9356 respectively. Likewise, for second approach, MCC was observed to be 0.9365, corroborating the accuracy of the designed model. The model was successfully deployed using Streamlit as a web application for easy use. This study presents insights for allowing easy cancer classifications.

Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer

INTRODUCTION

Emerging evidence suggests that data-driven support tools have found their way into clinical decision-making in a number of areas, including cancer care. Improving them and widening their scope of availability in various differing clinical scenarios, including for prognostic models derived from retrospective data, requires co-ordinated data sharing between clinical centres, secondary analyses of large multi-institutional clinical trial data, or distributed (federated) learning infrastructures. A systematic approach to utilizing routinely collected data across cancer care clinics remains a significant challenge due to privacy, administrative and political barriers.

METHODS

An information technology infrastructure and web service software was developed and implemented which uses machine learning to construct clinical decision support systems in a privacy-preserving manner across datasets geographically distributed in different hospitals. The infrastructure was deployed in a network of Australian hospitals. A harmonized, international ontology-linked, set of lung cancer databases were built with the routine clinical and imaging data at each centre. The infrastructure was demonstrated with the development of logistic regression models to predict major cardiovascular events following radiation therapy.

RESULTS

The infrastructure implemented forms the basis of the Australian computer-assisted theragnostics (AusCAT) network for radiation oncology data extraction, reporting and distributed learning. Four radiation oncology departments (across seven hospitals) in New South Wales (NSW) participated in this demonstration study. Infrastructure was deployed at each centre and used to develop a model predicting for cardiovascular admission within a year of receiving curative radiotherapy for non-small cell lung cancer. A total of 10417 lung cancer patients were identified with 802 being eligible for the model. Twenty features were chosen for analysis from the clinical record and linked registries. After selection, 8 features were included and a logistic regression model achieved an area under the receiver operating characteristic (AUROC) curve of 0.70 and C-index of 0.65 on out-of-sample data.

CONCLUSION

The infrastructure developed was demonstrated to be usable in practice between clinical centres to harmonize routinely collected oncology data and develop models with federated learning. It provides a promising approach to enable further research studies in radiation oncology using real world clinical data.

A Systematic Review of the Treatment Outcomes among Prostate Cancer Patients in Africa

Prostate cancer (PCa) is associated with a significant public health burden in Africa. This systematic review aimed to assess treatment outcomes among PCa patients in Africa. A systematic search of the literature was conducted from 1 December 2021 to 31 March 2022 to identify relevant published studies. PubMed, EMBASE, CINAHL, and Google Scholar databases were used. Twenty-four studies met the inclusion criteria, and the mean age was 68 years. Localized and locally advanced diseases had relatively higher overall survival than metastatic diseases. In metastatic disease, the mean overall five-year survival was 42% which is shorter than the Asian population (61.9%).

A Deep Learning Radiomics Analysis for Survival Prediction in Esophageal Cancer

The purpose of this study was to explore the deep learning radiomics (DLR) nomogram to predict the overall 3-year survival after chemoradiotherapy in patients with esophageal cancer. The 154 patients' data were used in this study, which was randomly split into training (116) and validation (38) data. Deep learning and handcrafted features were obtained via the preprocessing diagnostic computed tomography images. The selected features were used to construct radiomics signatures through the least absolute shrinkage and selection operator (LASSO) regression, maximizing relevance while minimizing redundancy. The DLR signature, handcrafted features' radiomics (HCR) signature, and clinical factors were incorporated to develop a DLR nomogram. The DLR nomogram was evaluated in terms of discrimination and calibration with comparison to the HCR signature-based radiomics model. The experimental results showed the outperforming discrimination ability of the proposed DLR over the HCR model in terms of Harrel's concordance index, 0.76 and 0.784, for training and validation sets, respectively. Also, the proposed DLR nomogram calibrates and classifies better than the HCR model in terms of AUC, 0.984 (vs. 0.797) and 0.942 (vs. 0.665) for training and validation sets, respectively. Furthermore, the nomogram-predicted Kaplan–Meier survival (KMS) curves differed significantly from the nonsurvival groups in the log-rank test (p value <0.05). The proposed DLR model based on conventional CT images showed the outperforming performance over the HCR signature model in noninvasively individualized prediction of the 3-year survival rate in esophageal cancer patients. The proposed model can potentially provide prognostic information that guides and helps the clinical decisions between observation and treatment.

An artificial intelligence framework integrating longitudinal electronic health records with real-world data enables continuous pan-cancer prognostication

Despite widespread adoption of electronic health records (EHRs), most hospitals are not ready to implement data science research in the clinical pipelines. Here, we develop MEDomics, a continuously learning infrastructure through which multimodal health data are systematically organized and data quality is assessed with the goal of applying artificial intelligence for individual prognosis. Using this framework, currently composed of thousands of individuals with cancer and millions of data points over a decade of data recording, we demonstrate prognostic utility of this framework in oncology. As proof of concept, we report an analysis using this infrastructure, which identified the Framingham risk score to be robustly associated with mortality among individuals with early-stage and advanced-stage cancer, a potentially actionable finding from a real-world cohort of individuals with cancer. Finally, we show how natural language processing (NLP) of medical notes could be used to continuously update estimates of prognosis as a given individual's disease course unfolds.

Role of 18F-Fluorodeoxyglucose-Positron Emission Tomography/Computed Tomography Imaging in the Prediction of Prognosis in Patients With Indolent Lymphoma: Prospective Study

BACKGROUND

The role of fluorodeoxyglucose-positron emission tomography/computed tomography (FDG-PET/CT) in indolent lymphoma has been minimally studied.

OBJECTIVE

This study aims to assess the value of FDG-PET/CT in predicting the prognosis of indolent lymphoma.

METHODS

We prospectively recruited 42 patients with indolent lymphoma. A total of 2 patients were excluded, and 40 underwent baseline PET/CT and follow-up at various time points. A total of 9 patients were observed only, 7 received 4 doses of rituximab alone, and 24 received chemoimmunotherapy. Metabolic response on follow-up PET/CT was assessed using the maximum standardized uptake value (SUVmax) and Deauville criteria (DC). We aimed to obtain the best SUVmax and DC to predict optimal survival rates, risk stratification, and optimize therapeutic strategies. The mean follow-up from the initial diagnosis was 33.83 months.

RESULTS

SUVmax <4.35 at interim PET/CT provided the best discrimination, with a progression-free survival (PFS) of 100% and a median survival time of 106.67 months compared with SUVmax ≥4.35 (P=.04), which had a PFS of 43.8% and a median survival time of 50.17 months. This cutoff was also valuable in predicting overall survival at baseline, that is, 100% overall survival with baseline SUVmax <4.35, versus 58.4% for SUVmax ≥4.35 (P=.13). The overall survival of patients with a baseline DC score <3.0 was 100%, with a median overall survival of 106.67 months.

CONCLUSIONS

We demonstrated the utility of PET/CT in indolent lymphomas. SUVmax (<4.35 vs ≥4.35) on interim PET/CT performed best in predicting PFS.

Improving Early Identification of Significant Weight Loss Using Clinical Decision Support System in Lung Cancer Radiation Therapy

PURPOSE

Early identification of patients who may be at high risk of significant weight loss (SWL) is important for timely clinical intervention in lung cancer radiotherapy (RT). A clinical decision support system (CDSS) for SWL prediction was implemented within the routine clinical workflow and assessed on a prospective cohort of patients.

MATERIALS AND METHODS

CDSS incorporated a machine learning prediction model on the basis of radiomics and dosiomics image features and was connected to a web-based dashboard for streamlined patient enrollment, feature extraction, SWL prediction, and physicians' evaluation processes. Patients with lung cancer (N = 37) treated with definitive RT without prior RT were prospectively enrolled in the study. Radiomics and dosiomics features were extracted from CT and 3D dose volume, and SWL probability (≥ 0.5 considered as SWL) was predicted. Two physicians predicted whether the patient would have SWL before and after reviewing the CDSS prediction. The physician's prediction performance without and with CDSS and prediction changes before and after using CDSS were compared.

RESULTS

CDSS showed significantly better prediction accuracy than physicians (0.73 v 0.54) with higher specificity (0.81 v 0.50) but with lower sensitivity (0.55 v 0.64). Physicians changed their original prediction after reviewing CDSS prediction for four cases (three correctly and one incorrectly), for all of which CDSS prediction was correct. Physicians' prediction was improved with CDSS in accuracy (0.54-0.59), sensitivity (0.64-0.73), specificity (0.50-0.54), positive predictive value (0.35-0.40), and negative predictive value (0.76-0.82).

CONCLUSION

Machine learning-based CDSS showed the potential to improve SWL prediction in lung cancer RT. More investigation on a larger patient cohort is needed to properly interpret CDSS prediction performance and its benefit in clinical decision making.

Exploratory Radiomic Analysis of Conventional vs. Quantitative Brain MRI: Toward Automatic Diagnosis of Early Multiple Sclerosis

Conventional magnetic resonance imaging (cMRI) is poorly sensitive to pathological changes related to multiple sclerosis (MS) in normal-appearing white matter (NAWM) and gray matter (GM), with the added difficulty of not being very reproducible. Quantitative MRI (qMRI), on the other hand, attempts to represent the physical properties of tissues, making it an ideal candidate for quantitative medical image analysis or radiomics. We therefore hypothesized that qMRI-based radiomic features have added diagnostic value in MS compared to cMRI. This study investigated the ability of cMRI (T1w) and qMRI features extracted from white matter (WM), NAWM, and GM to distinguish between MS patients (MSP) and healthy control subjects (HCS). We developed exploratory radiomic classification models on a dataset comprising 36 MSP and 36 HCS recruited in CHU Liege, Belgium, acquired with cMRI and qMRI. For each image type and region of interest, qMRI radiomic models for MS diagnosis were developed on a training subset and validated on a testing subset. Radiomic models based on cMRI were developed on the entire training dataset and externally validated on open-source datasets with 167 HCS and 10 MSP. Ranked by region of interest, the best diagnostic performance was achieved in the whole WM. Here the model based on magnetization transfer imaging (a type of qMRI) features yielded a median area under the receiver operating characteristic curve (AUC) of 1.00 in the testing sub-cohort. Ranked by image type, the best performance was achieved by the magnetization transfer models, with median AUCs of 0.79 (0.69–0.90, 90% CI) in NAWM and 0.81 (0.71–0.90) in GM. The external validation of the T1w models yielded an AUC of 0.78 (0.47–1.00) in the whole WM, demonstrating a large 95% CI and a low sensitivity of 0.30 (0.10–0.70). This exploratory study indicates that qMRI radiomics could provide efficient diagnostic information using NAWM and GM analysis in MSP. T1w radiomics could be useful for a fast and automated check of conventional MRI for WM abnormalities once acquisition and reconstruction heterogeneities have been overcome. Further prospective validation is needed, involving more data for better interpretation and generalization of the results.

A review in radiomics: Making personalized medicine a reality via routine imaging

Radiomics is the quantitative analysis of standard-of-care medical imaging; the information obtained can be applied within clinical decision support systems to create diagnostic, prognostic, and/or predictive models. Radiomics analysis can be performed by extracting hand-crafted radiomics features or via deep learning algorithms. Radiomics has evolved tremendously in the last decade, becoming a bridge between imaging and precision medicine. Radiomics exploits sophisticated image analysis tools coupled with statistical elaboration to extract the wealth of information hidden inside medical images, such as computed tomography (CT), magnetic resonance (MR), and/or Positron emission tomography (PET) scans, routinely performed in the everyday clinical practice. Many efforts have been devoted in recent years to the standardization and validation of radiomics approaches, to demonstrate their usefulness and robustness beyond any reasonable doubts. However, the booming of publications and commercial applications of radiomics approaches warrant caution and proper understanding of all the factors involved to avoid "scientific pollution" and overly enthusiastic claims by researchers and clinicians alike. For these reasons the present review aims to be a guidebook of sorts, describing the process of radiomics, its pitfalls, challenges, and opportunities, along with its ability to improve clinical decision-making, from oncology and respiratory medicine to pharmacological and genotyping studies.

Creation of clinical algorithms for decision-making in oncology: an example with dose prescription in radiation oncology

In oncology, decision-making in individual situations is often very complex. To deal with such complexity, people tend to reduce it by relying on their initial intuition. The downside of this intuitive, subjective way of decision-making is that it is prone to cognitive and emotional biases such as overestimating the quality of its judgements or being influenced by one’s current mood. Hence, clinical predictions based on intuition often turn out to be wrong and to be outperformed by statistical predictions. Structuring and objectivizing oncological decision-making may thus overcome some of these issues and have advantages such as avoidance of unwarranted clinical practice variance or error-prevention. Even for uncertain situations with limited medical evidence available or controversies about the best treatment option, structured decision-making approaches like clinical algorithms could outperform intuitive decision-making. However, the idea of such algorithms is not to prescribe the clinician which decision to make nor to abolish medical judgement, but to support physicians in making decisions in a systematic and structured manner. An example for a use-case scenario where such an approach may be feasible is the selection of treatment dose in radiation oncology. In this paper, we will describe how a clinical algorithm for selection of a fractionation scheme for palliative irradiation of bone metastases can be created. We explain which steps in the creation process of a clinical algorithm for supporting decision-making need to be  performed and which challenges and limitations have to be considered.

Machine learning applications in radiation oncology

Machine learning technology has a growing impact on radiation oncology with an increasing presence in research and industry. The prevalence of diverse data including 3D imaging and the 3D radiation dose delivery presents potential for future automation and scope for treatment improvements for cancer patients. Harnessing this potential requires standardization of tools and data, and focused collaboration between fields of expertise. The rapid advancement of radiation oncology treatment technologies presents opportunities for machine learning integration with investments targeted towards data quality, data extraction, software, and engagement with clinical expertise. In this review, we provide an overview of machine learning concepts before reviewing advances in applying machine learning to radiation oncology and integrating these techniques into the radiation oncology workflows. Several key areas are outlined in the radiation oncology workflow where machine learning has been applied and where it can have a significant impact in terms of efficiency, consistency in treatment and overall treatment outcomes. This review highlights that machine learning has key early applications in radiation oncology due to the repetitive nature of many tasks that also currently have human review. Standardized data management of routinely collected imaging and radiation dose data are also highlighted as enabling engagement in research utilizing machine learning and the ability integrate these technologies into clinical workflow to benefit patients. Physicists need to be part of the conversation to facilitate this technical integration.

Preoperative Prediction of Metastasis for Ovarian Cancer Based on Computed Tomography Radiomics Features and Clinical Factors

Background

There is urgent need for an accurate preoperative prediction of metastatic status to optimize treatment for patients with ovarian cancer (OC). The feasibility of predicting the metastatic status based on radiomics features from preoperative computed tomography (CT) images alone or combined with clinical factors were investigated.

Methods

A total of 101 OC patients who underwent primary debulking surgery were enrolled. Radiomics features were extracted from the tumor volumes contoured on CT images with LIFEx package. Mann-Whitney U tests, least absolute shrinkage selection operator (LASSO), and Ridge Regression were applied to select features and to build prediction models. Univariate and regression analysis were applied to select clinical factors for metastatic prediction. The performance of models generated with radiomics features alone, clinical factors, and combined factors were evaluated and compared.

Results

Nine radiomics features were screened out from 184 extracted features to classify patients with and without metastasis. Age and cancer antigen 125 (CA125) were the two clinical factors that were associated with metastasis. The area under curves (AUCs) for the radiomics signature, clinical factors model, and combined model were 0.82 (95% CI, 0.66-0.98; sensitivity = 0.90, specificity = 0.70), 0.83 (95% CI, 0.67-0.95; sensitivity = 0.71, specificity = 0.8), and 0.86 (95% CI, 0.72-0.99, sensitivity = 0.81, specificity = 0.8), respectively.

Conclusions

Radiomics features alone or radiomics features combined with clinical factors are feasible and accurate enough to predict the metastatic status for OC patients.

Modeling-Based Decision Support System for Radical Prostatectomy Versus External Beam Radiotherapy for Prostate Cancer Incorporating an In Silico Clinical Trial and a Cost-Utility Study

Simple Summary

Low–intermediate prostate cancer has a number of viable treatment options, such as radical prostatectomy and radiotherapy, with similar survival outcomes but different treatment-related side effects. The aim of this study is to facilitate patient-specific treatment selection by developing a decision support system (DSS) that incorporates predictive models for cancer-free survival and treatment-related side effects. We challenged this DSS by validating it against randomized clinical trials and assessing the benefit through a cost–utility analysis. We aim to expand upon the applications of this DSS by using it as the basis for an in silico clinical trial for an underrepresented patient group. This modeling study shows that DSS-based treatment decisions will result in a clinically relevant increase in the patients’ quality of life and can be used for in silico trials.

Abstract

The aim of this study is to build a decision support system (DSS) to select radical prostatectomy (RP) or external beam radiotherapy (EBRT) for low- to intermediate-risk prostate cancer patients. We used an individual state-transition model based on predictive models for estimating tumor control and toxicity probabilities. We performed analyses on a synthetically generated dataset of 1000 patients with realistic clinical parameters, externally validated by comparison to randomized clinical trials, and set up an in silico clinical trial for elderly patients. We assessed the cost-effectiveness (CE) of the DSS for treatment selection by comparing it to randomized treatment allotment. Using the DSS, 47.8% of synthetic patients were selected for RP and 52.2% for EBRT. During validation, differences with the simulations of late toxicity and biochemical failure never exceeded 2%. The in silico trial showed that for elderly patients, toxicity has more influence on the decision than TCP, and the predicted QoL depends on the initial erectile function. The DSS is estimated to result in cost savings (EUR 323 (95% CI: EUR 213–433)) and more quality-adjusted life years (QALYs; 0.11 years, 95% CI: 0.00–0.22) than randomized treatment selection.

Deciphering the glioblastoma phenotype by computed tomography radiomics

INTRODUCTION

Glioblastoma (GBM) is the most common malignant primary brain tumour which has, despite extensive treatment, a median overall survival of 15 months. Radiomics is the high-throughput extraction of large amounts of image features from radiographic images, which allows capturing the tumour phenotype in 3D and in a non-invasive way. In this study we assess the prognostic value of CT radiomics for overall survival in patients with a GBM.

MATERIALS AND METHODS

Clinical data and pre-treatment CT images were obtained from 218 patients diagnosed with a GBM via biopsy who underwent radiotherapy +/- temozolomide between 2004 and 2015 treated at three independent institutes (n = 93, 62 and 63). A clinical prognostic score (CPS), a simple radiomics model consisting of volume based score (VPS), a complex radiomics prognostic score (RPS) and a combined clinical and radiomics (C + R)PS model were developed. The population was divided into three risk groups for each prognostic score and respective Kaplan-Meier curves were generated.

RESULTS

Patient characteristics were broadly comparable. Clinically significant differences were observed with regards to radiation dose, tumour volume and performance status between datasets. Image acquisition parameters differed between institutes. The cross-validated c-indices were moderately discriminative and for the CPS ranged from 0.63 to 0.65; the VPS c-indices ranged between 0.52 and 0.61; the RPS c-indices ranged from 0.57 to 0.64 and the combined clinical and radiomics model resulted in c-indices of 0.59-0.71.

CONCLUSION

In this study clinical and CT radiomics features were used to predict OS in GBM. Discrimination between low-, middle- and high-risk patients based on the combined clinical and radiomics model was comparable to previous MRI-based models.

Impact of feature selection methods and subgroup factors on prognostic analysis with CT-based radiomics in non-small cell lung cancer patients

Background

Radiomics is a new technology to noninvasively predict survival prognosis with quantitative features extracted from medical images. Most radiomics-based prognostic studies of non-small-cell lung cancer (NSCLC) patients have used mixed datasets of different subgroups. Therefore, we investigated the radiomics-based survival prediction of NSCLC patients by focusing on subgroups with identical characteristics.

Methods

A total of 304 NSCLC (Stages I–IV) patients treated with radiotherapy in our hospital were used. We extracted 107 radiomic features (i.e., 14 shape features, 18 first-order statistical features, and 75 texture features) from the gross tumor volume drawn on the free breathing planning computed tomography image. Three feature selection methods [i.e., test–retest and multiple segmentation (FS1), Pearson's correlation analysis (FS2), and a method that combined FS1 and FS2 (FS3)] were used to clarify how they affect survival prediction performance. Subgroup analysis for each histological subtype and each T stage applied the best selection method for the analysis of All data. We used a least absolute shrinkage and selection operator Cox regression model for all analyses and evaluated prognostic performance using the concordance-index (C-index) and the Kaplan–Meier method. For subgroup analysis, fivefold cross-validation was applied to ensure model reliability.

Results

In the analysis of All data, the C-index for the test dataset is 0.62 (FS1), 0.63 (FS2), and 0.62 (FS3). The subgroup analysis indicated that the prediction model based on specific histological subtypes and T stages had a higher C-index for the test dataset than that based on All data (All data, 0.64 vs. SCC_all, 060; ADC_all, 0.69; T1, 0.68; T2, 0.65; T3, 0.66; T4, 0.70). In addition, the prediction models unified for each T stage in histological subtype showed a different trend in the C-index for the test dataset between ADC-related and SCC-related models (ADC_T1–ADC_T4, 0.72–0.83; SCC_T1–SCC_T4, 0.58–0.71).

Conclusions

Our results showed that feature selection methods moderately affected the survival prediction performance. In addition, prediction models based on specific subgroups may improve the prediction performance. These results may prove useful for determining the optimal radiomics-based predication model.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13014-021-01810-9.

Predicting Radiotherapy Impact on Late Bladder Toxicity in Prostate Cancer Patients: An Observational Study

BACKGROUND AND PURPOSE

The aim of our study was to elaborate a suitable model on bladder late toxicity in prostate cancer (PC) patients treated by radiotherapy with volumetric technique.

MATERIALS AND METHODS

PC patients treated between September 2010 and April 2017 were included in the analysis. An observational study was performed collecting late toxicity data of any grade, according to RTOG and CTCAE 4.03 scales, cumulative dose volumes histograms were exported for each patient. Vdose, the value of dose to a specific volume of organ at risk (OAR), impact was analyzed through the Mann-Whitney rank-sum test. Logistic regression was used as the final model. The model performance was estimated by taking 1000 samples with replacement from the original dataset and calculating the AUC average. In addition, the calibration plot (Hosmer-Lemeshow goodness-of-fit test) was used to evaluate the performance of internal validation. RStudio Software version 3.3.1 and an in house developed software package "Moddicom" were used.

RESULTS

Data from 175 patients were collected. The median follow-up was 39 months (min-max 3.00-113.00). We performed Mann-Whitney rank-sum test with continuity correction in the subset of patients with late bladder toxicity grade ≥ 2: a statistically significant p-value with a Vdose of 51.43 Gy by applying a logistic regression model (coefficient 4.3, p value 0.025) for the prediction of the development of late G ≥ 2 GU toxicity was observed. The performance for the model's internal validation was evaluated, with an AUC equal to 0.626. Accuracy was estimated through the elaboration of a calibration plot.

CONCLUSIONS

Our preliminary results could help to optimize treatment planning procedures and customize treatments.

Biology-guided radiotherapy: redefining the role of radiotherapy in metastatic cancer

The emerging biological understanding of metastatic cancer and proof-of-concept clinical trials suggest that debulking all gross disease holds great promise for improving patient outcomes. However, ablation of multiple targets with conventional external beam radiotherapy systems is burdensome, which limits investigation and utilization of complete metastatic ablation in the majority of patients with advanced disease. To overcome this logistical hurdle, technical innovation is necessary. Biology-guided radiotherapy (BgRT) is a new external beam radiotherapy delivery modality combining positron emission tomography-computed tomography (PET-CT) with a 6 MV linear accelerator. The key innovation is continuous response of the linear accelerator to outgoing tumor PET emissions with beamlets of radiotherapy at subsecond latency. This allows the deposited dose to track tumors in real time. Multiple new hardware and algorithmic advances further facilitate this low-latency feedback process. By transforming tumors into their own fiducials after intravenous injection of a radiotracer, BgRT has the potential to enable complete metastatic ablation in a manner efficient for a single patient and scalable to entire populations with metastatic disease. Future trends may further enhance the utility of BgRT in the clinic as this technology dovetails with other innovations in radiotherapy, including novel dose painting and fractionation schemes, radiomics, and new radiotracers.

Identification of patterns of tumour change measured on CBCT images in NSCLC patients during radiotherapy

In this study, we propose a novel approach to investigate changes in the visible tumour and surrounding tissues with the aim of identifying patterns of tumour change during radiotherapy (RT) without segmentation on the follow-up images. On-treatment cone-beam computed tomography (CBCT) images of 240 non-small cell lung cancer (NSCLC) patients who received 55 Gy of RT were included. CBCTs were automatically aligned onto planning computed tomography (planning CT) scan using a two-step rigid registration process. To explore density changes across the lung-tumour boundary, eight shells confined to the shape of the gross tumour volume (GTV) were created. The shells extended 6 mm inside and outside of the GTV border, and each shell is 1.5 mm thick. After applying intensity correction on CBCTs, the mean intensity was extracted from each shell across all CBCTs. Thereafter, linear fits were created, indicating density change over time in each shell during treatment. The slopes of all eight shells were clustered to explore patterns in the slopes that show how tumours change. Seven clusters were obtained, 97% of the patients were clustered into three groups. After visual inspection, we found that these clusters represented patients with little or no density change, progression and regression. For the three groups, the survival curves were not significantly different between the groups, p-value = 0.51. However, the results show that definite patterns of tumour change exist, suggesting that it may be possible to identify patterns of tumour changes from on-treatment CBCT images.

Radiomic Analysis of CT Predicts Tumor Response in Human Lung Cancer with Radiotherapy

PURPOSE

Radiomics features can be positioned to monitor changes throughout treatment. In this study, we evaluated machine learning for predicting tumor response by analyzing CT images of lung cancer patients treated with radiotherapy.

EXPERIMENTAL DESIGN

For this retrospective study, screening or standard diagnostic CT images were collected for 100 patients (mean age, 67 years; range, 55-82 years; 64 men [mean age, 68 years; range, 55-82 years] and 36 women [mean age, 65 years; range, 60-72 years]) from two institutions between 2013 and 2017. Radiomics analysis was available for each patient. Features were pruned to train machine learning classifiers with 50 patients, then trained in the test dataset.

RESULT

A support vector machine classifier with 2 radiomic features (flatness and coefficient of variation) achieved an area under the receiver operating characteristic curve (AUC) of 0.91 on the test set.

CONCLUSION

The 2 radiomic features, flatness, and coefficient of variation, from the volume of interest of lung tumor, can be the biomarkers for predicting tumor response at CT.

Learning health care systems: Highly needed but challenging

BACKGROUND

Learning health care systems (LHSs) have the potential to transform health care. However, this transformation process faces significant challenges.

MATERIALS AND METHODS

Based on proposals and early examples of LHSs in the literature and conceptual analysis of the LHS mission, we provide four models with distinct organizational and ethical implications that may facilitate the transformation.

RESULTS

An LHS could be developed in the following ways: by taking away practical impediments that prevent patients and professionals from engaging in scientific research (model 1: optimization LHS); by routinely analyzing observational data from electronic health records and other sources (model 2: comprehensive data LHS); by making clinical decisions based on the outcomes of the aforementioned data analyses and directly evaluating the outcomes in order to continuously improve decision-making (model 3: real-time LHS); or by embedding clinical trials into routine care delivery (model 4: full LHS).

CONCLUSIONS

Each model has different ethical implications for consent and oversight. Also, the four-model approach shows that reorganizing a health care center into an LHS is not an all-or-nothing decision. Rather, it is a choice from a menu of possibilities. Instead of discussing the advantages and disadvantages of the LHS menu in its entirety, the medical community should focus on the designs and ethical aspects of each of the separate options.

Decision Support Systems in Oncology

Precision medicine is the future of health care: please watch the animation at https://vimeo.com/241154708. As a technology-intensive and -dependent medical discipline, oncology will be at the vanguard of this impending change. However, to bring about precision medicine, a fundamental conundrum must be solved: Human cognitive capacity, typically constrained to five variables for decision making in the context of the increasing number of available biomarkers and therapeutic options, is a limiting factor to the realization of precision medicine. Given this level of complexity and the restriction of human decision making, current methods are untenable. A solution to this challenge is multifactorial decision support systems (DSSs), continuously learning artificial intelligence platforms that integrate all available data—clinical, imaging, biologic, genetic, cost—to produce validated predictive models. DSSs compare the personalized probable outcomes—toxicity, tumor control, quality of life, cost effectiveness—of various care pathway decisions to ensure optimal efficacy and economy. DSSs can be integrated into the workflows both strategically (at the multidisciplinary tumor board level to support treatment choice, eg, surgery or radiotherapy) and tactically (at the specialist level to support treatment technique, eg, prostate spacer or not). In some countries, the reimbursement of certain treatments, such as proton therapy, is already conditional on the basis that a DSS is used. DSSs have many stakeholders—clinicians, medical directors, medical insurers, patient advocacy groups—and are a natural consequence of big data in health care. Here, we provide an overview of DSSs, their challenges, opportunities, and capacity to improve clinical decision making, with an emphasis on the utility in oncology.

Radiotherapy treatment for lung cancer: Current status and future directions

Radiotherapy is an important modality used for the treatment of lung cancer. Seventy-seven percent of all patients with lung cancer have an evidence-based indication for radiotherapy, although it is often underutilized. Radiotherapy can be used as curative or palliative treatment across all stages of disease. Technological advances have allowed better radiotherapy targeting of tumours and reduced incidental irradiation of surrounding normal tissues. This has expanded the indications for radiotherapy in lung cancer and improved outcomes both in terms of increasing survival and reducing toxicity. This review examines the current role of radiotherapy in lung cancer, discusses the evidence behind this and identifies future directions in the radiotherapy treatment of lung cancer.

Updating Photon-Based Normal Tissue Complication Probability Models for Pneumonitis in Patients With Lung Cancer Treated With Proton Beam Therapy

PURPOSE

No validated models for predicting the risk of radiation pneumonitis (RP) with proton beam therapy (PBT) currently exist. Our goal was to externally validate and recalibrate multiple established photon-based normal tissue complication probability models for RP in a cohort with locally advanced nonsmall cell lung cancer treated with contemporary doses of chemoradiation using PBT.

METHODS AND MATERIALS

The external validation cohort consisted of 99 consecutive patients with locally advanced nonsmall cell lung cancer treated with chemoradiation using PBT. RP was retrospectively scored at 3 and 6 months posttreatment. We evaluated the performance of the photon Quantitative Analyses of Normal Tissue Effects in the Clinic (QUANTEC) pneumonitis model, the QUANTEC model adjusted for clinical risk factors, and the newer Netherlands updated QUANTEC model. A closed testing procedure was performed to test the need for model updating, either by recalibration-in-the-large (re-estimation of intercept), recalibration (re-estimation of intercept/slope), or model revision (re-estimation of all coefficients).

RESULTS

There were 21 events (21%) of ≥grade 2 RP. The closed testing procedure on the PBT data set did not detect major deviations between the models and the data and recommended adjustment of the intercept only for the photon-based Netherlands updated QUANTEC model (intercept update: -1.2). However, an update of the slope and revision of the model coefficients were not recommended by the closed testing procedure, as the deviations were not significant within the power of the data.

CONCLUSIONS

The similarity between the dose-response relationship for PBT and photons for normal tissue complications has been an assumption until now. We demonstrate that the preexisting, widely used photon based models fit our PBT data well with minor modifications. These now-validated and updated normal tissue complication probability models can aid in individualizing selection of the most optimal treatment technique for a particular patient.

Needs with Regard to Decision Support Systems for Treating Patients with Incurable Non-small Cell Lung Cancer

Treatment decision-making for patients with incurable non-small cell lung cancer (NSCLC) is complex due to the rapidly increasing number of treatments and discovery of new biomarkers. Decision support systems (DSS) could assist thoracic oncologists (TO) weighing of the pros and cons of treatments in order to arrive at an evidence-based and personalized treatment advice. Our aim is to inventory (1) TO's needs with regard to DSS in the treatment of incurable (stage IIIB/IV) NSCLC patients, and (2) preferences regarding the development of future tools in this field. We disseminated an online inventory questionnaire among all members of the Section of Oncology within the Society of Physicians in Chest Medicine and Tuberculosis. Telephone interviews were conducted to better contextualize the findings from the questionnaire. In total, 58 TO completed the questionnaire and expressed a need for new DSS. They reported that it is important for tools to include genetic and immune markers, to be sufficiently validated, regularly updated, and time-efficient. Also, future DSS should incorporate multiple treatment options, integrate estimates of toxicity, quality of life and cost-effectiveness of treatments, enhance communication between caregivers and patients, and use IT solutions for a clear interface and continuous updating of tools. With this inventory among Dutch TO, we summarized the need for new DSS to aid treatment decision-making for patients with incurable NSCLC. To meet the expressed needs, substantial additional efforts will be required by DSS developers, above already existing tools.

Cone-beam CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients

Background

Stereotactic body radiotherapy (SBRT) is the standard care for inoperable early stage non‐small cell lung cancer (NSCLC). The purpose of our study was to investigate whether a prediction model based on cone‐beam CT (CBCT) plus pretreatment CT radiomics features could improve the prediction of tumor control and lung toxicity after SBRT in comparison to a model based on pretreatment CT radiomics features alone.

Methods

A total of 34 cases of stage I NSCLC patients who received SBRT were included in the study. The pretreatment planning CT and serial CBCT radiomics features were analyzed using the imaging biomarker explorer (IBEX) software platform. Multivariate logistic regression was conducted for the association between progression‐free survival (PFS), lung toxicity and features. The predictive capabilities of the models based on CBCT and CT features were compared using receiver operating characteristic (ROC) curves.

Results

Five CBCT features and two planning CT features were correlated with disease progression. Six CBCT features and two planning CT features were related to lung injury. The ROC curves indicated that the model based on the CBCT plus planning CT features might be better than the model based on the planning CT features in predicting lung injury. The other ROC curves indicated that the model based on the planning CT features was similar to the model based on the CBCT plus planning CT features in predicting disease progression.

Conclusions

Both pretreatment CT and CBCT radiomics features could predict disease progression and lung injury. A model with CBCT plus pretreatment CT radiomics features might improve the prediction of lung toxicity in comparison with a model with pretreatment CT features alone.

Key points

Significant findings of the study: A model with cone‐beam CT radiomics features plus pre‐treatment CT radiomics features might improve the prediction of lung toxicity after SBRT in stage I NSCLC patients.

What this study adds: In the prediction of PFS and lung toxicity in early‐stage NSCLC patients treated with SBRT, CBCT radiomics could be another effective method.

Biological Determinants of Chemo-Radiotherapy Response in HPV-Negative Head and Neck Cancer: A Multicentric External Validation

Purpose: Tumor markers that are related to hypoxia, proliferation, DNA damage repair and stem cell-ness, have a prognostic value in advanced stage HNSCC patients when assessed individually. Here we aimed to evaluate and validate this in a multifactorial context and assess interrelation and the combined role of these biological factors in determining chemo-radiotherapy response in HPV-negative advanced HNSCC. Methods: RNA sequencing data of pre-treatment biopsy material from 197 HPV-negative advanced stage HNSCC patients treated with definitive chemoradiotherapy was analyzed. Biological parameter scores were assigned to patient samples using previously generated and described gene expression signatures. Locoregional control rates were used to assess the role of these biological parameters in radiation response and compared to distant metastasis data. Biological factors were ranked according to their clinical impact using bootstrapping methods and multivariate Cox regression analyses that included clinical variables. Multivariate Cox regression analyses comprising all biological variables were used to define their relative role among all factors when combined. Results: Only few biomarker scores correlate with each other, underscoring their independence. The different biological factors do not correlate or cluster, except for the two stem cell markers CD44 and SLC3A2 (r = 0.4, p < 0.001) and acute hypoxia prediction scores which correlated with T-cell infiltration score, CD8⁺ T cell abundance and proliferation scores (r = 0.52, 0.56, and 0.6, respectively with p < 0.001). Locoregional control association analyses revealed that chronic (Hazard Ratio (HR) = 3.9) and acute hypoxia (HR = 1.9), followed by stem cell-ness (CD44/SLC3A2; HR = 2.2/2.3), were the strongest and most robust determinants of radiation response. Furthermore, multivariable analysis, considering other biological and clinical factors, reveal a significant role for EGFR expression (HR = 2.9, p < 0.05) and T-cell infiltration (CD8⁺T-cells: HR = 2.2, p < 0.05; CD8⁺T-cells/Treg: HR = 2.6, p < 0.01) signatures in locoregional control of chemoradiotherapy-treated HNSCC. Conclusion: Tumor acute and chronic hypoxia, stem cell-ness, and CD8⁺ T-cell parameters are relevant and largely independent biological factors that together contribute to locoregional control. The combined analyses illustrate the additive value of multifactorial analyses and support a role for EGFR expression analysis and immune cell markers in addition to previously validated biomarkers. This external validation underscores the relevance of biological factors in determining chemoradiotherapy outcome in HNSCC.

External Validation of Radiation-Induced Dyspnea Models on Esophageal Cancer Radiotherapy Patients

Purpose: Radiation-induced lung disease (RILD), defined as dyspnea in this study, is a risk for patients receiving high-dose thoracic irradiation. This study is a TRIPOD (Transparent Reporting of A Multivariable Prediction Model for Individual Prognosis or Diagnosis) Type 4 validation of previously-published dyspnea models via secondary analysis of esophageal cancer SCOPE1 trial data. We quantify the predictive performance of these two models for predicting the maximal dyspnea grade ≥ 2 within 6 months after the end of high-dose chemo-radiotherapy for primary esophageal cancer.

Materials and methods: We tested the performance of two previously published dyspnea risk models using baseline, treatment and follow-up data on 258 esophageal cancer patients in the UK enrolled into the SCOPE1 multi-center trial. The tested models were developed from lung cancer patients treated at MAASTRO Clinic (The Netherlands) from the period 2002 to 2011. The adverse event of interest was dyspnea ≥ Grade 2 (CTCAE v3) within 6 months after the end of radiotherapy. As some variables were missing randomly and cannot be imputed, 212 patients in the SCOPE1 were used for validation of model 1 and 255 patients were used for validation of model 2. The model parameter Forced Expiratory Volume in 1 s (FEV₁), as a predictor to both validated models, was imputed using the WHO performance status. External validation was performed using an automated, decentralized approach, without exchange of individual patient data.

Results: Out of 258 patients with esophageal cancer in SCOPE1 trial data, 38 patients (14.7%) developed radiation-induced dyspnea (≥ Grade 2) within 6 months after chemo-radiotherapy. The discrimination performance of the models in esophageal cancer patients treated with high-dose external beam radiotherapy was moderate, area under curve (AUC) of 0.68 (95% CI 0.55–0.76) and 0.70 (95% CI 0.58–0.77), respectively. The curves and AUCs derived by distributed learning were identical to the results from validation on a local host.

Conclusion: We have externally validated previously published dyspnea models using an esophageal cancer dataset. FEV₁ that is not routinely measured for esophageal cancer was imputed using WHO performance status. Prediction performance was not statistically different from previous training and validation sets. Risk estimates were dominated by WHO score in Model 1 and baseline dyspnea in Model 2. The distributed learning approach gave the same answer as local processing, and could be performed without accessing a validation site's individual patients-level data.

Discovery of stable and prognostic CT-based radiomic features independent of contrast administration and dimensionality in oesophageal cancer

The aim of this work was to investigate radiomic analysis of contrast and non-contrast enhanced planning CT images of oesophageal cancer (OC) patients in terms of stability, dimensionality and contrast agent dependency. The prognostic significance of CT-based radiomic features was also evaluated. Different 2D and 3D radiomic features were extracted from contrast and non-contrast enhanced CT images of 213 patients from the multi-centre SCOPE1 randomised controlled trial (RCT) in OC. Feature stability was evaluated by randomly dividing patients into three groups and identifying textures with similar distributions among groups with a Kruskal-Wallis analysis. A paired two-sided Wilcoxon signed rank test was used to assess for significant differences in the remaining corresponding 2D and 3D stable features. A prognostic model was constructed using clinical characteristics and remaining filtered features. The discriminative ability of significant variables was tested using Kaplan-Meier analysis. A total of 238 2D and 3D radiomic features were computed from oesophageal CT images. More than 75 features were stable if extracted from homogeneous cohort (contrast or non-contrast enhanced CT images) and inhomogeneous cohort (contrast and non-contrast enhanced CT images). Among the remaining corresponding stable features computed from both cohorts, only 4 features did not show a statistically significant difference if obtained in 2D or in 3D (p-value < 0.05). A Cox regression model constructed using 5 clinical variables (age, sex, tumour, node and metastasis (TNM) stage, WHO performance status and contrast administration) and 4 radiomic variables (inverse varianceGLCM, large distance emphasisGLDZM, zone distance non uniformity normGLDZM, zone distance varianceGLDZM), identified one radiomic feature (zone distance varianceGLDZM) that was significantly associated with overall survival (p-value = 0.032, HR = 1.25, 95% CI = 1.02-1.52). A significant difference in overall survival between groups was found when considering a threshold of zone distance varianceGLDZM equals to 1.70 (X2 = 7.692, df = 1, p-value = 0.006). Zone distance varianceGLDZM was identified as the only stable CT radiomic feature statistically correlated with overall survival, independent of dimensionality and contrast administration. This feature was able to identify high-risk patients and if validated, could be the subject of a future clinical trial aiming to improve clinical decision making and personalise OC treatment.

Real-time presurgical resting-state fMRI in patients with brain tumors: Quality control and comparison with task-fMRI and intraoperative mapping

Resting-state functional magnetic resonance imaging (rsfMRI) is a promising task-free functional imaging approach, which may complement or replace task-based fMRI (tfMRI) in patients who have difficulties performing required tasks. However, rsfMRI is highly sensitive to head movement and physiological noise, and validation relative to tfMRI and intraoperative electrocortical mapping is still necessary. In this study, we investigate (a) the feasibility of real-time rsfMRI for presurgical mapping of eloquent networks with monitoring of data quality in patients with brain tumors and (b) rsfMRI localization of eloquent cortex compared with tfMRI and intraoperative electrocortical stimulation (ECS) in retrospective analysis. Five brain tumor patients were studied with rsfMRI and tfMRI on a clinical 3T scanner using MultiBand(8)-echo planar imaging (EPI) with repetition time: 400 ms. Moving-averaged sliding-window correlation analysis with regression of motion parameters and signals from white matter and cerebrospinal fluid was used to map sensorimotor and language resting-state networks. Data quality monitoring enabled rapid optimization of scan protocols, early identification of task noncompliance, and head movement-related false-positive connectivity to determine scan continuation or repetition. Sensorimotor and language resting-state networks were identifiable within 1 min of scan time. The Euclidean distance between ECS and rsfMRI connectivity and task-activation in motor cortex, Broca's, and Wernicke's areas was 5-10 mm, with the exception of discordant rsfMRI and ECS localization of Wernicke's area in one patient due to possible cortical reorganization and/or altered neurovascular coupling. This study demonstrates the potential of real-time high-speed rsfMRI for presurgical mapping of eloquent cortex with real-time data quality control, and clinically acceptable concordance of rsfMRI with tfMRI and ECS localization.

The association between preoperative physical functioning and short-term postoperative outcomes: a cohort study of patients undergoing elective hepatic resection

BACKGROUND

This study evaluated the association between practical performance-based indices of preoperative physical functioning and short-term postoperative outcomes in patients undergoing hepatic resection.

METHOD

Preoperative characteristics and results of practical performance-based tests of physical functioning were analyzed concerning the effect on postoperative outcomes (recovery of physical functioning, non-surgical complications, and length of hospital stay) using univariable and multivariable logistic regression.

RESULTS

Perioperative data of 96 patients showed that besides the conventional risk-factors (American Society of Anesthesiologists grade III and BMI), lower absolute steep ramp test performance (in watts; OR 0.992), and lower perceived level of functional capacity to perform activities of daily living (ADL) on Duke activity status index (in metabolic equivalent of task (MET); OR 0.806) and lower score on the veterans-specific activity questionnaire (in MET, OR 0.875) were associated with delayed recovery of physical functioning. Furthermore, more comorbidities, worse functional mobility, and lower levels of perceived functional capacity to perform ADL were associated with non-surgical complications and length of hospital stay.

CONCLUSION

Adequate preoperative performance and perceived level of functional capacity to perform ADL appear to be of importance to identify individual patients that are at risk of a complicated postoperative course.

Optimal modality selection in external beam radiotherapy

The goal in external beam radiotherapy (EBRT) for cancer is to maximize damage to the tumour while limiting toxic effects on the organs-at-risk. EBRT can be delivered via different modalities such as photons, protons and neutrons. The choice of an optimal modality depends on the anatomy of the irradiated area and the relative physical and biological properties of the modalities under consideration. There is no single universally dominant modality. We present the first-ever mathematical formulation of the optimal modality selection problem. We show that this problem can be tackled by solving the Karush-Kuhn-Tucker conditions of optimality, which reduce to an analytically tractable quartic equation. We perform numerical experiments to gain insights into the effect of biological and physical properties on the choice of an optimal modality or combination of modalities.

Development and validation of a patient decision aid for prostate Cancer therapy: from paternalistic towards participative shared decision making

BACKGROUND

Patient decision aids (PDAs) can support the treatment decision making process and empower patients to take a proactive role in their treatment pathway while using a shared decision-making (SDM) approach making participatory medicine possible. The aim of this study was to develop a PDA for prostate cancer that is accurate and user-friendly.

METHODS

We followed a user-centered design process consisting of five rounds of semi-structured interviews and usability surveys with topics such as informational/decisional needs of users and requirements for PDAs. Our user-base consisted of 8 urologists, 4 radiation oncologists, 2 oncology nurses, 8 general practitioners, 19 former prostate cancer patients, 4 usability experts and 11 healthy volunteers.

RESULTS

Informational needs for patients centered on three key factors: treatment experience, post-treatment quality of life, and the impact of side effects. Patients and clinicians valued a PDA that presents balanced information on these factors through simple understandable language and visual aids. Usability questionnaires revealed that patients were more satisfied overall with the PDA than clinicians; however, both groups had concerns that the PDA might lengthen consultation times (42 and 41%, respectively). The PDA is accessible on http://beslissamen.nl/ .

CONCLUSIONS

User-centered design provided valuable insights into PDA requirements but challenges in integrating diverse perspectives as clinicians focus on clinical outcomes while patients also consider quality of life. Nevertheless, it is crucial to involve a broad base of clinical users in order to better understand the decision-making process and to develop a PDA that is accurate, usable, and acceptable.

Computed Tomography-based Radiomics for Risk Stratification in Prostate Cancer

PURPOSE

To explore the role of Computed tomography (CT)-based radiomics features in prostate cancer risk stratification.

METHODS AND MATERIALS

The study population consisted of 506 patients with prostate cancer collected from a clinically annotated database. After applying exclusion criteria, 342 patients were included in the final analysis. CT-based radiomics features were extracted from planning CT scans for prostate gland-only structure, and machine learning was used to train models for Gleason score (GS) and risk group (RG) classifications. Repeated cross-validation was used. The discriminatory performance of the developed models was assessed using receiver operating characteristic area under the curve (AUC) analysis.

RESULTS

Classifiers using CT-based radiomics features distinguished between GS ≤ 6 versus GS ≥ 7 with AUC = 0.90 and GS 7(3 + 4) versus GS 7(4 + 3) with AUC = 0.98. Developed classifiers also showed excellent performance in distinguishing low versus high RG (AUC = 0.96) and low versus intermediate RG (AUC = 1.00), but poorer performance was observed for GS 7 versus GS > 7 (AUC = 0.69). An overall modest performance was observed for validation on holdout data sets with the highest AUC of 0.75 for classifiers of low versus high RG and an AUC of 0.70 for GS 7 versus GS > 7.

CONCLUSIONS

Our results show that radiomics features from routinely acquired planning CT scans could provide insights into prostate cancer aggressiveness in a noninvasive manner. Assessing models on training data sets, the classifiers were especially accurate in discerning high-risk from low-risk patients and in classifying GS 7 versus GS > 7 and GS 7(3 + 4) versus G7(4 + 3); however, classifiers were less adept at distinguishing high RG versus intermediate RG. External validation and prospective studies are warranted to verify the presented findings. These findings could potentially guide targeted radiation therapy strategies in radical intent radiation therapy for prostate cancer.

Decision Support Systems in Prostate Cancer Treatment: An Overview

Background

A multifactorial decision support system (mDSS) is a tool designed to improve the clinical decision-making process, while using clinical inputs for an individual patient to generate case-specific advice. The study provides an overview of the literature to analyze current available mDSS focused on prostate cancer (PCa), in order to better understand the availability of decision support tools as well as where the current literature is lacking.

Methods

We performed a MEDLINE literature search in July 2018. We divided the included studies into different sections: diagnostic, which aids in detection or staging of PCa; treatment, supporting the decision between treatment modalities; and patient, which focusses on informing the patient. We manually screened and excluded studies that did not contain an mDSS concerning prostate cancer and study proposals.

Results

Our search resulted in twelve diagnostic mDSS; six treatment mDSS; two patient mDSS; and eight papers that could improve mDSS.

Conclusions

Diagnosis mDSS is well represented in the literature as well as treatment mDSS considering external-beam radiotherapy; however, there is a lack of mDSS for other treatment modalities. The development of patient decision aids is a new field of research, and few successes have been made for PCa patients. These tools can improve personalized medicine but need to overcome a number of difficulties to be successful and require more research.

Challenges and caveats of a multi-center retrospective radiomics study: an example of early treatment response assessment for NSCLC patients using FDG-PET/CT radiomics

Background

Prognostic models based on individual patient characteristics can improve treatment decisions and outcome in the future. In many (radiomic) studies, small size and heterogeneity of datasets is a challenge that often limits performance and potential clinical applicability of these models. The current study is example of a retrospective multi-centric study with challenges and caveats. To highlight common issues and emphasize potential pitfalls, we aimed for an extensive analysis of these multi-center pre-treatment datasets, with an additional ¹⁸F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography (PET/CT) scan acquired during treatment.

Methods

The dataset consisted of 138 stage II-IV non-small cell lung cancer (NSCLC) patients from four different cohorts acquired from three different institutes. The differences between the cohorts were compared in terms of clinical characteristics and using the so-called ‘cohort differences model’ approach. Moreover, the potential prognostic performances for overall survival of radiomic features extracted from CT or FDG-PET, or relative or absolute differences between the scans at the two time points, were assessed using the LASSO regression method. Furthermore, the performances of five different classifiers were evaluated for all image sets.

Results

The individual cohorts substantially differed in terms of patient characteristics. Moreover, the cohort differences model indicated statistically significant differences between the cohorts. Neither LASSO nor any of the tested classifiers resulted in a clinical relevant prognostic model that could be validated on the available datasets.

Conclusion

The results imply that the study might have been influenced by a limited sample size, heterogeneous patient characteristics, and inconsistent imaging parameters. No prognostic performance of FDG-PET or CT based radiomics models can be reported. This study highlights the necessity of extensive evaluations of cohorts and of validation datasets, especially in retrospective multi-centric datasets.

Reproducibility and non-redundancy of radiomic features extracted from arterial phase CT scans in hepatocellular carcinoma patients: impact of tumor segmentation variability

Background

The reproducibility and non-redundancy of radiomic features are challenges in accelerating the clinical translation of radiomics. In this study, we focused on the robustness and non-redundancy of radiomic features extracted from computed tomography (CT) scans in hepatocellular carcinoma (HCC) patients with respect to different tumor segmentation methods.

Methods

Arterial enhanced CT images were retrospectively randomly obtained from 106 patients. As a training data set, 26 HCC patients were used to calculate the features' reproducibility and redundancy. Another data set (55 HCC patients and 25 healthy volunteers) was used for classification. The GrowCut and GraphCut semiautomatic segmentation methods were implemented in 3D Slicer software by two independent observers, and manual delineation was performed by five abdominal radiation oncologists to acquire the gross tumor volume (GTV). Seventy-one radiomic features were extracted from GTVs using Imaging Biomarker Explorer (IBEX) software, including 17 tumor intensity statistical features, 16 shape features and 38 textural features. For each radiomic feature, intraclass correlation coefficient (ICC) and hierarchical clustering were used to quantify its reproducibility and redundancy. Features with ICC values greater than 0.75 were considered reproducible. To generate the number of non-redundancy feature subgroups, the R² statistic method was used. Then, a classification model was built using a support vector machine (SVM) algorithm with 10-fold cross validation, and area under ROC curve (AUC) was used to evaluate the utility of non-redundant feature extraction by hierarchical clustering.

Results

The percentages of excellent reproducible features in the manual delineation group, GraphCut and GrowCut segmentation group were 69% [49], 73% [52] and 79% [56], respectively. Sixty-five percent [46] of the features showed strong robustness for all segmentation methods. The optimal number of cluster subgroup were 9, 13 and 11 for manual delineation, GraphCut and GrowCut segmentation, respectively. The optimal cluster subgroup number was 6 for all groups when the collectively high reproducibility features were selected for clustering. The receiver operating characteristic (ROC) analysis of radiomics classification model with and without feature reduction for healthy liver and HCC had an AUC value of 0.857 and 0.721 respectively.

Conclusions

Our study demonstrates that variations exist in the reproducibility of quantitative imaging features extracted from tumor regions segmented using different methods. The reproducibility and non-redundancy of the radiomic features rely greatly on the tumor segmentation in HCC CT images. We recommend that the most reliable and uniform radiomic features should be selected in the clinical use of radiomics. Classification experiments with feature reduction showed that radiomic features were effective in identifying healthy liver and HCC.