Prediction of survival outcome based on clinical features and pretreatment 18FDG-PET/CT for HNSCC patients

Novel ensemble feature selection techniques applied to high-grade gastroenteropancreatic neuroendocrine neoplasms for the prediction of survival

BACKGROUND AND OBJECTIVE

Determining the most informative features for predicting the overall survival of patients diagnosed with high-grade gastroenteropancreatic neuroendocrine neoplasms is crucial to improve individual treatment plans for patients, as well as the biological understanding of the disease. The main objective of this study is to evaluate the use of modern ensemble feature selection techniques for this purpose with respect to (a) quantitative performance measures such as predictive performance, (b) clinical interpretability, and (c) the effect of integrating prior expert knowledge.

METHODS

The Repeated Elastic Net Technique for Feature Selection (RENT) and the User-Guided Bayesian Framework for Feature Selection (UBayFS) are recently developed ensemble feature selectors investigated in this work. Both allow the user to identify informative features in datasets with low sample sizes and focus on model interpretability. While RENT is purely data-driven, UBayFS can integrate expert knowledge a priori in the feature selection process. In this work, we compare both feature selectors on a dataset comprising 63 patients and 110 features from multiple sources, including baseline patient characteristics, baseline blood values, tumor histology, imaging, and treatment information.

RESULTS

Our experiments involve data-driven and expert-driven setups, as well as combinations of both. In a five-fold cross-validated experiment without expert knowledge, our results demonstrate that both feature selectors allow accurate predictions: A reduction from 110 to approximately 20 features (around 82%) delivers near-optimal predictive performances with minor variations according to the choice of the feature selector, the predictive model, and the fold. Thereafter, we use findings from clinical literature as a source of expert knowledge. In addition, expert knowledge has a stabilizing effect on the feature set (an increase in stability of approximately 40%), while the impact on predictive performance is limited.

CONCLUSIONS

The features WHO Performance Status, Albumin, Platelets, Ki-67, Tumor Morphology, Total MTV, Total TLG, and SUVmax are the most stable and predictive features in our study. Overall, this study demonstrated the practical value of feature selection in medical applications not only to improve quantitative performance but also to deliver potentially new insights to experts.

A systematic review and meta-analysis of predictive and prognostic models for outcome prediction using positron emission tomography radiomics in head and neck squamous cell carcinoma patients

BACKGROUND

Positron emission tomography (PET) images of head and neck squamous cell carcinoma (HNSCC) patients can assess the functional and biochemical processes at cellular levels. Therefore, PET radiomics-based prediction and prognostic models have the potentials to understand tumour heterogeneity and assist clinicians with diagnosis, prognosis and management of the disease. We conducted a systematic review of published modelling information to evaluate the usefulness of PET radiomics in the prediction and prognosis of HNSCC patients.

METHODS

We searched bibliographic databases (MEDLINE, Embase, Web of Science) from 2010 to 2021 and considered 31 studies with pre-defined inclusion criteria. We followed the CHARMS checklist for data extraction and performed quality assessment using the PROBAST tool. We conducted a meta-analysis to estimate the accuracy of the prediction and prognostic models using the diagnostic odds ratio (DOR) and average C-statistic, respectively.

RESULTS

Manual segmentation method followed by 40% of the maximum standardised uptake value (SUVmax ) thresholding is a commonly used approach. The area under the receiver operating curves of externally validated prediction models ranged between 0.60-0.87, 0.65-0.86 and 0.62-0.75 for overall survival, distant metastasis and recurrence, respectively. Most studies highlighted an overall high risk of bias (outcome definition, statistical methodologies and external validation of models) and high unclear concern in terms of applicability. The meta-analysis showed the estimated pooled DOR of 6.75 (95% CI: 4.45, 10.23) for prediction models and the C-statistic of 0.71 (95% CI: 0.67, 0.74) for prognostic models.

CONCLUSIONS

Both prediction and prognostic models using clinical variables and PET radiomics demonstrated reliable accuracy for detecting adverse outcomes in HNSCC, suggesting the prospect of PET radiomics in clinical settings for diagnosis, prognosis and management of HNSCC patients. Future studies of prediction and prognostic models should emphasise the quality of reporting, external model validation, generalisability to real clinical scenarios and enhanced reproducibility of results.

Prognostic value of initial [ 18 F]FDG PET/computed tomography volumetric and texture analysis-based parameters in patients with head and neck squamous cell carcinoma

AIM OF WORK

To determine the predictive value of initial [ 18 F]FDG PET/computed tomography (CT) volumetric and radiomics-derived analyses in patients with head and neck squamous cell carcinoma (HNSCC).

METHODS

Forty-six adult patients had pathologically proven HNSCC and underwent pretherapy [ 18 F]FDG PET/CT were enrolled. Semi-quantitative PET-derived volumetric [(maximum standardized uptake value (SUVmax) and mean SUV (SUVmean), total lesion glycolysis (TLG) and metabolic tumor volume (MTV)] and radiomics analyses using LIFEx 6.73.3 software were performed.

RESULTS

In the current study group, the receiver operating characteristic curve marked a cutoff point of 21.105 for primary MTV with area under the curve (AUC) of 0.727, sensitivity of 62.5%, and specificity of 86.8% ( P value 0.041) to distinguish responders from non-responders, while no statistically significant primary SUVmean or max or primary TLG cut off points could be determined. It also marked the cutoff point for survival prediction of 10.845 for primary MTV with AUC 0.728, sensitivity of 80%, and specificity of 77.8% ( P value 0.026). A test of the synergistic performance of PET-derived volumetric and textural features significant parameters was conducted in an attempt to develop the most accurate and stable prediction model. Therefore, multivariate logistic regression analysis was performed to detect independent predictors of mortality. With a high specificity of 97.1% and an overall accuracy of 89.1%, the combination of primary tumor MTV and the textural feature gray-level co-occurrence matrix correlation provided the most accurate prediction of mortality ( P value < 0.001).

CONCLUSION

Textural feature indices are a noninvasive method for capturing intra-tumoral heterogeneity. In our study, a PET-derived prediction model was successfully generated with high specificity and accuracy.

Survival prediction of heart failure patients using motion-based analysis method

BACKGROUND AND OBJECTIVE

Survival prediction of heart failure patients is critical to improve the prognostic management of the cardiovascular disease. The existing survival prediction methods focus on the clinical information while lacking the cardiac motion information. we propose a motion-based analysis method to predict the survival risk of heart failure patients for aiding clinical diagnosis and treatment.

METHODS

We propose a motion-based analysis method for survival prediction of heart failure patients. First, our method proposes the hierarchical spatial-temporal structure to capture the myocardial border. It promotes the model discrimination on border features. Second, our method explores the dense optical flow structure to capture motion fields. It improves the tracking capability on cardiac images. The cardiac motion information is obtained by fusing boundary information and motion fields of cardiac images. Finally, our method proposes the multi-modality deep-cox structure to predict the survival risk of heart failure patients. It improves the survival probability of heart failure patients.

RESULTS

The motion-based analysis method is confirmed to be able to improve the survival prediction of heart failure patients. The precision, recall, F1-score, and C-index are 0.8519, 0.8333, 0.8425, and 0.8478, respectively, which is superior to other state-of-the-art methods.

CONCLUSIONS

The experimental results show that the proposed model can effectively predict survival risk of heart failure patients. It facilitates the application of robust clinical treatment strategies.

Testing the Ability of Convolutional Neural Networks to Learn Radiomic Features

BACKGROUND AND OBJECTIVE

Radiomics and deep learning have emerged as two distinct approaches to medical image analysis. However, their relative expressive power remains largely unknown. Theoretically, hand-crafted radiomic features represent a mere subset of features that neural networks can approximate, thus making deep learning a more powerful approach. On the other hand, automated learning of hand-crafted features may require a prohibitively large number of training samples. Here we directly test the ability of convolutional neural networks (CNNs) to learn and predict the intensity, shape, and texture properties of tumors as defined by standardized radiomic features.

METHODS

Conventional 2D and 3D CNN architectures with an increasing number of convolutional layers were trained to predict the values of 16 standardized radiomic features from real and synthetic PET images of tumors, and tested. In addition, several ImageNet-pretrained advanced networks were tested. A total of 4000 images were used for training, 500 for validation, and 500 for testing.

RESULTS

Features quantifying size and intensity were predicted with high accuracy, while shape irregularity and heterogeneity features had very high prediction errors and generalized poorly. For example, mean normalized prediction error of tumor diameter with a 5-layer CNN was 4.23 ± 0.25, while the error for tumor sphericity was 15.64 ± 0.93. We additionally found that learning shape features required an order of magnitude more samples compared to intensity and size features.

CONCLUSIONS

Our findings imply that CNNs trained to perform various image-based clinical tasks may generally under-utilize the shape and texture information that is more easily captured by radiomics. We speculate that to improve the CNN performance, shape and texture features can be computed explicitly and added as auxiliary variables to the networks, or supplied as synthetic inputs.

Radiomics in Oncological PET Imaging: A Systematic Review-Part 1, Supradiaphragmatic Cancers

Radiomics is an upcoming field in nuclear oncology, both promising and technically challenging. To summarize the already undertaken work on supradiaphragmatic neoplasia and assess its quality, we performed a literature search in the PubMed database up to 18 February 2022. Inclusion criteria were: studies based on human data; at least one specified tumor type; supradiaphragmatic malignancy; performing radiomics on PET imaging. Exclusion criteria were: studies only based on phantom or animal data; technical articles without a clinically oriented question; fewer than 30 patients in the training cohort. A review database containing PMID, year of publication, cancer type, and quality criteria (number of patients, retrospective or prospective nature, independent validation cohort) was constructed. A total of 220 studies met the inclusion criteria. Among them, 119 (54.1%) studies included more than 100 patients, 21 studies (9.5%) were based on prospectively acquired data, and 91 (41.4%) used an independent validation set. Most studies focused on prognostic and treatment response objectives. Because the textural parameters and methods employed are very different from one article to another, it is complicated to aggregate and compare articles. New contributions and radiomics guidelines tend to help improving quality of the reported studies over the years.

A Systematic Review of PET Textural Analysis and Radiomics in Cancer

Background: Although many works have supported the utility of PET radiomics, several authors have raised concerns over the robustness and replicability of the results. This study aimed to perform a systematic review on the topic of PET radiomics and the used methodologies. Methods: PubMed was searched up to 15 October 2020. Original research articles based on human data specifying at least one tumor type and PET image were included, excluding those that apply only first-order statistics and those including fewer than 20 patients. Each publication, cancer type, objective and several methodological parameters (number of patients and features, validation approach, among other things) were extracted. Results: A total of 290 studies were included. Lung (28%) and head and neck (24%) were the most studied cancers. The most common objective was prognosis/treatment response (46%), followed by diagnosis/staging (21%), tumor characterization (18%) and technical evaluations (15%). The average number of patients included was 114 (median = 71; range 20–1419), and the average number of high-order features calculated per study was 31 (median = 26, range 1–286). Conclusions: PET radiomics is a promising field, but the number of patients in most publications is insufficient, and very few papers perform in-depth validations. The role of standardization initiatives will be crucial in the upcoming years.