The prognostic role of end-of-treatment FDG-PET/CT in diffuse large B cell lymphoma: a pilot study application of neural networks to predict time-to-event

PURPOSE

To evaluate the prognostic role of end-of-treatment (EoT) FDG-PET/CT parameters in diffuse large B cell lymphoma (DLBCL), and then to explore a pilot application of Neural Networks (NN) in predicting time-to-relapse.

METHODS

For conventional survival analysis, parameters as Deauville score (DS) and quantitative extension of DS (qPET) were correlated to adverse events as relapse or progression in the follow-up. To build NN and conventional multi-regression models (MM) for time-to-event prediction, patients with residual FDG uptake (DS ≥ 2) and an adverse event were divided into a training and a test group. Models developed on the training group were evaluated in the test group. Pearson correlation coefficient (R) and mean relative error between observed and forecasted time-to-event were calculated.

RESULTS

FDG-PET/CT data of 308 patients with DLBCL were analyzed. DS and qPET were prognostic factors in conventional univariate analysis. Positive and negative predictive values, respectively, were 55% and 83% for DS 4-5, 89% and 82% for positive qPET. Focusing on 37 relapsed patients with a residual FDG uptake, R between observed and forecasted time-to-event was of 0.63 in the NN model and 0.49 in the MM. Mean relative error in predicting time-to-event was of 58% for NN and 67% for MM.

CONCLUSIONS

EoT FDG-PET/CT visual score (DS) is a strong outcome predictor in DLBCL in a large monocentric cohort. The semi-quantitative parameter qPET may increase this prognostic performance. A pilot NN model applied on residual FDG uptake parameters seems to predict time-to-event in the follow-up.

Artificial intelligence and hybrid imaging: the best match for personalized medicine in oncology

Artificial intelligence (AI) refers to a field of computer science aimed to perform tasks typically requiring human intelligence. Currently, AI is recognized in the broader technology radar within the five key technologies which emerge for their wide-ranging applications and impact in communities, companies, business, and value chain framework alike. However, AI in medical imaging is at an early phase of development, and there are still hurdles to take related to reliability, user confidence, and adoption. The present narrative review aimed to provide an overview on AI-based approaches (distributed learning, statistical learning, computer-aided diagnosis and detection systems, fully automated image analysis tool, natural language processing) in oncological hybrid medical imaging with respect to clinical tasks (detection, contouring and segmentation, prediction of histology and tumor stage, prediction of mutational status and molecular therapies targets, prediction of treatment response, and outcome). Particularly, AI-based approaches have been briefly described according to their purpose and, finally lung cancer-being one of the most extensively malignancy studied by hybrid medical imaging-has been used as illustrative scenario. Finally, we discussed clinical challenges and open issues including ethics, validation strategies, effective data-sharing methods, regulatory hurdles, educational resources, and strategy to facilitate the interaction among different stakeholders. Some of the major changes in medical imaging will come from the application of AI to workflow and protocols, eventually resulting in improved patient management and quality of life. Overall, several time-consuming tasks could be automatized. Machine learning algorithms and neural networks will permit sophisticated analysis resulting not only in major improvements in disease characterization through imaging, but also in the integration of multiple-omics data (i.e., derived from pathology, genomic, proteomics, and demographics) for multi-dimensional disease featuring. Nevertheless, to accelerate the transition of the theory to practice a sustainable development plan considering the multi-dimensional interactions between professionals, technology, industry, markets, policy, culture, and civil society directed by a mindset which will allow talents to thrive is necessary.

A sparse representation-based radiomics for outcome prediction of higher grade gliomas

PURPOSE

Accurately predicting outcome (i.e., overall survival (OS) time) for higher grade glioma (HGG) has great clinical value and would provide optimized guidelines for treatment planning. Radiomics focuses on revealing underlying pathophysiological information in biomedical images for disease analysis and demonstrates promising prognostic clinical performance. In this paper, we propose a novel sparse representation-based radiomics framework to predict if HGG patients would have long or short OS time.

METHODS

First, taking advantages of the scale invariant feature transform (SIFT) feature in image characterizing, we developed a sparse representation-based method to convert a local SIFT descriptor into a global tumor feature. Next, because preserving sample structure is beneficial for feature selection, we proposed a locality preserving projection and sparse representation-combined feature selection method to select more discriminative features for tumor classification. Finally, we employed a multifeature collaborative sparse representation classification to combine the information of multimodal images to classify OS time.

RESULTS

Three experiments were performed on the two datasets provided by different institutions. Specifically, the proposed model was trained and independently tested on dataset 1 (135 subjects), on dataset 2 (86 subjects), and on the combination of dataset 1 and dataset 2, respectively. Experimental results demonstrated that the proposed method achieved encouraging prediction performance, exhibiting a testing accuracy of 93.33% on dataset 1 (one modality), 92.31% on dataset 2 (two modalities), and 87.93% on the combined dataset (one modality).

CONCLUSIONS

The sparse representation theory provides reasonable solutions to feature extraction, feature selection, and classification for radiomics. This study provides a promising tool to enhance the prediction performance of HGG patient's outcome.