Lung cancer diagnosis remains a critical challenge in personalized medicine, demanding novel approaches for efficient and accurate prediction. In this context, we propose the Spatially Optimized Lung Volume Evidence (SOLVE) framework, which is a novel lung malignancy prediction model developed by integrating principles from brain-inspired evidence accumulation and retina-inspired data processing. SOLVE introduces spatial scale optimization in Computed Tomography (CT) scan analysis, integrating evidence accumulation concepts to enhance decision-making. Mimicking the retina’s ability to process images across various spatial scales, SOLVE applies a series of filters and progressively captures features from coarse to fine details within each CT slice that may not be apparent when analyzed at a single resolution. Such an approach allows for a more discriminating feature representation, improving the richness of available information for analysis and reducing the reliance on large datasets. Addressing the challenge of limited medical image resources, SOLVE effectively decreases computational complexity via the use of its evidence-based mechanism. Through experiments conducted on an in-house dataset of 114 subjects, SOLVE demonstrated a marked improvement in prediction accuracy, outperforming traditional methods with far less training data.

Drawing inspiration from the primate brain’s intriguing evidence accumulation process, and guided by models from cognitive psychology and neuroscience, the paper introduces the NYCTALE framework, a neuro-inspired and evidence accumulation-based Transformer architecture. The proposed neuro-inspired NYCTALE offers a novel pathway in the domain of Personalized Medicine (PM) for lung cancer diagnosis. In nature, Nyctales are small owls known for their nocturnal behavior, hunting primarily during the darkness of night. The NYCTALE operates in a similarly vigilant manner, i.e., processing data in an evidence-based fashion and making predictions dynamically/adaptively. Distinct from conventional Computed Tomography (CT)-based Deep Learning (DL) models, the NYCTALE performs predictions only when sufficient amount of evidence is accumulated. In other words, instead of processing all or a pre-defined subset of CT slices, for each person, slices are provided one at a time. The NYCTALE framework then computes an evidence vector associated with contribution of each new CT image. A decision is made once the total accumulated evidence surpasses a specific threshold. Preliminary experimental analyses conducted using a challenging in-house dataset comprising 114 subjects. The results are noteworthy, suggesting that NYCTALE outperforms the benchmark accuracy even with approximately $60 \%$ less training data on this demanding and small dataset.

The paper proposes a novel hybrid discovery Radiomics framework that simultaneously integrates temporal and spatial features extracted from non-thin chest Computed Tomography (CT) slices to predict Lung Adenocarcinoma (LUAC) malignancy with minimum expert involvement. Lung cancer is the leading cause of mortality from cancer worldwide and has various histologic types, among which LUAC has recently been the most prevalent. LUACs are classified as pre-invasive, minimally invasive, and invasive adenocarcinomas. Timely and accurate knowledge of the lung nodules malignancy leads to a proper treatment plan and reduces the risk of unnecessary or late surgeries. Currently, chest CT scan is the primary imaging modality to assess and predict the invasiveness of LUACs. However, the radiologists’ analysis based on CT images is subjective and suffers from a low accuracy compared to the ground truth pathological reviews provided after surgical resections. The proposed hybrid framework, referred to as the CAET-SWin, consists of two parallel paths: (i) The Convolutional Auto-Encoder (CAE) Transformer path that extracts and captures informative features related to inter-slice relations via a modified Transformer architecture, and; (ii) The Shifted Window (SWin) Transformer path, which is a hierarchical vision transformer that extracts nodules’ related spatial features from a volumetric CT scan. Extracted temporal (from the CAET path) and spatial (from the SWin path) are then fused through a fusion path to classify LUACs. Experimental results on our in-house dataset of 114 pathologically proven SubSolid Nodules (SSNs) demonstrate that the CAET-SWin significantly improves reliability of the invasiveness prediction task while achieving an accuracy of 82.65%, sensitivity of 83.66%, and specificity of 81.66% using 10-fold cross-validation.

The global outbreak of the novel corona virus (COVID-19) disease has drastically impacted the world and led to one of the most challenging crisis across the globe since World War II. The early diagnosis and isolation of COVID-19 positive cases are considered as crucial steps towards preventing the spread of the disease and flattening the epidemic curve. Chest Computed Tomography (CT) scan is a highly sensitive, rapid, and accurate diagnostic technique that can complement Reverse Transcription Polymerase Chain Reaction (RT-PCR) test. Recently, deep learning-based models, mostly based on Convolutional Neural Networks (CNN), have shown promising diagnostic results. CNNs, however, are incapable of capturing spatial relations between image instances and require large datasets. Capsule Networks, on the other hand, can capture spatial relations, require smaller datasets, and have considerably fewer parameters. In this paper, a Capsule network framework, referred to as the "CT-CAPS", is presented to automatically extract distinctive features of chest CT scans. These features, which are extracted from the layer before the final capsule layer, are then leveraged to differentiate COVID-19 from Non-COVID cases. The experiments on our in-house dataset of 307 patients show the state-of-the-art performance with the accuracy of 90.8%, sensitivity of 94.5%, and specificity of 86.0%.

The unprecedented COVID-19 pandemic has been remarkably impacting the world and influencing a broad aspect of people’s lives since its first emergence in late 2019. The highly contagious nature of the COVID-19 has raised the necessity of developing deep learning-based diagnostic tools to identify the infected cases in the early stages. Recently, we proposed a fully-automated framework based on Capsule Networks, referred to as the CT-CAPS, to distinguish COVID-19 infection from normal and Community Acquired Pneumonia (CAP) cases using chest Computed Tomography (CT) scans. Although CT scans can provide a comprehensive illustration of the lung abnormalities, COVID-19 lung manifestations highly overlap with the CAP findings making their identification challenging even for experienced radiologists. Here, the CT-CAPS is augmented with a wide range of clinical/demographic data, including patients’ gender, age, weight and symptoms. More specifically, we propose a hybrid deep learning model that utilizes both clinical/demographic data and CT scans to classify COVID-19 and non-COVID cases using a Random Forest Classifier. The proposed hybrid model specifies the most important predictive factors increasing the explainability of the model. The experimental results show that the proposed hybrid model improves the CT-CAPS performance, achieving accuracy of 90.8%, sensitivity of 94.5% and specificity of 86.0%.

Predicting death in lung cancer patients before initiating treatment is of paramount importance as this may guide decision-making towards more aggressive or combination of different types of treatment. In this work, we propose a Multi-scale Deep learning-based Radiomics model, referred to as "MDR-SURV" that exploits the information from positron emission tomography/computed tomography (PET/CT) images, combined with other clinical factors, to predict the overall survival (OS). Deep learning-based radiomics has the advantage of learning what features to extract, on its own. Furthermore, it does not require the exact segmentation of the tumor. The proposed MDR-SURV, which is a multi-scale framework, incorporates the tumor region and its surroundings, from different scales, and can extract both local and global tumor features. PET/CT images of 132 lung cancer patients who underwent stereotactic body radiotherapy (SBRT) were used to predict OS with the proposed model. Our results show that the MDR-SURV model outperforms its single-scale counterparts in predicting OS. Furthermore, the proposed MDR-SURV model achieves significantly high concordance index (C-index) of 73% in predicting the OS, which is noticeably higher than the results reported in existing literature.