Artificial intelligence for oral squamous cell carcinoma detection based on oral photographs: A comprehensive literature review.

INTRODUCTION: Oral squamous cell carcinoma (OSCC) presents a significant global health challenge. The integration of artificial intelligence (AI) and computer vision holds promise for the early detection of OSCC through the analysis of digitized oral photographs. This literature review explores the landscape of AI-driven OSCC automatic detection, assessing both the performance and limitations of the current state of the art. MATERIALS AND METHODS: An electronic search using several data base was conducted, and a systematic review performed in accordance with PRISMA guidelines (CRD42023441416). RESULTS: Several studies have demonstrated remarkable results for this task, consistently achieving sensitivity rates exceeding 85% and accuracy rates surpassing 90%, often encompassing around 1000 images. The review scrutinizes these studies, shedding light on their methodologies, including the use of recent machine learning and pattern recognition approaches coupled with different supervision strategies. However, comparing the results from different papers is challenging due to variations in the datasets used. DISCUSSION: Considering these findings, this review underscores the urgent need for more robust and reliable datasets in the field of OSCC detection. Furthermore, it highlights the potential of advanced techniques such as multi-task learning, attention mechanisms, and ensemble learning as crucial tools in enhancing the accuracy and sensitivity of OSCC detection through oral photographs. CONCLUSION: These insights collectively emphasize the transformative impact of AI-driven approaches on early OSCC diagnosis, with the potential to significantly improve patient outcomes and healthcare practices.

Pembrolizumab with Capox Bevacizumab in patients with microsatellite stable metastatic colorectal cancer and a high immune infiltrate: The FFCD 1703-POCHI trial.

Pembrolizumab, a PD1 immune checkpoint inhibitor (ICI), was recently reported to be very effective in patients with microsatellite instable/deficient mismatch repair metastatic colorectal cancer (MSI/dMMR mCRC), unlike patients with microsatellite stable/proficient MMR (MSS/pMMR) mCRC, in whom ICIs are generally ineffective. However, about 15% of MSS/pMMR CRCs are highly infiltrated by tumour infiltrating lymphocytes. In addition, both oxaliplatin and bevacizumab have been shown to have immunomodulatory properties that may increase the efficacy of an ICI. We formulated the hypothesis that patients with MSS/pMMR mCRC with a high immune infiltrate can be sensitive to ICI plus oxalipatin and bevacizumab-based chemotherapy. POCHI is a multicenter, open-label, single-arm phase II trial to evaluate efficacy of Pembrolizumab with Capox Bevacizumab as first-line treatment of MSS/pMMR mCRC with a high immune infiltrate for which we plan to enrol 55 patients. Primary endpoint is progression-free survival (PFS) at 10 months, which is expected greater than 50%, but a 70% rate is hoped for. Main secondary objectives are overall survival, secondary resection rate and depth of response. Patients must have been resected of their primary tumour so as to evaluate two different immune scores (Immunoscore® and TuLIS) and are eligible if one score is "high". The first patient was included on April 20, 2021.

Relevance feedback for enhancing content based image retrieval and automatic prediction of semantic image features: Application to bone tumor radiographs.

BACKGROUND: The majority of current medical CBIR systems perform retrieval based only on "imaging signatures" generated by extracting pixel-level quantitative features, and only rarely has a feedback mechanism been incorporated to improve retrieval performance. In addition, current medical CBIR approaches do not routinely incorporate semantic terms that model the user's high-level expectations, and this can limit CBIR performance. METHOD: We propose a retrieval framework that exploits a hybrid feature space (HFS) that is built by integrating low-level image features and high-level semantic terms, through rounds of relevance feedback (RF) and performs similarity-based retrieval to support semi-automatic image interpretation. The novelty of the proposed system is that it can impute the semantic features of the query image by reformulating the query vector representation in the HFS via user feedback. We implemented our framework as a prototype that performs the retrieval over a database of 811 radiographic images that contains 69 unique types of bone tumors. RESULTS: We evaluated the system performance by conducting independent reading sessions with two subspecialist musculoskeletal radiologists. For the test set, the proposed retrieval system at fourth RF iteration of the sessions conducted with both the radiologists achieved mean average precision (MAP) value ∼0.90 where the initial MAP with baseline CBIR was 0.20. In addition, we also achieved high prediction accuracy (>0.8) for the majority of the semantic features automatically predicted by the system. CONCLUSION: Our proposed framework addresses some limitations of existing CBIR systems by incorporating user feedback and simultaneously predicting the semantic features of the query image. This obviates the need for the user to provide those terms and makes CBIR search more efficient for inexperience users/trainees. Encouraging results achieved in the current study highlight possible new directions in radiological image interpretation employing semantic CBIR combined with relevance feedback of visual similarity.

Directional Enlacement Histograms for the Description of Complex Spatial Configurations between Objects.

The analysis of spatial relations between objects in digital images plays a crucial role in various application domains related to pattern recognition and computer vision. Classical models for the evaluation of such relations are usually sufficient for the handling of simple objects, but can lead to ambiguous results in more complex situations. In this article, we investigate the modeling of spatial configurations where the objects can be imbricated in each other. We formalize this notion with the term enlacement, from which we also derive the term interlacement, denoting a mutual enlacement of two objects. Our main contribution is the proposition of new relative position descriptors designed to capture the enlacement and interlacement between two-dimensional objects. These descriptors take the form of circular histograms allowing to characterize spatial configurations with directional granularity, and they highlight useful invariance properties for typical image understanding applications. We also show how these descriptors can be used to evaluate different complex spatial relations, such as the surrounding of objects. Experimental results obtained in the different application domains of medical imaging, document image analysis and remote sensing, confirm the genericity of this approach.

Tree leaves extraction in natural images: comparative study of preprocessing tools and segmentation methods.

In this paper, we propose a comparative study of various segmentation methods applied to the extraction of tree leaves from natural images. This study follows the design of a mobile application, developed by Cerutti et al. (published in ReVeS Participation--Tree Species Classification Using Random Forests and Botanical Features. CLEF 2012), to highlight the impact of the choices made for segmentation aspects. All the tests are based on a database of 232 images of tree leaves depicted on natural background from smartphones acquisitions. We also propose to study the improvements, in terms of performance, using preprocessing tools, such as the interaction between the user and the application through an input stroke, as well as the use of color distance maps. The results presented in this paper shows that the method developed by Cerutti et al. (denoted Guided Active Contour), obtains the best score for almost all observation criteria. Finally, we detail our online benchmark composed of 14 unsupervised methods and 6 supervised ones.

Connected filtering based on multivalued component-trees.

In recent papers, a new notion of component-graph was introduced. It extends the classical notion of component-tree initially proposed in mathematical morphology to model the structure of gray-level images. Component-graphs can indeed model the structure of any-gray-level or multivalued-images. We now extend the antiextensive filtering scheme based on component-trees, to make it tractable in the framework of component-graphs. More precisely, we provide solutions for building a component-graph, reducing it based on selection criteria, and reconstructing a filtered image from a reduced component-graph. In this paper, we first consider the cases where component-graphs still have a tree structure; they are then called multivalued component-trees. The relevance and usefulness of such multivalued component-trees are illustrated by applicative examples on hierarchically classified remote sensing images.

On combining image-based and ontological semantic dissimilarities for medical image retrieval applications.

Computer-assisted image retrieval applications can assist radiologists by identifying similar images in archives as a means to providing decision support. In the classical case, images are described using low-level features extracted from their contents, and an appropriate distance is used to find the best matches in the feature space. However, using low-level image features to fully capture the visual appearance of diseases is challenging and the semantic gap between these features and the high-level visual concepts in radiology may impair the system performance. To deal with this issue, the use of semantic terms to provide high-level descriptions of radiological image contents has recently been advocated. Nevertheless, most of the existing semantic image retrieval strategies are limited by two factors: they require manual annotation of the images using semantic terms and they ignore the intrinsic visual and semantic relationships between these annotations during the comparison of the images. Based on these considerations, we propose an image retrieval framework based on semantic features that relies on two main strategies: (1) automatic "soft" prediction of ontological terms that describe the image contents from multi-scale Riesz wavelets and (2) retrieval of similar images by evaluating the similarity between their annotations using a new term dissimilarity measure, which takes into account both image-based and ontological term relations. The combination of these strategies provides a means of accurately retrieving similar images in databases based on image annotations and can be considered as a potential solution to the semantic gap problem. We validated this approach in the context of the retrieval of liver lesions from computed tomographic (CT) images and annotated with semantic terms of the RadLex ontology. The relevance of the retrieval results was assessed using two protocols: evaluation relative to a dissimilarity reference standard defined for pairs of images on a 25-images dataset, and evaluation relative to the diagnoses of the retrieved images on a 72-images dataset. A normalized discounted cumulative gain (NDCG) score of more than 0.92 was obtained with the first protocol, while AUC scores of more than 0.77 were obtained with the second protocol. This automatical approach could provide real-time decision support to radiologists by showing them similar images with associated diagnoses and, where available, responses to therapies.

Predicting visual semantic descriptive terms from radiological image data: preliminary results with liver lesions in CT.

We describe a framework to model visual semantics of liver lesions in CT images in order to predict the visual semantic terms (VST) reported by radiologists in describing these lesions. Computational models of VST are learned from image data using linear combinations of high-order steerable Riesz wavelets and support vector machines (SVM). In a first step, these models are used to predict the presence of each semantic term that describes liver lesions. In a second step, the distances between all VST models are calculated to establish a nonhierarchical computationally-derived ontology of VST containing inter-term synonymy and complementarity. A preliminary evaluation of the proposed framework was carried out using 74 liver lesions annotated with a set of 18 VSTs from the RadLex ontology. A leave-one-patient-out cross-validation resulted in an average area under the ROC curve of 0.853 for predicting the presence of each VST. The proposed framework is expected to foster human-computer synergies for the interpretation of radiological images while using rotation-covariant computational models of VSTs to 1) quantify their local likelihood and 2) explicitly link them with pixel-based image content in the context of a given imaging domain.

Automated tracking of quantitative assessments of tumor burden in clinical trials.

THERE ARE TWO KEY CHALLENGES HINDERING EFFECTIVE USE OF QUANTITATIVE ASSESSMENT OF IMAGING IN CANCER RESPONSE ASSESSMENT: 1) Radiologists usually describe the cancer lesions in imaging studies subjectively and sometimes ambiguously, and 2) it is difficult to repurpose imaging data, because lesion measurements are not recorded in a format that permits machine interpretation and interoperability. We have developed a freely available software platform on the basis of open standards, the electronic Physician Annotation Device (ePAD), to tackle these challenges in two ways. First, ePAD facilitates the radiologist in carrying out cancer lesion measurements as part of routine clinical trial image interpretation workflow. Second, ePAD records all image measurements and annotations in a data format that permits repurposing image data for analyses of alternative imaging biomarkers of treatment response. To determine the impact of ePAD on radiologist efficiency in quantitative assessment of imaging studies, a radiologist evaluated computed tomography (CT) imaging studies from 20 subjects having one baseline and three consecutive follow-up imaging studies with and without ePAD. The radiologist made measurements of target lesions in each imaging study using Response Evaluation Criteria in Solid Tumors 1.1 criteria, initially with the aid of ePAD, and then after a 30-day washout period, the exams were reread without ePAD. The mean total time required to review the images and summarize measurements of target lesions was 15% (P < .039) shorter using ePAD than without using this tool. In addition, it was possible to rapidly reanalyze the images to explore lesion cross-sectional area as an alternative imaging biomarker to linear measure. We conclude that ePAD appears promising to potentially improve reader efficiency for quantitative assessment of CT examinations, and it may enable discovery of future novel image-based biomarkers of cancer treatment response.

A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations.

Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances. Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images. However, most of the existing image retrieval strategies do not consider the intrinsic properties of these terms during the comparison of the images beyond treating them as simple binary (presence/absence) features. We propose a new framework that includes semantic features in images and that enables retrieval of similar images in large databases based on their semantic relations. It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms using the Hierarchical Semantic-Based Distance (HSBD) coupled to an ontological measure. The combination of these two steps provides a means of capturing the semantic correlations among the terms used to characterize the images that can be considered as a potential solution to deal with the semantic gap problem. We validate this approach in the context of the retrieval and the classification of 2D regions of interest (ROIs) extracted from computed tomographic (CT) images of the liver. Under this framework, retrieval accuracy of more than 0.96 was obtained on a 30-images dataset using the Normalized Discounted Cumulative Gain (NDCG) index that is a standard technique used to measure the effectiveness of information retrieval algorithms when a separate reference standard is available. Classification results of more than 95% were obtained on a 77-images dataset. For comparison purpose, the use of the Earth Mover's Distance (EMD), which is an alternative distance metric that considers all the existing relations among the terms, led to results retrieval accuracy of 0.95 and classification results of 93% with a higher computational cost. The results provided by the presented framework are competitive with the state-of-the-art and emphasize the usefulness of the proposed methodology for radiology image retrieval and classification.