Stargazing through the lens of AI in clinical oncology

Digital pathology-based artificial intelligence models for differential diagnosis and prognosis of sporadic odontogenic keratocysts

Odontogenic keratocyst (OKC) is a common jaw cyst with a high recurrence rate. OKC combined with basal cell carcinoma as well as skeletal and other developmental abnormalities is thought to be associated with Gorlin syndrome. Moreover, OKC needs to be differentiated from orthokeratinized odontogenic cyst and other jaw cysts. Because of the different prognosis, differential diagnosis of several cysts can contribute to clinical management. We collected 519 cases, comprising a total of 2 157 hematoxylin and eosin-stained images, to develop digital pathology-based artificial intelligence (AI) models for the diagnosis and prognosis of OKC. The Inception_v3 neural network was utilized to train and test models developed from patch-level images. Finally, whole slide image-level AI models were developed by integrating deep learning-generated pathology features with several machine learning algorithms. The AI models showed great performance in the diagnosis (AUC = 0.935, 95% CI: 0.898-0.973) and prognosis (AUC = 0.840, 95%CI: 0.751-0.930) of OKC. The advantages of multiple slides model for integrating of histopathological information are demonstrated through a comparison with the single slide model. Furthermore, the study investigates the correlation between AI features generated by deep learning and pathological findings, highlighting the interpretative potential of AI models in the pathology. Here, we have developed the robust diagnostic and prognostic models for OKC. The AI model that is based on digital pathology shows promise potential for applications in odontogenic diseases of the jaw.

Validation of Artificial Intelligence (AI)-Assisted Flow Cytometry Analysis for Immunological Disorders

Flow cytometry is a vital diagnostic tool for hematologic and immunologic disorders, but manual analysis is prone to variation and time-consuming. Over the last decade, artificial intelligence (AI) has advanced significantly. In this study, we developed and validated an AI-assisted flow cytometry workflow using 379 clinical cases from 2021, employing a 3-tube, 10-color flow panel with 21 antibodies for primary immunodeficiency diseases and related immunological disorders. The AI software (DeepFlow™, version 2.1.1) is fully automated, reducing analysis time to under 5 min per case. It interacts with hematopatholoists for manual gating adjustments when necessary. Using proprietary multidimensional density-phenotype coupling algorithm, the AI model accurately classifies and enumerates T, B, and NK cells, along with important immune cell subsets, including CD4+ helper T cells, CD8+ cytotoxic T cells, CD3+/CD4-/CD8- double-negative T cells, and class-switched or non-switched B cells. Compared to manual analysis with hematopathologist-determined lymphocyte subset percentages as the gold standard, the AI model exhibited a strong correlation (r > 0.9) across lymphocyte subsets. This study highlights the accuracy and efficiency of AI-assisted flow cytometry in diagnosing immunological disorders in a clinical setting, providing a transformative approach within a concise timeframe.

Unlocking the Power of Benchmarking: Real-World-Time Data Analysis for Enhanced Sarcoma Patient Outcomes

Benchmarking is crucial for healthcare providers to enhance quality and efficiency, notably for complex conditions like sarcomas. Multidisciplinary teams/sarcoma boards (MDT/SBs) are vital in sarcoma management, but differences in their processes can affect patient outcomes and treatment costs, despite adherence to international guidelines. To address this issue, this study aimed to compare two MDT/SBs and establish an interoperable digital platform, Sarconnector®, for real-time-world data assessment and automated analysis. The study included 983 patients, 46.0% of whom female, with a median age of 58 years, and 4.5% of patients presented with metastasis at diagnosis. Differences were observed in the number of first-time presentations, follow-up presentations, primary sarcomas, biopsies and chemotherapy indications between the two MDT/SB. The results highlight the importance of benchmarking and utilizing a harmonized data approach, such as the RWT approach provided by the Sarconnector®, to standardize and evaluate quality and cost metrics. By identifying areas of improvement and making data-driven decisions on the meta-level, healthcare providers can optimize resources and improve patient outcomes. In conclusion, benchmarking with the RWT harmonized data approach provided by the Sarconnector® can help healthcare providers improve the overall effectiveness of the healthcare system and achieve better outcomes for their patients in terms of both outcomes and costs.

Challenging gold standard hematology diagnostics through the introduction of whole genome sequencing and artificial intelligence

The diagnosis of hematological malignancies is rather complex and requires the application of a plethora of different assays, techniques and methodologies. Some of the methods, like cytomorphology, have been in use for decades, while other methods, such as next-generation sequencing or even whole genome sequencing (WGS), are relatively new. The application of the methods and the evaluation of the results require distinct skills and knowledge and place different demands on the practitioner. However, even with experienced hematologists, diagnostic ambiguity remains a regular occurrence and the comprehensive analysis of high-dimensional WGS data soon exceeds any human's capacity. Hence, in order to reduce inter-observer variability and to improve the timeliness and accuracy of diagnoses, machine learning based approaches have been developed to assist in the decision making process. Moreover, to achieve the goal of precision oncology, comprehensive genomic profiling is increasingly being incorporated into routine standard of care.

Artificial intelligence in hematological diagnostics: Game changer or gadget?

The future of clinical diagnosis and treatment of hematologic diseases will inevitably involve the integration of artificial intelligence (AI)-based systems into routine practice to support the hematologists' decision making. Several studies have shown that AI-based models can already be used to automatically differentiate cells, reliably detect malignant cell populations, support chromosome banding analysis, and interpret clinical variants, contributing to early disease detection and prognosis. However, even the best tool can become useless if it is misapplied or the results are misinterpreted. Therefore, in order to comprehensively judge and correctly apply newly developed AI-based systems, the hematologist must have a basic understanding of the general concepts of machine learning. In this review, we provide the hematologist with a comprehensive overview of various machine learning techniques, their current implementations and approaches in different diagnostic subfields (e.g., cytogenetics, molecular genetics), and the limitations and unresolved challenges of the systems.

Lung cancer subtype diagnosis using weakly-paired multi-omics data

MOTIVATION

Cancer subtype diagnosis is crucial for its precise treatment and different subtypes need different therapies. Although the diagnosis can be greatly improved by fusing multiomics data, most fusion solutions depend on paired omics data, which are actually weakly paired, with different omics views missing for different samples. Incomplete multiview learning-based solutions can alleviate this issue but are still far from satisfactory because they: (i) mainly focus on shared information while ignore the important individuality of multiomics data and (ii) cannot pick out interpretable features for precise diagnosis.

RESULTS

We introduce an interpretable and flexible solution (LungDWM) for Lung cancer subtype Diagnosis using Weakly paired Multiomics data. LungDWM first builds an attention-based encoder for each omics to pick out important diagnostic features and extract shared and complementary information across omics. Next, it proposes an individual loss to jointly extract the specific information of each omics and performs generative adversarial learning to impute missing omics of samples using extracted features. After that, it fuses the extracted and imputed features to diagnose cancer subtypes. Experiments on benchmark datasets show that LungDWM achieves a better performance than recent competitive methods, and has a high authenticity and good interpretability.

AVAILABILITY AND IMPLEMENTATION

The code is available at http://www.sdu-idea.cn/codes.php?name=LungDWM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.