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oncology Oncology

Artificial intelligence in oncology: chances and pitfalls.

In Journal of cancer research and clinical oncology

Artificial intelligence (AI) has been available in rudimentary forms for many decades. Early AI programs were successful in niche areas such as chess or handwriting recognition. However, AI methods had little practical impact on the practice of medicine until recently. Beginning around 2012, AI has emerged as an increasingly important tool in healthcare, and AI-based devices are now approved for clinical use. These devices are capable of processing image data, making diagnoses, and predicting biomarkers for solid tumors, among other applications. Despite this progress, the development of AI in medicine is still in its early stages, and there have been exponential technical advancements since 2022, with some AI programs now demonstrating human-level understanding of image and text data. In the past, technical advances have led to new medical applications with a delay of a few years. Therefore, now we might be at the beginning of a new era in which AI will become even more important in clinical practice. It is essential that this transformation is humane and evidence based, and physicians must take a leading role in ensuring this, particularly in hematology and oncology.

Kather Jakob Nikolas

2023-Mar-15

Artificial intelligence, Hematology, Large language models, Oncology

Radiology Radiology

An overview and a roadmap for artificial intelligence in hematology and oncology.

In Journal of cancer research and clinical oncology

BACKGROUND : Artificial intelligence (AI) is influencing our society on many levels and has broad implications for the future practice of hematology and oncology. However, for many medical professionals and researchers, it often remains unclear what AI can and cannot do, and what are promising areas for a sensible application of AI in hematology and oncology. Finally, the limits and perils of using AI in oncology are not obvious to many healthcare professionals.

METHODS : In this article, we provide an expert-based consensus statement by the joint Working Group on "Artificial Intelligence in Hematology and Oncology" by the German Society of Hematology and Oncology (DGHO), the German Association for Medical Informatics, Biometry and Epidemiology (GMDS), and the Special Interest Group Digital Health of the German Informatics Society (GI). We provide a conceptual framework for AI in hematology and oncology.

RESULTS : First, we propose a technological definition, which we deliberately set in a narrow frame to mainly include the technical developments of the last ten years. Second, we present a taxonomy of clinically relevant AI systems, structured according to the type of clinical data they are used to analyze. Third, we show an overview of potential applications, including clinical, research, and educational environments with a focus on hematology and oncology.

CONCLUSION : Thus, this article provides a point of reference for hematologists and oncologists, and at the same time sets forth a framework for the further development and clinical deployment of AI in hematology and oncology in the future.

Rösler Wiebke, Altenbuchinger Michael, Baeßler Bettina, Beissbarth Tim, Beutel Gernot, Bock Robert, von Bubnoff Nikolas, Eckardt Jan-Niklas, Foersch Sebastian, Loeffler Chiara M L, Middeke Jan Moritz, Mueller Martha-Lena, Oellerich Thomas, Risse Benjamin, Scherag André, Schliemann Christoph, Scholz Markus, Spang Rainer, Thielscher Christian, Tsoukakis Ioannis, Kather Jakob Nikolas

2023-Mar-15

Artificial intelligence, Computer vision, Digital health, Large language models, Machine learning

Surgery Surgery

Construction of an enhanced computed tomography radiomics model for non-invasively predicting granzyme A in head and neck squamous cell carcinoma by machine learning.

In European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery

PURPOSE : Classical prognostic indicators of head and neck squamous cell carcinoma (HNSCC) can no longer meet the clinical needs of precision medicine. This study aimed to establish a radiomics model to predict Granzyme A (GZMA) expression in patients with HNSCC.

METHODS : We downloaded transcriptomic data of HNSCC patients from The Cancer Genome Atlas for prognosis analysis and then used corresponding enhanced computed tomography (CT) images from The Cancer Imaging Archive for feature extraction and model construction. We explored the influence of differences in GZMA expression on signaling pathways and analyzed the potential molecular mechanism and its relationship with immune cell infiltration. Subsequently, non-invasive CT radiomics models were established to predict the expression of GZMA mRNA and evaluate the correlation with the radiomics-score (Rad-score), related genes, and prognosis.

RESULTS : We found that GZMA was highly expressed in tumor tissues, and high GZMA expression was a protective factor for overall survival. The degree of B and T lymphocyte and natural killer cell infiltration was significantly correlated with GZMA expression. The receiver operating characteristic curve showed that the Relief GBM and RFE_GBM radiomics models had good predictive ability, and there were significant differences in the Rad-score distribution between the high- and low-GZMA-expression groups.

CONCLUSIONS : GZMA expression can significantly affect the prognosis of patients with HNSCC. Enhanced CT radiomics models can effectively predict the expression of GZMA mRNA.

Hang Ren, Bai Guo, Sun Bin, Xu Peng, Sun Xiaofeng, Yan Guoxin, Zhang Wenhao, Wang Fang

2023-Mar-15

Clinical prognosis, Enhanced CT, GZMA, Head and neck squamous cell carcinoma, Radiomics

Pathology Pathology

1,2,4,5-Tetrazine-tethered probes for fluorogenically imaging superoxide in live cells with ultrahigh specificity.

In Nature communications ; h5-index 260.0

Superoxide (O2·-) is the primary reactive oxygen species in mammal cells. Detecting superoxide is crucial for understanding redox signaling but remains challenging. Herein, we introduce a class of activity-based sensing probes. The probes utilize 1,2,4,5-tetrazine as a superoxide-responsive trigger, which can be modularly tethered to various fluorophores to tune probe sensitivity and emission color. These probes afford ultra-specific and ultra-fluorogenic responses towards superoxide, and enable multiplexed imaging of various cellular superoxide levels in an organelle-resolved way. Notably, the probes reveal the aberrant superoxide generation in the pathology of myocardial ischemia/reperfusion injury, and facilitate the establishment of a high-content screening pipeline for mediators of superoxide homeostasis. One such identified mediator, coprostanone, is shown to effectively ameliorating oxidative stress-induced injury in mice with myocardial ischemia/reperfusion injury. Collectively, these results showcase the potential of 1,2,4,5-tetrazine-tethered probes as versatile tools to monitor superoxide in a range of pathophysiological settings.

Jiang Xuefeng, Li Min, Wang Yule, Wang Chao, Wang Yingchao, Shen Tianruo, Shen Lili, Liu Xiaogang, Wang Yi, Li Xin

2023-Mar-14

Dermatology Dermatology

Development and Clinical Evaluation of an Artificial Intelligence Support Tool for Improving Telemedicine Photo Quality.

In JAMA dermatology ; h5-index 54.0

IMPORTANCE : Telemedicine use accelerated during the COVID-19 pandemic, and skin conditions were a common use case. However, many images submitted may be of insufficient quality for making a clinical determination.

OBJECTIVE : To determine whether an artificial intelligence (AI) decision support tool, a machine learning algorithm, could improve the quality of images submitted for telemedicine by providing real-time feedback and explanations to patients.

DESIGN, SETTING, AND PARTICIPANTS : This quality improvement study with an AI performance component and single-arm clinical pilot study component was conducted from March 2020 to October 2021. After training, the AI decision support tool was tested on 357 retrospectively collected telemedicine images from Stanford telemedicine from March 2020 to June 2021. Subsequently, a single-arm clinical pilot study was conducted to assess feasibility with 98 patients in the Stanford Department of Dermatology across 2 clinical sites from July 2021 to October 2021. For the clinical pilot study, inclusion criteria for patients included being adults (aged ≥18 years), presenting to clinic for a skin condition, and being able to photograph their own skin with a smartphone.

INTERVENTIONS : During the clinical pilot study, patients were given a handheld smartphone device with a machine learning algorithm interface loaded and were asked to take images of any lesions of concern. Patients were able to review and retake photos prior to submitting, so each submitted photo met the patient's assumed standard of clinical acceptability. A machine learning algorithm then gave the patient feedback on whether the image was acceptable. If the image was rejected, the patient was provided a reason by the AI decision support tool and allowed to retake the photos.

MAIN OUTCOMES AND MEASURES : The main outcome of the retrospective image analysis was the receiver operator curve area under the curve (ROC-AUC). The main outcome of the clinical pilot study was the image quality difference between the baseline images and the images approved by AI decision support.

RESULTS : Of the 98 patients included, the mean (SD) age was 49.8 (17.6) years, and 50 (51%) of the patients were male. On retrospective telemedicine images, the machine learning algorithm effectively identified poor-quality images (ROC-AUC of 0.78) and the reason for poor quality (blurry ROC-AUC of 0.84; lighting issues ROC-AUC of 0.70). The performance was consistent across age and sex. In the clinical pilot study, patient use of the machine learning algorithm was associated with improved image quality. An AI algorithm was associated with reduction in the number of patients with a poor-quality image by 68.0%.

CONCLUSIONS AND RELEVANCE : In this quality improvement study, patients use of the AI decision support with a machine learning algorithm was associated with improved quality of skin disease photographs submitted for telemedicine use.

Vodrahalli Kailas, Ko Justin, Chiou Albert S, Novoa Roberto, Abid Abubakar, Phung Michelle, Yekrang Kiana, Petrone Paige, Zou James, Daneshjou Roxana

2023-Mar-15

General General

A Multimodal Data-driven Framework for Anxiety Screening

ArXiv Preprint

Early screening for anxiety and appropriate interventions are essential to reduce the incidence of self-harm and suicide in patients. Due to limited medical resources, traditional methods that overly rely on physician expertise and specialized equipment cannot simultaneously meet the needs for high accuracy and model interpretability. Multimodal data can provide more objective evidence for anxiety screening to improve the accuracy of models. The large amount of noise in multimodal data and the unbalanced nature of the data make the model prone to overfitting. However, it is a non-differentiable problem when high-dimensional and multimodal feature combinations are used as model inputs and incorporated into model training. This causes existing anxiety screening methods based on machine learning and deep learning to be inapplicable. Therefore, we propose a multimodal data-driven anxiety screening framework, namely MMD-AS, and conduct experiments on the collected health data of over 200 seafarers by smartphones. The proposed framework's feature extraction, dimension reduction, feature selection, and anxiety inference are jointly trained to improve the model's performance. In the feature selection step, a feature selection method based on the Improved Fireworks Algorithm is used to solve the non-differentiable problem of feature combination to remove redundant features and search for the ideal feature subset. The experimental results show that our framework outperforms the comparison methods.

Haimiao Mo, Shuai Ding, Siu Cheung Hui

2023-03-16