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

Rösler, Wiebke; Altenbuchinger, Michael; Baeßler, Bettina; Beißbarth, Tim; Beutel, Gernot; Bock, Robert M.; Bubnoff, Nikolas von; Foersch, Sebastian; Loeffler, Chiara Maria Lavinia; 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

doi:10.1007/s00432-023-04667-5

Cited by 35 publications

(7 citation statements)

References 75 publications

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“…In light of modern health challenges, including the COVID-19 pandemic, deep and machine learning methods have been proven to facilitate medical decision making and provide benefits to patients and caregivers beyond the previously known non-medical areas of application of the technology [2][3][4][5][6]. Particularly for the diagnosis, treatment and follow-up of highly complex and chronic diseases, as is the case in oncology, there is growing interest in corresponding clinical applications of individualized precision medicine [7,8]. In view of the demographic development and rapid aging of the population in central Europe, a continuing increase in oncological disease is predicted [9].…”

Section: Introductionmentioning

confidence: 99%

Challenging ChatGPT 3.5 in Senology—An Assessment of Concordance with Breast Cancer Tumor Board Decision Making

Griewing,

Gremke,

Wagner

et al. 2023

JPM

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With the recent diffusion of access to publicly available large language models (LLMs), common interest in generative artificial-intelligence-based applications for medical purposes has skyrocketed. The increased use of these models by tech-savvy patients for personal health issues calls for a scientific evaluation of whether LLMs provide a satisfactory level of accuracy for treatment decisions. This observational study compares the concordance of treatment recommendations from the popular LLM ChatGPT 3.5 with those of a multidisciplinary tumor board for breast cancer (MTB). The study design builds on previous findings by combining an extended input model with patient profiles reflecting patho- and immunomorphological diversity of primary breast cancer, including primary metastasis and precancerous tumor stages. Overall concordance between the LLM and MTB is reached for half of the patient profiles, including precancerous lesions. In the assessment of invasive breast cancer profiles, the concordance amounts to 58.8%. Nevertheless, as the LLM makes considerably fraudulent decisions at times, we do not identify the current development status of publicly available LLMs to be adequate as a support tool for tumor boards. Gynecological oncologists should familiarize themselves with the capabilities of LLMs in order to understand and utilize their potential while keeping in mind potential risks and limitations.

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Section: Introductionmentioning

confidence: 99%

Challenging ChatGPT 3.5 in Senology—An Assessment of Concordance with Breast Cancer Tumor Board Decision Making

Griewing,

Gremke,

Wagner

et al. 2023

JPM

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“…Кроме того, глубокое обучение было успешно применено для выявления диабетической ретинопатии на фундус-фотографиях сетчатки [24] и для разделения кожных новообразований на злокачественные и доброкачественные [25]. Такие системы автоматизированного обучения потенциально могут способствовать улучшению результатов лечения онкологических пациентов за счет усовершенствования тестов для раннего выявления заболеваний и повышения эффективности диагностики [26].…”

Section: Discussionunclassified

Deep learning for early detection of papillary bladder cancer on a limited set of cystoscopic images

Rozova,

Russo,

Lekarev

et al. 2024

jour

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Aim. The aim of this study was to develop and evaluate the effectiveness of a convolutional neural network (CNN) in detecting papillary bladder cancer (PBC) using a limited set of cystoscopic images.Materials and methods. Twenty patients who underwent white light cystoscopy and histologically confirmed papillary bladder cancer were included in the study. The dataset included 125 images retrieved and marked by a urologist: 88 images were papillary tumors and 37 were healthy bladder wall tissue. 100 images were selected for training and 25 images were selected for validation. The U-net architecture and the CNN VGG16 model were used. A binary mask was manually created for each image based on the comments given by the urologist. Each image was additionally processed for model compatibility, with 224×224 pixel images as input to reduce the number of parameters. The dataset was augmented by applying vertical and horizontal turns, as well as random rotations. The following metrics were calculated: Dice coefficient, sensitivity, specificity, proportion of false positives and false negatives, accuracy, and area under the ROC curve.Results. The original data set yielded the following parameters: specificity 84.56%, sensitivity 82.18%, false positive rate 15.44%, false negative rate 17.82%, accuracy 76.40%, and a Dice coefficient 83.16%. For the augmented dataset, the following values were obtained: specificity: 82.99%, sensitivity: 82.70%, false positive rate 17.01%, false negative rate 17.30%, accuracy 74.72%, Dice coefficient – 82.82%. The area under the ROC curves was 92.93% for the original dataset and 91.69% for the augmented dataset.Conclusion. The CNN created in this study can detect signs of early PBC when analyzing cystoscopic images. The results of the study can be a starting point for developing new methods to diagnose PBC using deep learning technologies.

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“…However, the basis for this is, of course, that the genetic signatures of the canine tumours must first be known before a histomorphological correlation can be made. Furthermore, regarding the clinical implications of such AI tools, thorough validation is required to guarantee the accuracy and robustness of a given image-analysis program [ 148 , 149 ]. Clinical AI systems need to be safe, and the European Council very recently defined an artificial intelligence act to ensure this in the medical and other fields.…”

Section: Emerging Fields For Genetic Investigations Of Canine Tumoursmentioning

confidence: 99%

Review of Molecular Technologies for Investigating Canine Cancer

Kehl,

Aupperle-Lellbach,

de Brot

et al. 2024

Animals

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Genetic molecular testing is starting to gain traction as part of standard clinical practice for dogs with cancer due to its multi-faceted benefits, such as potentially being able to provide diagnostic, prognostic and/or therapeutic information. However, the benefits and ultimate success of genomic analysis in the clinical setting are reliant on the robustness of the tools used to generate the results, which continually expand as new technologies are developed. To this end, we review the different materials from which tumour cells, DNA, RNA and the relevant proteins can be isolated and what methods are available for interrogating their molecular profile, including analysis of the genetic alterations (both somatic and germline), transcriptional changes and epigenetic modifications (including DNA methylation/acetylation and microRNAs). We also look to the future and the tools that are currently being developed, such as using artificial intelligence (AI) to identify genetic mutations from histomorphological criteria. In summary, we find that the molecular genetic characterisation of canine neoplasms has made a promising start. As we understand more of the genetics underlying these tumours and more targeted therapies become available, it will no doubt become a mainstay in the delivery of precision veterinary care to dogs with cancer.

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

Cited by 35 publications

References 75 publications

Challenging ChatGPT 3.5 in Senology—An Assessment of Concordance with Breast Cancer Tumor Board Decision Making

Challenging ChatGPT 3.5 in Senology—An Assessment of Concordance with Breast Cancer Tumor Board Decision Making

Deep learning for early detection of papillary bladder cancer on a limited set of cystoscopic images

Review of Molecular Technologies for Investigating Canine Cancer

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