Artificial intelligence for tumour tissue detection and histological regression grading in oesophageal adenocarcinomas: a retrospective algorithm development and validation study

Tolkach, Yuri; Wolgast, Lisa Marie; Damanakis, Alexander; Pryalukhin, Alexey; Schallenberg, Simon; Hulla, Wolfgang; Eich, Marie‐Lisa; Schröeder, Wolfgang; Mukhopadhyay, Anirban; Fuchs, Moritz; Klein, Sebastian; Bruns, Christiane; Büttner, Reinhard; Gebauer, Florian; Schömig-Markiefka, Birgid; Quaas, Alexander

doi:10.1016/s2589-7500(23)00027-4

Cited by 15 publications

(2 citation statements)

References 27 publications

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“…By analyzing morphological characteristics from histological images, AI algorithms have shown remarkable capabilities in forecasting patient prognosis and response to specific therapies [ 31 , 33 ]. These tools excel in stratifying patients with varying disease grades, sometimes surpassing the abilities of pathologists [ 34 ]. By incorporating factors such as tumor grade, subtype, microenvironment patterns, and genetic profiles, AI algorithms can establish connections between pathology images, survival outcomes, and treatment responses, facilitating a more nuanced approach to precision medicine [ 31 ].…”

Section: Reviewmentioning

confidence: 99%

From Pixels to Prognosis: A Narrative Review on Artificial Intelligence’s Pioneering Role in Colorectal Carcinoma Histopathology

Naseri,

Shukla,

Hiwale

et al. 2024

Cureus

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Colorectal carcinoma, a prevalent and deadly malignancy, necessitates precise histopathological assessment for effective diagnosis and prognosis. Artificial intelligence (AI) emerges as a transformative force in this realm, offering innovative solutions to enhance traditional histopathological methods. This narrative review explores AI's pioneering role in colorectal carcinoma histopathology, encompassing its evolution, techniques, and advancements. AI algorithms, notably machine learning and deep learning, have revolutionized image analysis, facilitating accurate diagnosis and prognosis prediction. Furthermore, AI-driven histopathological analysis unveils potential biomarkers and therapeutic targets, heralding personalized treatment approaches. Despite its promise, challenges persist, including data quality, interpretability, and integration. Collaborative efforts among researchers, clinicians, and AI developers are imperative to surmount these hurdles and realize AI's full potential in colorectal carcinoma care. This review underscores AI's transformative impact and implications for future oncology research, clinical practice, and interdisciplinary collaboration.

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Section: Reviewmentioning

confidence: 99%

From Pixels to Prognosis: A Narrative Review on Artificial Intelligence’s Pioneering Role in Colorectal Carcinoma Histopathology

Naseri,

Shukla,

Hiwale

et al. 2024

Cureus

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“… 2 – 6 Another important direction is building models employing relevant prediction factors to predict radiation pneumonitis. 7 – 9 Developments in supportive techniques including automated organ detection, registration, classification, and segmentation 10 – 14 greatly helped the efforts. The employed methods range from traditional machine learning approaches to modern deep learning neural networks and relatively recent architectures including vision transformers.…”

Section: Introductionmentioning

confidence: 99%

Deep-Learning Model Prediction of Radiation Pneumonitis Using Pretreatment Chest Computed Tomography and Clinical Factors

Lee,

Kang,

Park

et al. 2024

Technol Cancer Res Treat

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Objectives: This study aimed to build a comprehensive deep-learning model for the prediction of radiation pneumonitis using chest computed tomography (CT), clinical, dosimetric, and laboratory data. Introduction: Radiation therapy is an effective tool for treating patients with lung cancer. Despite its effectiveness, the risk of radiation pneumonitis limits its application. Although several studies have demonstrated models to predict radiation pneumonitis, no reliable model has been developed yet. Herein, we developed prediction models using pretreatment chest CT and various clinical data to assess the likelihood of radiation pneumonitis in lung cancer patients. Methods: This retrospective study analyzed 3-dimensional (3D) lung volume data from chest CT scans and 27 features including dosimetric, clinical, and laboratory data from 548 patients who were treated at our institution between 2010 and 2021. We developed a neural network, named MergeNet, which processes lung 3D CT, clinical, dosimetric, and laboratory data. The MergeNet integrates a convolutional neural network with subsequent fully connected layers. A support vector machine (SVM) and light gradient boosting machine (LGBM) model were also implemented for comparison. For comparison, the convolution-only neural network was implemented as well. Three-dimensional Resnet-10 network and 4-fold cross-validation were used. Results: Classification performance was quantified by using the area under the receiver operative characteristic curve (AUC) metrics. MergeNet showed the AUC of 0.689. SVM, LGBM, and convolution-only networks showed AUCs of 0.525, 0.541, and 0.550, respectively. Application of DeLong test to pairs of receiver operating characteristic curves respectively yielded P values of .001 for the MergeNet–SVM pair and 0.001 for the MergeNet–LGBM pair. Conclusion: The MergeNet model, which incorporates chest CT, clinical, dosimetric, and laboratory data, demonstrated superior performance compared to other models. However, since its prediction performance has not yet reached an efficient level for clinical application, further research is required. Contribution: This study showed that MergeNet may be an effective means to predict radiation pneumonitis. Various predictive factors can be used together for the radiation pneumonitis prediction task via the MergeNet.

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Maßgeschneiderte Chirurgie in der Behandlung gastroösophagealer Tumoren

Schmidt,

Fuchs,

Thomas

et al. 2024

Chirurgie

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Artificial intelligence for tumour tissue detection and histological regression grading in oesophageal adenocarcinomas: a retrospective algorithm development and validation study

Cited by 15 publications

References 27 publications

From Pixels to Prognosis: A Narrative Review on Artificial Intelligence’s Pioneering Role in Colorectal Carcinoma Histopathology

From Pixels to Prognosis: A Narrative Review on Artificial Intelligence’s Pioneering Role in Colorectal Carcinoma Histopathology

Deep-Learning Model Prediction of Radiation Pneumonitis Using Pretreatment Chest Computed Tomography and Clinical Factors

Maßgeschneiderte Chirurgie in der Behandlung gastroösophagealer Tumoren

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