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AI in Oncology

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Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability in FDG PET and CT images.

Tumor segmentation is a fundamental step for radiotherapy treatment planning...

DOI: 10.1088/1361-6560/acb9cf

De Biase A, Sijtsema NM, van Dijk L, Langendijk JA, van Ooijen PMA

Physics in medicine and biology, 2023-02-08

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The Evidence for Using Artificial Intelligence to Enhance Prostate Cancer MR Imaging.

PURPOSE OF REVIEW: The purpose of this review is to summarize the current status of artificial intelligence applied to prostate cancer MR imaging..

DOI: 10.1007/s11912-023-01371-y

Canellas R, Kohli MD, Westphalen AC

Current oncology reports, 2023-02-08

Deep learning model integrating positron emission tomography and clinical data for prognosis prediction in non-small cell lung cancer patients.

The proposed deep learning-based integrative model combining the two modalities improved the survival prediction in patients with NSCLC.

DOI: 10.1186/s12859-023-05160-z

Oh S, Kang SR, Oh IJ, Kim MS

BMC bioinformatics, 2023-02-08

Artificial Intelligence-assisted Treatment Strategy for T1 Colorectal Cancer after Endoscopic Resection.

No abstract text is available for this article

DOI: 10.1016/j.gie.2023.01.057

Ichimasa K, Kudo SE, Lee JWJ, Nemoto T, Yeoh KG

Gastrointestinal endoscopy, 2023-02-07

Artificial intelligence in pancreatic cancer: diagnosis, limitations, and the future prospects-a narrative review.

Artificial intelligence (AI) refers to computational machine systems that accomplish a set of given tasks by imitating human intelligence in an exponential learning pattern. AI in gastrointestinal oncology has continued to provide significant advancements in the clinical, molecular, and radiological diagnosis and intervention techniques required to improve the prognosis of many gastrointestinal cancer types, particularly pancreatic cancer.

DOI: 10.1007/s00432-023-04625-1

Katta MR, Kalluru PKR, Bavishi DA, Hameed M, Valisekka SS

Journal of cancer research and clinical oncology, 2023-02-06

Machine learning predictive performance evaluation of conventional and fuzzy radiomics in clinical cancer imaging cohorts.

Fuzzy radiomics has the potential to increase predictive performance particularly in small lesion sizes compared to conventional binary radiomics in PET. We hypothesize that this effect is due to the ability of fuzzy radiomics to model partial volume effects and delineation uncertainties at small lesion boundaries. In addition, we consider that the lower redundancy of fuzzy radiomic features supports the identification of imaging biomarkers in future studies. Future studies shall consider systematically analyzing lesions and their surroundings with fuzzy and binary radiomics.

DOI: 10.1007/s00259-023-06127-1

Grahovac M, Spielvogel CP, Krajnc D, Ecsedi B, Traub-Weidinger T, Rasul S, Kluge K, Zhao M, Li X, Hacker M, Haug A, Papp L

European journal of nuclear medicine and molecular imaging, 2023-02-05

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...

DOI: 10.1111/ijlh.14033

Haferlach T, Walter W

International journal of laboratory hematology, 2023-02-05

The Intersection of Lung Cancer Screening, Radiomics, and Artificial Intelligence: Can One Scan Really Predict the Future Development of Lung Cancer?

No abstract text is available for this article

DOI: 10.1200/JCO.22.02885

Silvestri GA, Jett JR

Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2023-02-04

[Comparison of four convolutional neural networks for histopathological diagnosis of salivary gland carcinomas].

The current work was able to demonstrate the applicability of CNNs for histopathological analysis of salivary gland tumors for the first time and provide a comparison of the performance of different network architectures. The results indicate a clear potential benefit for future applications.

DOI: 10.1007/s00106-023-01276-z

Schulz T, Becker C, Kayser G

HNO, 2023-02-04

Building Practical Risk Prediction Models for Nasopharyngeal Carcinoma Screening with Patient Graph Analysis and Machine Learning.

This study demonstrated the feasibility of developing practical NPC risk prediction models using EMR-wide ML and patient graph CDR analysis, without requiring EBV data. These models could enable broader implementation of NPC risk evaluation and screening recommendations for larger populations in urban community health centers and rural clinics.

DOI: 10.1158/1055-9965.EPI-22-0792

Chen A, Lu R, Han R, Huang R, Qin G, Wen J, Li Q, Zhang Z, Jiang W

Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology, 2023-02-07

Serum Raman spectroscopy combined with convolutional neural network for rapid diagnosis of HER2-positive and triple-negative breast cancer.

Breast cancer is common in women, and its number of patients ranks first among female malignant tumors...

DOI: 10.1016/j.saa.2022.122000

Zeng Q, Chen C, Chen C, Song H, Li M, Yan J, Lv X

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy, 2023-02-06

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