Non-invasive prediction of microsatellite instability in colorectal cancer by a genetic algorithm–enhanced artificial neural network–based CT radiomics signature

Chen, Xiaobo; Li, Qingshu; Liu, Liu; Li, Suyun; Zhang, Yuan; Liu, Zaiyi; Huang, Yanqi; Mao, Yun; Chen, Xin

doi:10.1007/s00330-022-08954-6

Cited by 14 publications

(23 citation statements)

References 38 publications

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“… 75 A study conducted among patients with colorectal cancer identified a radiogenomic signature that can reliably predict microsatellite instability status of the tumors and stratify patients into low-risk and high-risk groups. 76 Another radiomic signature identified KRAS/neuroblastoma rat sarcaoma virus (RAS) viral oncogene homolog (NRAS)/B-Raf proto-oncogene (BRAF) mutations in colorectal cancer, which reduce the response to monoclonal antibodies cetuximab and panitumumab. 77 …”

Section: Radiogenomicsmentioning

confidence: 99%

Artificial Intelligence in the Era of Precision Oncological Imaging

Cellina

Cè

Khenkina

et al. 2022

Technol Cancer Res Treat

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Rapid-paced development and adaptability of artificial intelligence algorithms have secured their almost ubiquitous presence in the field of oncological imaging. Artificial intelligence models have been created for a variety of tasks, including risk stratification, automated detection, and segmentation of lesions, characterization, grading and staging, prediction of prognosis, and treatment response. Soon, artificial intelligence could become an essential part of every step of oncological workup and patient management. Integration of neural networks and deep learning into radiological artificial intelligence algorithms allow for extrapolating imaging features otherwise inaccessible to human operators and pave the way to truly personalized management of oncological patients. Although a significant proportion of currently available artificial intelligence solutions belong to basic and translational cancer imaging research, their progressive transfer to clinical routine is imminent, contributing to the development of a personalized approach in oncology. We thereby review the main applications of artificial intelligence in oncological imaging, describe the example of their successful integration into research and clinical practice, and highlight the challenges and future perspectives that will shape the field of oncological radiology.

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Section: Radiogenomicsmentioning

confidence: 99%

Artificial Intelligence in the Era of Precision Oncological Imaging

Cellina

Cè

Khenkina

et al. 2022

Technol Cancer Res Treat

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“…The initial search yielded 97 records from the four public databases. After the removal of 48 duplicates, 37 ineligible studies, 12 studies were finally included in this systematic review [27][28][29][30][31][32][33][34][35][36][37][38]. Among them, 10 studies with available data were able to construct a 2 × 2 contingency table [27-31, 33-35, 37, 38].…”

Section: Resultsmentioning

confidence: 99%

“…A total of 4,320 patients were included, with a sample size ranging from 90 to 837 (median 238) and a male/female ratio of 1.5 (2,592/1,728). Four studies were performed as multicenter research, with a sample size in the external cohorts ranging from 61 to 441 (median 82) [30,35,37,38]. Five studies exclusively focused on rectal cancer, while the others on CRC [29,32,[36][37][38].…”

Section: General Characteristics and The Incidence Of Msimentioning

confidence: 99%

“…(1) Imaging acquisition and tumor segmentation Among the included studies, seven used CT imaging, four MRI [29,32,37,38], and one PET/CT [33]. Six studies applied images from one phase/sequence [27][28][29][34][35][36]; the most frequently used phase was the portal venous phase of CT imaging (7/12) [27,28,30,31,[34][35][36]. The tumor was segmented manually in 11 studies and semi-automatically in one [27].…”

Section: Study Characteristicsmentioning

confidence: 99%

“…Cross-validation with 5 or tenfold was applied in six studies (6/12) to avoid model overfitting and to determine the superparameter [27,29,30,34,37,38]. Six studies evaluated the predictive value of clinicopathological variables [29][30][31][34][35][36], in which tumor location and age (both 4/6) were the most frequent, significant indicators for the prediction of MSI status, followed by carcinoembryonic antigen (3/6). All those six studies then combined the studied variables with the calculated radiomics risk score into a compound clinical radiomics model to predict MSI status.…”

Section: Study Characteristicsmentioning

confidence: 99%

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Systematic review of machine learning-based radiomics approach for predicting microsatellite instability status in colorectal cancer

Wang

Xu²,

Wang

et al. 2023

Radiol med

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This study aimed to systematically summarize the performance of the machine learning-based radiomics models in the prediction of microsatellite instability (MSI) in patients with colorectal cancer (CRC). It was conducted according to the preferred reporting items for a systematic review and meta-analysis of diagnostic test accuracy studies (PRISMA-DTA) guideline and was registered at the PROSPERO website with an identifier CRD42022295787. Systematic literature searching was conducted in databases of PubMed, Embase, Web of Science, and Cochrane Library up to November 10, 2022. Research which applied radiomics analysis on preoperative CT/MRI/PET-CT images for predicting the MSI status in CRC patients with no history of anti-tumor therapies was eligible. The radiomics quality score (RQS) and Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) were applied to evaluate the research quality (full score 100%). Twelve studies with 4,320 patients were included. All studies were retrospective, and only four had an external validation cohort. The median incidence of MSI was 19% (range 8–34%). The area under the receiver operator curve of the models ranged from 0.78 to 0.96 (median 0.83) in the external validation cohort. The median sensitivity was 0.76 (range 0.32–1.00), and the median specificity was 0.87 (range 0.69–1.00). The median RQS score was 38% (range 14–50%), and half of the studies showed high risk in patient selection as evaluated by QUADAS-2. In conclusion, while radiomics based on pretreatment imaging modalities had a high performance in the prediction of MSI status in CRC, so far it does not appear to be ready for clinical use due to insufficient methodological quality.

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