A Novel Multiresolution-Statistical Texture Analysis Architecture: Radiomics-Aided Diagnosis of PDAC Based on Plain CT Images

Qiu, Jiajun; Jin, Yixin; Qian, Wei; Liu, Jin-Heng; Huang, Zixing; Yu, Haopeng; Ji, Li; Zeng, Xiaoxi

doi:10.1109/tmi.2020.3021254

Cited by 24 publications

(15 citation statements)

References 30 publications

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“…For faster and more accurate examination, many artificial intelligence (AI) techniques for automated detection and quantitative analysis of COVID-19 lesions from CT images have been developed based on deep learning and radiomics (9)(10)(11)(12)(13)(14)(15)(16)(17)(18). In addition to detecting lesions, assessing the grade of COVID-19 pulmonary lesions is important for the hierarchical management and treatment of infected patients (19).…”

Section: Introductionmentioning

confidence: 99%

Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions

Jiang¹,

Jin²,

Han³

et al. 2022

Quant Imaging Med Surg

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Background: This study set out to develop a computed tomography (CT)-based wavelet transforming radiomics approach for grading pulmonary lesions caused by COVID-19 and to validate it using real-world data.Methods: This retrospective study analyzed 111 patients with 187 pulmonary lesions from 16 hospitals; all patients had confirmed COVID-19 and underwent non-contrast chest CT. Data were divided into a training cohort (72 patients with 127 lesions from nine hospitals) and an independent test cohort (39 patients with 60 lesions from seven hospitals) according to the hospital in which the CT was performed. In all, 73 texture features were extracted from manually delineated lesion volumes, and 23 three-dimensional (3D) wavelets with eight decomposition modes were implemented to compare and validate the value of wavelet transformation for grade assessment. Finally, the optimal machine learning pipeline, valuable radiomic features, and final radiomic models were determined. The area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve were used to determine the diagnostic performance and clinical utility of the models.Results: Of the 187 lesions, 108 (57.75%) were diagnosed as mild lesions and 79 (42.25%) as moderate/ severe lesions. All selected radiomic features showed significant correlations with the grade of COVID-19 pulmonary lesions (P<0.05). Biorthogonal 1.1 (bior1.1) LLL was determined as the optimal wavelet transform mode. The wavelet transforming radiomic model had an AUC of 0.910 in the test cohort, outperforming the original radiomic model (AUC =0.880; P<0.05). Decision analysis showed the radiomic model could add a net benefit at any given threshold of probability.Conclusions: Wavelet transformation can enhance CT texture features. Wavelet transforming radiomics based on CT images can be used to effectively assess the grade of pulmonary lesions caused by COVID-19, ^ ORCID: Zekun Jiang,

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Section: Introductionmentioning

confidence: 99%

Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions

Jiang¹,

Jin²,

Han³

et al. 2022

Quant Imaging Med Surg

Self Cite

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“…In our statistics, several studies focused on PC or PC precursor lesions diagnosis or prediction by AI-assisted CT. Their AUC, accuracy, sensitivity, specificity were 0.79-0.999, 77.66%-99.2%, 76.64%-100%, 85.59%-98.5%, respectively [113][114][115][116][117][118][119][120][121][122]. The method of Chu et al [120] has the highest accuracy (99.2%) among the studies.…”

Section: Computed Tomographymentioning

confidence: 99%

Artificial intelligence in pancreatic cancer

Huang¹,

Huang²,

Zhang³

et al. 2022

Theranostics

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Pancreatic cancer is the deadliest disease, with a five-year overall survival rate of just 11%. The pancreatic cancer patients diagnosed with early screening have a median overall survival of nearly ten years, compared with 1.5 years for those not diagnosed with early screening. Therefore, early diagnosis and early treatment of pancreatic cancer are particularly critical. However, as a rare disease, the general screening cost of pancreatic cancer is high, the accuracy of existing tumor markers is not enough, and the efficacy of treatment methods is not exact. In terms of early diagnosis, artificial intelligence technology can quickly locate high-risk groups through medical images, pathological examination, biomarkers, and other aspects, then screening pancreatic cancer lesions early. At the same time, the artificial intelligence algorithm can also be used to predict the survival time, recurrence risk, metastasis, and therapy response which could affect the prognosis. In addition, artificial intelligence is widely used in pancreatic cancer health records, estimating medical imaging parameters, developing computer-aided diagnosis systems, etc. Advances in AI applications for pancreatic cancer will require a concerted effort among clinicians, basic scientists, statisticians, and engineers. Although it has some limitations, it will play an essential role in overcoming pancreatic cancer in the foreseeable future due to its mighty computing power.

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“…[42,43] In the meantime, a deep-learning model based on imaging characteristics can extract features from plain CT images to locate and segment the pancreas and diagnose pancreatic cancer. [44,45] It can also distinguish IPMN from PDAC, which aids the surgical decision for surgeons. [44] In company with machine learning, liquid biopsy, and imaging, an effective prognostic model for guiding the early diagnosis and differential diagnoses of pancreatic cancer will be established.…”

Section: Promoting Early Diagnosis In Pancreatic Cancermentioning

confidence: 99%

Insight of pancreatic cancer: recommendations for improving its therapeutic efficacy in the next decade

Wang

Lou

et al. 2022

Journal of Pancreatology

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Pancreatic cancer is one of the most malignant digestive system tumors. The effectiveness of pancreatic cancer treatment is still dismal, and the 5-year survival rate is only about 10%. Further improving the diagnosis and treatment of pancreatic cancer is the top priority of oncology research and clinical practice. Based on the existing clinical and scientific research experience, the review provides insight into the hotspots and future directions for pancreatic cancer, which focuses on early detection, early diagnosis, molecular typing and precise treatment, new drug development and regimen combination, immunotherapy, database development, model establishment, surgical technology and strategy change, as well as innovation of traditional Chinese medicine and breakthrough of treatment concept.

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A Novel Multiresolution-Statistical Texture Analysis Architecture: Radiomics-Aided Diagnosis of PDAC Based on Plain CT Images

Cited by 24 publications

References 30 publications

Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions

Wavelet transformation can enhance computed tomography texture features: a multicenter radiomics study for grade assessment of COVID-19 pulmonary lesions

Artificial intelligence in pancreatic cancer

Insight of pancreatic cancer: recommendations for improving its therapeutic efficacy in the next decade

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