Radiomics in precision medicine for gastric cancer: opportunities and challenges

Zhang, Lu; Liu, Shuyi; You, Jungmok; Chen, Luyan; Jin, Zhe; Zhang, Shuixing; Zhang, Bin

doi:10.1007/s00330-022-08704-8

Cited by 56 publications

(31 citation statements)

References 60 publications

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“…These models can be supervised or unsupervised, in relation to the desired outcome of interest in model knowledge [244][245][246][247][248][249]. In their supervised form, a training dataset is introduced to obtain the desired outcome [250][251][252][253][254][255][256][257][258][259][260][261][262][263]. This type of learning requires large amounts of training data which has been pre-labeled ("curated") by a human operator.…”

Section: Artificial Intelligence Radiomics and Pancreatic Cancermentioning

confidence: 99%

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

Granata

Fusco

Setola

et al. 2023

Cancers

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Pancreatic cancer (PC) is one of the deadliest cancers, and it is responsible for a number of deaths almost equal to its incidence. The high mortality rate is correlated with several explanations; the main one is the late disease stage at which the majority of patients are diagnosed. Since surgical resection has been recognised as the only curative treatment, a PC diagnosis at the initial stage is believed the main tool to improve survival. Therefore, patient stratification according to familial and genetic risk and the creation of screening protocol by using minimally invasive diagnostic tools would be appropriate. Pancreatic cystic neoplasms (PCNs) are subsets of lesions which deserve special management to avoid overtreatment. The current PC screening programs are based on the annual employment of magnetic resonance imaging with cholangiopancreatography sequences (MR/MRCP) and/or endoscopic ultrasonography (EUS). For patients unfit for MRI, computed tomography (CT) could be proposed, although CT results in lower detection rates, compared to MRI, for small lesions. The actual major limit is the incapacity to detect and characterize the pancreatic intraepithelial neoplasia (PanIN) by EUS and MR/MRCP. The possibility of utilizing artificial intelligence models to evaluate higher-risk patients could favour the diagnosis of these entities, although more data are needed to support the real utility of these applications in the field of screening. For these motives, it would be appropriate to realize screening programs in research settings.

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Section: Artificial Intelligence Radiomics and Pancreatic Cancermentioning

confidence: 99%

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

Granata

Fusco

Setola

et al. 2023

Cancers

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“…Moreover, a recently published systematic review [ 78 ] enrolled 25 studies including a total of more than 10000 patients. The most commonly used imaging technique was CT (96% of the included studies).…”

Section: New Frontiersmentioning

confidence: 99%

Imaging in Gastric Cancer: Current Practice and Future Perspectives

et al. 2023

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Gastric cancer represents one of the most common oncological causes of death worldwide. In order to treat patients in the best possible way, the staging of gastric cancer should be accurate. In this regard, endoscopy ultrasound (EUS) has been considered the reference standard for tumor (T) and nodal (N) statuses in recent decades. However, thanks to technological improvements, computed tomography (CT) has gained an important role, not only in the assessment of distant metastases (M status) but also in T and N staging. In addition, magnetic resonance imaging (MRI) can contribute to the detection and staging of primary gastric tumors thanks to its excellent soft tissue contrast and multiple imaging sequences without radiation-related risks. In addition, MRI can help with the detection of liver metastases, especially small lesions. Finally, positron emission tomography (PET) is still considered a useful diagnostic tool for the staging of gastric cancer patients, with a focus on nodal metastases and peritoneal carcinomatosis. In addition, it may play a role in the treatment of gastric cancer in the coming years thanks to the introduction of new labeling peptides. This review aims to summarize the most common advantages and pitfalls of EUS, CT, MRI and PET in the TNM staging of gastric cancer patients.

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“…Imaging modalities that can be used for radiomics analysis include computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET). Since CT is the most commonly used staging method for esophageal cancer (EC) and gastric cancer (GC), most radiomics studies on EC and GC are based on CT images[ 5 - 9 ]. In contrast, as MRI is widely used for colorectal cancer (CRC) staging, most radiomics studies on CRC are based on MRI features[ 10 - 13 ].…”

Section: Radiomics Workflowmentioning

confidence: 99%

“…In recent years, some researchers have also explored the value of radiomics to the diagnosis and treatment of GC[ 9 , 54 , 55 ]. The CT radiomics model has high application value in the identification of GC[ 54 , 56 - 58 ].…”

Section: Radiomics Workflowmentioning

confidence: 99%

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

Qi¹,

Zhou²,

Zhao³

et al. 2022

World J Gastroenterol

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Gastrointestinal cancer (GIC) has high morbidity and mortality as one of the main causes of cancer death. Preoperative risk stratification is critical to guide patient management, but traditional imaging studies have difficulty predicting its biological behavior. The emerging field of radiomics allows the conversion of potential pathophysiological information in existing medical images that cannot be visually recognized into high-dimensional quantitative image features. Tumor lesion characterization, therapeutic response evaluation, and survival prediction can be achieved by analyzing the relationships between these features and clinical and genetic data. In recent years, the clinical application of radiomics to GIC has increased dramatically. In this editorial, we describe the latest progress in the application of radiomics to GIC and discuss the value of its potential clinical applications, as well as its limitations and future directions.

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Radiomics in precision medicine for gastric cancer: opportunities and challenges

Cited by 56 publications

References 60 publications

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

Risk Assessment and Pancreatic Cancer: Diagnostic Management and Artificial Intelligence

Imaging in Gastric Cancer: Current Practice and Future Perspectives

Role of radiomics in the diagnosis and treatment of gastrointestinal cancer

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