Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis

A, Faur; Lazăr, Daniela; Ghenciu, Laura Andreea Bolintineanu

doi:10.3748/wjg.v29.i12.1811

Cited by 9 publications

(3 citation statements)

References 43 publications

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“…The high-risk group showed a higher classification performance, with 86.4% accuracy, compared with the low-risk group, with 78.3% accuracy. In conclusion, the addition of CT radiomics features to standard clinical factors improves survival prediction in patients with PDAC [145,154,[217][218][219][220].…”

Section: Prognosismentioning

confidence: 88%

Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis

Pacella,

Brunese,

D’Imperio

et al. 2023

JCM

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Background: Pancreatic ductal adenocarcinoma (PDAC) is the seventh leading cause of cancer-related deaths worldwide. Surgical resection is the main driver to improving survival in resectable tumors, while neoadjuvant treatment based on chemotherapy (and radiotherapy) is the best option-treatment for a non-primally resectable disease. CT-based imaging has a central role in detecting, staging, and managing PDAC. As several authors have proposed radiomics for risk stratification in patients undergoing surgery for PADC, in this narrative review, we have explored the actual fields of interest of radiomics tools in PDAC built on pre-surgical imaging and clinical variables, to obtain more objective and reliable predictors. Methods: The PubMed database was searched for papers published in the English language no earlier than January 2018. Results: We found 301 studies, and 11 satisfied our research criteria. Of those included, four were on resectability status prediction, three on preoperative pancreatic fistula (POPF) prediction, and four on survival prediction. Most of the studies were retrospective. Conclusions: It is possible to conclude that many performing models have been developed to get predictive information in pre-surgical evaluation. However, all the studies were retrospective, lacking further external validation in prospective and multicentric cohorts. Furthermore, the radiomics models and the expression of results should be standardized and automatized to be applicable in clinical practice.

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Section: Prognosismentioning

confidence: 88%

Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis

Pacella,

Brunese,

D’Imperio

et al. 2023

JCM

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“…Patients diagnosed with PC are generally classified into four categories, resectable, borderline resectable, locally advanced, or metastatic, based on the results of cross-sectional imaging. In current clinical practice, CT is the most cost-effective and extensively used cross-sectional imaging modality to evaluate patients with or for PC [10]. However, the diagnosis of PC from CT remains a difficult task for radiologists due to high heterogeneity among suspicious pancreatic masses.…”

Section: Applications In the Diagnosis Of Pancreatic Ductal Adenocarc...mentioning

confidence: 99%

Deep Learning Applications in Pancreatic Cancer

Patel,

Zanos,

Hewitt

2024

Cancers

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Pancreatic cancer is one of the most lethal gastrointestinal malignancies. Despite advances in cross-sectional imaging, chemotherapy, radiation therapy, and surgical techniques, the 5-year overall survival is only 12%. With the advent and rapid adoption of AI across all industries, we present a review of applications of DL in the care of patients diagnosed with PC. A review of different DL techniques with applications across diagnosis, management, and monitoring is presented across the different pathological subtypes of pancreatic cancer. This systematic review highlights AI as an emerging technology in the care of patients with pancreatic cancer.

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“…types, estimate clinical outcomes associated with tumor biology. 7 Radiomics represents a promising non-invasive tool for various applications in PC, including early diagnosis, evaluating treatment response, predicting prognosis, and precise diagnosis.…”

mentioning

confidence: 99%

Radiomics in Pancreatic Cancer: Present and Future

Ren,

Song,

Daniels

et al. 2024

Cancer Screen Prev

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Dear editor, Pancreatic cancer (PC) is the fourth most common cause of cancer-related mortality, with a meager 5-year survival rate of under 20%. 1 Patients with PC often exhibit nonspecific symptoms such as abdominal pain and weight loss, leading to delayed diagnosis. However, even when promptly diagnosed after symptom onset, the majority of PC patients are found to have advanced-stage disease. Despite notable progress in surgical methods, chemotherapy, and radiation therapy, the 5-year survival rate remains dishearteningly low at 8.2%. 2While the exact causes of PC remain poorly understood, several risk factors have been identified. These include a family history of PC, obesity, chronic pancreatitis (CP), smoking, the presence of preneoplastic lesions, or certain hereditary syndromes associated with a high risk of developing PC. 3 Cross-sectional imaging is a vital tool in initially assessing symptomatic individuals suspected of having PC. It is also crucial in screening asymptomatic individuals at a heightened risk of developing PC. 4 Computed tomography (CT) is the predominant imaging diagnostic method, often complemented by endoscopic ultrasound with fine needle biopsy or aspiration for pinpointing small lesions and confirming diagnoses definitively. 5 Additionally, magnetic resonance imaging (MRI) and positron emission tomography (PET) play significant roles in systematically staging the disease and determining whether the primary tumor is resectable, borderline resectable, or unresectable. 5 Radiomics, a subset of medical imaging, involves extracting quantitative data from various medical images like CT scans, MRI scans, or PET scans. This data is then meticulously analyzed to glean valuable insights into tumor heterogeneity. 6 Quantitative imaging facilitates integrating radiomics and dynamic imaging features, independently or together, enabling the construction of clinical prediction models.

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Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis

Cited by 9 publications

References 43 publications

Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis

Pancreatic Ductal Adenocarcinoma: Update of CT-Based Radiomics Applications in the Pre-Surgical Prediction of the Risk of Post-Operative Fistula, Resectability Status and Prognosis

Deep Learning Applications in Pancreatic Cancer

Radiomics in Pancreatic Cancer: Present and Future

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