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Sijithra PC, Santhi N, Ramasamy N. A review study on early detection of pancreatic ductal adenocarcinoma using artificial intelligence assisted diagnostic methods. Eur J Radiol 2023; 166:110972. [PMID: 37454557 DOI: 10.1016/j.ejrad.2023.110972] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2023] [Accepted: 07/10/2023] [Indexed: 07/18/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive, chemo-refractory and recalcitrant cancer and increases the number of deaths. With just around 1 in 4 individuals having respectable tumours, PDAC is frequently discovered when it is in an advanced stage. Accordingly, ED of PDAC improves patient survival. Subsequently, this paper reviews the early detection of PDAC, initially, the work presented an overview of PDAC. Subsequently, it reviews the molecular biology of pancreatic cancer and the development of molecular biomarkers are represented. This article illustrates the importance of identifying PDCA, the Immune Microenvironment of Pancreatic Cancer. Consequently, in this review, traditional and non-traditional imaging techniques are elucidated, traditional and non-traditional methods like endoscopic ultrasound, Multidetector CT, CT texture analysis, PET-CT, magnetic resonance imaging, diffusion-weighted imaging, secondary signs of pancreatic cancer, and molecular imaging. The use of artificial intelligence in pancreatic cancer, novel MRI techniques, and the future directions of AI for PDAC detection and prognosis is then described. Additionally, the research problem definition and motivation, current trends and developments, state of art of survey, and objective of the research are demonstrated in the review. Consequently, this review concluded that Artificial Intelligence Assisted Diagnostic Methods with MRI images can be proposed in future to improve the specificity and the sensitivity of the work, and to classify malignant PDAC with greater accuracy.
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Affiliation(s)
- P C Sijithra
- Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Kanyakumari District, Tamilnadu, India.
| | - N Santhi
- Department of Electronics and Communication Engineering, Noorul Islam Centre for Higher Education, Kanyakumari District, Tamilnadu, India
| | - N Ramasamy
- Department of Mechanical Engineering, Noorul Islam Centre for Higher Education, Kanyakumari District, Tamilnadu, India
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Bodalal Z, Bogveradze N, Ter Beek LC, van den Berg JG, Sanders J, Hofland I, Trebeschi S, Groot Lipman KBW, Storck K, Hong EK, Lebedyeva N, Maas M, Beets-Tan RGH, Gomez FM, Kurilova I. Radiomic signatures from T2W and DWI MRI are predictive of tumour hypoxia in colorectal liver metastases. Insights Imaging 2023; 14:133. [PMID: 37477715 PMCID: PMC10361926 DOI: 10.1186/s13244-023-01474-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2023] [Accepted: 06/27/2023] [Indexed: 07/22/2023] Open
Abstract
BACKGROUND Tumour hypoxia is a negative predictive and prognostic biomarker in colorectal cancer typically assessed by invasive sampling methods, which suffer from many shortcomings. This retrospective proof-of-principle study explores the potential of MRI-derived imaging markers in predicting tumour hypoxia non-invasively in patients with colorectal liver metastases (CLM). METHODS A single-centre cohort of 146 CLMs from 112 patients were segmented on preoperative T2-weighted (T2W) images and diffusion-weighted imaging (DWI). HIF-1 alpha immunohistochemical staining index (high/low) was used as a reference standard. Radiomic features were extracted, and machine learning approaches were implemented to predict the degree of histopathological tumour hypoxia. RESULTS Radiomic signatures from DWI b200 (AUC = 0.79, 95% CI 0.61-0.93, p = 0.002) and ADC (AUC = 0.72, 95% CI 0.50-0.90, p = 0.019) were significantly predictive of tumour hypoxia. Morphological T2W TE75 (AUC = 0.64, 95% CI 0.42-0.82, p = 0.092) and functional DWI b0 (AUC = 0.66, 95% CI 0.46-0.84, p = 0.069) and b800 (AUC = 0.64, 95% CI 0.44-0.82, p = 0.071) images also provided predictive information. T2W TE300 (AUC = 0.57, 95% CI 0.33-0.78, p = 0.312) and b = 10 (AUC = 0.53, 95% CI 0.33-0.74, p = 0.415) images were not predictive of tumour hypoxia. CONCLUSIONS T2W and DWI sequences encode information predictive of tumour hypoxia. Prospective multicentre studies could help develop and validate robust non-invasive hypoxia-detection algorithms. CRITICAL RELEVANCE STATEMENT Hypoxia is a negative prognostic biomarker in colorectal cancer. Hypoxia is usually assessed by invasive sampling methods. This proof-of-principle retrospective study explores the role of AI-based MRI-derived imaging biomarkers in non-invasively predicting tumour hypoxia in patients with colorectal liver metastases (CLM).
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Affiliation(s)
- Zuhir Bodalal
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Nino Bogveradze
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
- Department of Radiology, American Hospital Tbilisi, Tbilisi, Georgia
| | - Leon C Ter Beek
- Department of Medical Physics, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Jose G van den Berg
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Joyce Sanders
- Department of Pathology, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Ingrid Hofland
- Core Facility Molecular Pathology & Biobank, The Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Stefano Trebeschi
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Kevin B W Groot Lipman
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Koen Storck
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Eun Kyoung Hong
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Natalya Lebedyeva
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Monique Maas
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
| | - Regina G H Beets-Tan
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
- GROW School for Oncology and Developmental Biology, Maastricht University, Maastricht, The Netherlands
| | - Fernando M Gomez
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands.
- Hospital Clinic-Hospital Sant Joan de Deu, Barcelona, Spain.
| | - Ieva Kurilova
- Department of Radiology, The Netherlands Cancer Institute, Plesmanlaan 121, 1066 CX, Amsterdam, The Netherlands
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Qu C, Zeng P, Wang H, Guo L, Zhang L, Yuan C, Yuan H, Xiu D. Preoperative Multiparametric Quantitative Magnetic Resonance Imaging Correlates with Prognosis and Recurrence Patterns in Pancreatic Ductal Adenocarcinoma. Cancers (Basel) 2022; 14:cancers14174243. [PMID: 36077777 PMCID: PMC9454581 DOI: 10.3390/cancers14174243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 08/19/2022] [Accepted: 08/28/2022] [Indexed: 11/17/2022] Open
Abstract
Simple Summary Magnetic resonance imaging (MRI) has been considered a noninvasive prognostic biomarker in some cancers; however, the correlation with pancreatic ductal adenocarcinoma (PDAC) remains inconclusive. The aim of our study was to identify quantitative MRI parameters associated with prognosis and recurrence patterns. In an analysis of data from the 136 patients ultimately included in this study, we found that the value of the pure diffusion coefficient D in intravoxel incoherent MRI is an independent risk factor for overall survival (OS) and recurrence-free survival (RFS), while a low value of D is significantly associated with a higher risk of local recurrence. All the patients have been operated on with histopathology for further evaluation. Based on the results of our research, we believe that it is possible in clinical practice to stratify patients based on quantitative MRI data in order to guide treatment strategies, reduce the risk of local tumor recurrence, and improve patients’ prognosis. Abstract Magnetic resonance imaging (MRI) has been shown to be associated with prognosis in some tumors; however, the correlation in pancreatic ductal adenocarcinoma (PDAC) remains inconclusive. In this retrospective study, we ultimately included 136 patients and analyzed quantitative MRI parameters that are associated with prognosis and recurrence patterns in PDAC using survival analysis and competing risks models; all the patients have been operated on with histopathology and immunohistochemical staining for further evaluation. In intravoxel incoherent motion diffusion-weighted imaging (DWI), we found that pure-diffusion coefficient D value was an independent risk factor for overall survival (OS) (HR: 1.696, 95% CI: 1.003–2.869, p = 0.049) and recurrence-free survival (RFS) (HR: 2.066, 95% CI: 1.252–3.409, p = 0.005). A low D value (≤1.08 × 10−3 mm2/s) was significantly associated with a higher risk of local recurrence (SHR: 5.905, 95% CI: 2.107–16.458, p = 0.001). Subgroup analysis revealed that patients with high D and f values had significantly better outcomes with adjuvant chemotherapy. Distant recurrence patients in the high-D value group who received chemotherapy may significantly improve their OS and RFS. It was found that preoperative multiparametric quantitative MRI correlates with prognosis and recurrence patterns in PDAC. Diffusion coefficient D value can be used as a noninvasive biomarker for predicting prognosis and recurrence patterns in PDAC.
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Affiliation(s)
- Chao Qu
- Department of General Surgery, Peking University Third Hospital, Beijing 100191, China
| | - Piaoe Zeng
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
| | - Hangyan Wang
- Department of General Surgery, Peking University Third Hospital, Beijing 100191, China
| | - Limei Guo
- Department of Pathology, School of Basic Medical Sciences, Peking University Third Hospital, Peking University Health Science Center, Beijing 100191, China
| | - Lingfu Zhang
- Department of General Surgery, Peking University Third Hospital, Beijing 100191, China
| | - Chunhui Yuan
- Department of General Surgery, Peking University Third Hospital, Beijing 100191, China
| | - Huishu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing 100191, China
- Correspondence: (H.Y.); (D.X.)
| | - Dianrong Xiu
- Department of General Surgery, Peking University Third Hospital, Beijing 100191, China
- Correspondence: (H.Y.); (D.X.)
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Qu C, Zeng PE, Wang HY, Yuan CH, Yuan HS, Xiu DR. Application of Magnetic Resonance Imaging in Neoadjuvant Treatment of Pancreatic Ductal Adenocarcinoma. J Magn Reson Imaging 2022; 55:1625-1632. [PMID: 35132729 DOI: 10.1002/jmri.28096] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 01/18/2022] [Accepted: 01/22/2022] [Indexed: 12/11/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest malignant tumors of the human digestive system. Due to its insidious onset, many patients have already lost the opportunity for radical resection upon tumor diagnosis. In recent years, neoadjuvant treatment for patients with borderline resectable PDAC has been recommended by multiple guidelines to increase the resection rate of radical surgery and improve the postoperative survival. However, further developments are required to accurately assess the tumor response to neoadjuvant therapy and to select the population suitable for such treatment. Reductions in drug toxicity and the number of neoadjuvant cycles are also critical. At present, the clinical evaluation of neoadjuvant treatment is mainly based on several serological and imaging indicators; however, the unique characteristics of PDAC and the insufficient sensitivity and specificity of the markers render this system ineffective. The imaging evaluation system, magnetic resonance imaging (MRI), has its own unique imaging advantages compared with computed tomography (CT) and other imaging examinations. One key advantage is the ability to reflect the changes more rapidly in tumor tissue components, such as the degree of fibrosis, microvessel density, and tissue hypoxia. It can also perform multiparameter quantitative analysis of tumor tissue and changes, attributing to its increasingly important role in imaging evaluation, and potentially the evaluation of neoadjuvant treatment of pancreatic cancer, as several current articles have studied. At the same time, owing to the complexity of MRI and some of its limitations, its wider application is limited. Compared with CT imaging, few relevant studies have been conducted. In this review article, we will investigate and summarize the advantages, limitations, and future development of MRI in the evaluation of neoadjuvant treatment of PDAC. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Chao Qu
- Department of General Surgery, Peking University Third Hospital, Beijing, China
| | - Piao-E Zeng
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Hang-Yan Wang
- Department of General Surgery, Peking University Third Hospital, Beijing, China
| | - Chun-Hui Yuan
- Department of General Surgery, Peking University Third Hospital, Beijing, China
| | - Hui-Shu Yuan
- Department of Radiology, Peking University Third Hospital, Beijing, China
| | - Dian-Rong Xiu
- Department of General Surgery, Peking University Third Hospital, Beijing, China
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