The impact of radiomics in diagnosis and staging of pancreatic cancer

Casà, Calogero; Piras, Antonio; D’Aviero, Andrea; Preziosi, Francesco; Mariani, Silvia; Cusumano, D.; Romano, Angela; Boškoski, Ivo; Lenkowicz, Jacopo; Dinapoli, N.; Cellini, Francesco; Gambacorta, Maria Antonietta; Valentini, Vincenzo; Mattiucci, Gian Carlo; Boldrini, Luca

doi:10.1177/26317745221081596

Cited by 23 publications

(14 citation statements)

References 91 publications

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“…Another study by Ahmad et al ]. Experiments on diverse datasets from different regions have further validated the value of radiomics in pancreatic cancer diagnosis tasks [15].…”

Section: Basicsmentioning

confidence: 90%

“…Support Vector Machines (SVM) and XGBoost have emerged as robust classifiers for such tasks [11 ▪ ,12 ▪ ,14 ▪ ], with most advances focusing on application aspects, such as the comparison between CT and MRI [14 ▪ ]. Experiments on diverse datasets from different regions have further validated the value of radiomics in pancreatic cancer diagnosis tasks [15].…”

Section: Radiomicsmentioning

confidence: 99%

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A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging

Zhang

Keleş

et al. 2023

Current Opinion in Gastroenterology

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Purpose of review Early and accurate diagnosis of pancreatic cancer is crucial for improving patient outcomes, and artificial intelligence (AI) algorithms have the potential to play a vital role in computer-aided diagnosis of pancreatic cancer. In this review, we aim to provide the latest and relevant advances in AI, specifically deep learning (DL) and radiomics approaches, for pancreatic cancer diagnosis using cross-sectional imaging examinations such as computed tomography (CT) and magnetic resonance imaging (MRI). Recent findings This review highlights the recent developments in DL techniques applied to medical imaging, including convolutional neural networks (CNNs), transformer-based models, and novel deep learning architectures that focus on multitype pancreatic lesions, multiorgan and multitumor segmentation, as well as incorporating auxiliary information. We also discuss advancements in radiomics, such as improved imaging feature extraction, optimized machine learning classifiers and integration with clinical data. Furthermore, we explore implementing AI-based clinical decision support systems for pancreatic cancer diagnosis using medical imaging in practical settings. Summary Deep learning and radiomics with medical imaging have demonstrated strong potential to improve diagnostic accuracy of pancreatic cancer, facilitate personalized treatment planning, and identify prognostic and predictive biomarkers. However, challenges remain in translating research findings into clinical practice. More studies are required focusing on refining these methods, addressing significant limitations, and developing integrative approaches for data analysis to further advance the field of pancreatic cancer diagnosis.

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“…Another study by Ahmad et al ]. Experiments on diverse datasets from different regions have further validated the value of radiomics in pancreatic cancer diagnosis tasks [15].…”

Section: Basicsmentioning

confidence: 90%

Section: Radiomicsmentioning

confidence: 99%

A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging

Zhang

Keleş

et al. 2023

Current Opinion in Gastroenterology

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“…Radiomics, a novel and promising higher computational method, involves extracting so-called radiomic features (RFs) from medical images that are not discernible by the human eye. The discovery of non-invasive RF-based imaging biomarkers could potentially enable better staging, and lead to improved response to treatment and overall survival as part of precision/personalised medicine [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Current state of radiomic research in pancreatic cancer: focusing on study design and reproducibility of findings

et al. 2023

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Objectives To critically appraise methodology and reproducibility of published studies on CT radiomics of pancreatic ductal adenocarcinoma (PDAC). Methods PRISMA literature search of MEDLINE, PubMed, and Scopus databases was conducted from June to August 2022 relating to CT radiomics human research articles pertaining to PDAC diagnosis, treatment, and/ or prognosis, utilising Image Biomarker Standardisation Initiative-compliant (IBSI) radiomic software. Keyword search included [pancreas OR pancreatic] AND [radiomic OR [quantitative AND imaging] OR [texture AND analysis]]. Analysis included cohort size, CT protocol used, radiomic feature (RF) extraction, segmentation, and selection, software used, outcome correlation, and statistical methodology, with focus on reproducibility. Results Initial search yielded 1112 articles; however, only 12 articles met all inclusion/exclusion criteria. Cohort sizes ranged from 37 to 352 (median = 106, mean = 155.8). CT slice thickness varied among studies (4 using ≤ 1 mm, 5 using > 1 to 3 mm, 2 using > 3 to 5 mm, 1 not specifying). CT protocol varied (5 using a single portal-venous (pv)-phase, 5 using a pancreas protocol, 1 study using a non-contrast protocol). RF extraction and segmentation were heterogeneous (RF extraction: 5 using pv-phase, 2 using late arterial, 4 using multi-phase, 1 using non-contrast phase; RF selection: 3 pre-selected, 9 software-selected). 2D/3D RF segmentation was diverse (2D in 6, 3D in 4, 2D and 3D in 2 studies). Six different radiomics software were used. Research questions and cohort characteristics varied, ultimately leading to non-comparable outcome results. Conclusion The current twelve published IBSI-compliant PDAC radiomic studies show high variability and often incomplete methodology resulting in low robustness and reproducibility. Clinical relevance statement Radiomics research requires IBSI compliance, data harmonisation, and reproducible feature extraction methods for non-invasive imaging biomarker discoveries to be valid. This will ensure a successful clinical implementation and ultimately an improvement of patient outcomes as part of precision and personalised medicine. Key Points • Current state of radiomics research in pancreatic cancer shows low software compliance to the Image Biomarker Standardisation Initiative (IBSI). • IBSI-compliant radiomics studies in pancreatic cancer are heterogeneous and not comparable, and the majority of study designs showed low reproducibility. • Improved methodology and standardisation of practice in the emerging field of radiomics has the potential of this non-invasive imaging biomarker in the management of pancreatic cancer.

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“…However, it mainly focuses on the detection and differential diagnosis of pancreatic cancer. Furthermore, most researches are based on computed tomography (CT), rarely relating to pathological indicators, particularly for MP‐MRI, which can extract image features from multiple sequences 13,14 …”

mentioning

confidence: 99%

“…Furthermore, most researches are based on computed tomography (CT), rarely relating to pathological indicators, particularly for MP-MRI, which can extract image features from multiple sequences. 13,14 Thus, the aim of this study is to investigate whether radiomics can more accurately evaluate the pancreatic cancer's HG and LNM based on MP-MRI.…”

mentioning

confidence: 99%

Peritumoral and Intratumoral Texture Features Based on Multiparametric MRI and Multiple Machine Learning Methods to Preoperatively Evaluate the Pathological Outcomes of Pancreatic Cancer

Xie

Fan

Chen

et al. 2022

Magnetic Resonance Imaging

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Background: Radiomics-based preoperative evaluation of lymph node metastasis (LNM) and histological grade (HG) might facilitate the decision-making for pancreatic cancer and further efforts are needed to develop effective models. Purpose: To develop multiparametric MRI (MP-MRI)-based radiomics models to evaluate LNM and HG. Study Type: Retrospective. Population: The pancreatic cancer patients from the main center (n = 126) were assigned to the training and validation sets at a 4:1 ratio. The patients from the other center (n = 40) served as external test sets. Field Strength/Sequence: A 3.0 T and 1.5 T/T2-weighted imaging, diffusion-weighted imaging, and dynamic contrast enhancement T1-weighted imaging. Assessment: A total of 10,686 peritumoral and intratumoral radiomics features were extracted which contained first-order, shape-based, and texture features. The following three-step method was applied to reduce the feature dimensionality: SelectKBest (a function from scikit-learn package), least absolute shrinkage and selection operator (LASSO), and recursive feature elimination based on random forest (RFE-RF). Six classifiers (random forest, logistic regression, support vector machine, K-nearest neighbor, decision tree, and XGBOOST) were trained and selected based on their performance to construct the clinical, radiomics, and combination models. Statistical Tests: Delong's test was used to compare the models' performance. P value less than 0.05 was considered significant. Results: Twelve significant features for LNM and 11 features for HG were obtained. Random forest and logistic regression performed better than the other classifiers in evaluating LNM and HG, respectively, according to the surgical pathological results. The best performance was obtained with the models that combined peritumoral and intratumoral features with area under curve (AUC) values of 0.944 and 0.892 in the validation and external test sets for HG and 0.924 and 0.875 for LNM.

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The impact of radiomics in diagnosis and staging of pancreatic cancer

Cited by 23 publications

References 91 publications

A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging

A review of deep learning and radiomics approaches for pancreatic cancer diagnosis from medical imaging

Current state of radiomic research in pancreatic cancer: focusing on study design and reproducibility of findings

Peritumoral and Intratumoral Texture Features Based on Multiparametric MRI and Multiple Machine Learning Methods to Preoperatively Evaluate the Pathological Outcomes of Pancreatic Cancer

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