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Lu X, Chen H, Zhang T. Solid pseudopapillary neoplasm (SPN) of the pancreas: current understanding on its malignant potential and management. Discov Oncol 2024; 15:77. [PMID: 38498246 PMCID: PMC10948659 DOI: 10.1007/s12672-024-00905-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 02/22/2024] [Indexed: 03/20/2024] Open
Abstract
Solid pseudopapillary neoplasms (SPN) of the pancreas are presently recognized as low-grade malignant tumors that are frequently observed in young females. This tumor has a low incidence and is associated with an excellent prognosis following surgical resection. Typical SPNs primarily affect the pancreas and tend to have moderate or asymptomatic manifestations. Based on retrospective research, it is anticipated that patients with SPN can achieve disease-free survival, even in cases when metastasis is detected during inspection. However, the incidence of malignant SPN has been consistently underestimated, as evidenced by recent research findings. Malignancy of SPN primarily encompasses invasion and infiltration, metastasis, and recurrence after R0 resection. Imaging technologies such as Ultrasound, Computed Tomography, Magnetic Resonance Imaging, and Position Emission Tomography are capable of preliminarily identifying malignant SPN, which is primarily based on its invasive clinical features. Research on risk factors of malignant SPN revealed that larger tumor size, Ki-67 index, and several other parameters had significant correlations with invasive tumor behavior. Pathologic features of malignant SPNs overlay other pancreatic tumors, nevertheless they can provide valuable assistance in the process of diagnosis. Several confirmed specific pathologic biomarkers are related to its cellular origin, characteristic gene mutation, and cell proliferation. Considering the invasiveness of malignant SPN, it is imperative to enhance the comprehensiveness of its therapy. Tumor resection remains a suggested course of action in line with typical SPN, and additional lymph node dissection is seen as reasonable. Compared to benign SPNs, malignant SPNs have worse prognosis, underscoring the necessity of early identification and treatment in comprehensive medical centers to get improved clinical outcomes.
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Affiliation(s)
- Xiaoyue Lu
- Peking Union Medical College, Beijing, China
| | - Hao Chen
- Department of General Surgery, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China
| | - Taiping Zhang
- Department of General Surgery, Chinese Academy of Medical Sciences, Peking Union Medical College Hospital, Beijing, China.
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Mao KZ, Ma C, Song B. Radiomics advances in the evaluation of pancreatic cystic neoplasms. Heliyon 2024; 10:e25535. [PMID: 38333791 PMCID: PMC10850586 DOI: 10.1016/j.heliyon.2024.e25535] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2023] [Revised: 01/23/2024] [Accepted: 01/29/2024] [Indexed: 02/10/2024] Open
Abstract
With the development of medical imaging, the detection rate of pancreatic cystic neoplasms (PCNs) has increased greatly. Serous cystic neoplasm, solid pseudopapillary neoplasm, intraductal papillary mucinous neoplasm and mucinous cystic neoplasm are the main subtypes of PCN, and their treatment options vary greatly due to the different biological behaviours of the tumours. Different from conventional qualitative imaging evaluation, radiomics is a promising noninvasive approach for the diagnosis, classification, and risk stratification of diseases involving high-throughput extraction of medical image features. We present a review of radiomics in the diagnosis of serous cystic neoplasm and mucinous cystic neoplasm, risk classification of intraductal papillary mucinous neoplasm and prediction of solid pseudopapillary neoplasm invasiveness compared to conventional imaging diagnosis. Radiomics is a promising tool in the field of medical imaging, providing a noninvasive, high-performance model for preoperative diagnosis and risk stratification of PCNs and improving prospects regarding management of these diseases. Further studies are warranted to investigate MRI image radiomics in connection with PCNs to improve the diagnosis and treatment strategies in the management of PCN patients.
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Affiliation(s)
- Kuan-Zheng Mao
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, China
- Department of Pancreatic Surgery, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China
| | - Chao Ma
- Department of Radiology, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China
- College of Electronic and Information Engineering, Tongji University, Shanghai, 201804, China
| | - Bin Song
- Department of Pancreatic Surgery, Changhai Hospital of Shanghai, Naval Medical University, Shanghai, 200433, China
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Li Y, Yang L, Gu X, Wang Q, Shi G, Zhang A, Yue M, Wang M, Ren J. Computed tomography radiomics identification of T1-2 and T3-4 stages of esophageal squamous cell carcinoma: two-dimensional or three-dimensional? Abdom Radiol (NY) 2024; 49:288-300. [PMID: 37843576 PMCID: PMC10789855 DOI: 10.1007/s00261-023-04070-1] [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: 05/30/2023] [Revised: 09/15/2023] [Accepted: 09/19/2023] [Indexed: 10/17/2023]
Abstract
BACKGROUND To evaluate two-dimensional (2D) and three-dimensional (3D) computed tomography (CT) radiomics analysis for the T stage of esophageal squamous cell carcinoma (ESCC). METHODS 398 patients with pathologically confirmed ESCC were divided into training and testing sets. All patients underwent chest CT scans preoperatively. For each tumor, based on CT images, a 2D region of interest (ROI) was outlined on the largest cross-sectional area, and a 3D ROI was outlined layer by layer on each section of the tumor. The radiomics platform was used for feature extraction. For feature selection, stepwise logistic regression was used. The receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of the 2D radiomics model versus the 3D radiomics model. The differences were compared using the DeLong test. The value of the clinical utility of the two radiomics models was evaluated. RESULTS 1595 radiomics features were extracted. After screening, two radiomics models were constructed. In the training set, the difference between the area under the curve (AUC) of the 2D radiomics model (AUC = 0.831) and the 3D radiomics model (AUC = 0.830) was not statistically significant (p = 0.973). In the testing set, the difference between the AUC of the 2D radiomics model (AUC = 0.807) and the 3D radiomics model (AUC = 0.797) was also not statistically significant (p = 0.748). A 2D model was equally useful as a 3D model in clinical situations. CONCLUSION The performance of 2D radiomics model is comparable to that of 3D radiomics model in distinguishing between the T1-2 and T3-4 stages of ESCC. In addition, 2D radiomics model may be a more feasible option due to the shorter time required for segmenting the ROI.
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Affiliation(s)
- Yang Li
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China
| | - Li Yang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China
| | - Xiaolong Gu
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China.
| | - Qi Wang
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China
| | - Gaofeng Shi
- Department of Radiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China
| | - Andu Zhang
- Department of Radiotherapy, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China
| | - Meng Yue
- Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China
| | - Mingbo Wang
- Department of Thoracic Surgery, The Fourth Hospital of Hebei Medical University, Shijiazhuang, 050000, Hebei Province, People's Republic of China
| | - Jialiang Ren
- GE Healthcare China, Beijing, 100176, People's Republic of China
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Yang ZH, Han YJ, Cheng M, Wang R, Li J, Zhao HP, Gao JB. Prognostic value of computed tomography radiomics features in patients with gastric neuroendocrine neoplasm. Front Oncol 2023; 13:1143291. [PMID: 37409252 PMCID: PMC10319063 DOI: 10.3389/fonc.2023.1143291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 06/05/2023] [Indexed: 07/07/2023] Open
Abstract
Purpose The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric neuroendocrine neoplasm (GNEN). Methods and Materials A retrospective study of 182 patients with GNEN who underwent dual-phase enhanced computed tomography (CT) scanning was conducted. LASSO-Cox regression analysis was used to screen the features and establish the arterial, venous and the arteriovenous phase combined R-signature, respectively. The association between the optimal R-signature with the best prognostic performance and overall survival (OS) was assessed in the training cohort and verified in the validation cohort. Univariate and multivariate Cox regression analysis were used to identify the significant factors of clinicopathological characteristics for OS. Furthermore, the performance of a combined radiomics-clinical nomogram integrating the R-signature and independent clinicopathological risk factors was evaluated. Results The arteriovenous phase combined R-signature had the best performance in predicting OS, and its C-index value was better than the independent arterial and venous phase R-signature (0.803 vs 0.784 and 0.803 vs 0.756, P<0.001, respectively). The optimal R-signature was significantly associated with OS in the training cohort and validation cohort. GNEN patients could be successfully divided into high and low prognostic risk groups with radiomics score median. The combined radiomics-clinical nomogram combining this R-signature and independent clinicopathological risk factors (sex, age, treatment methods, T stage, N stage, M stage, tumor boundary, Ki67, CD56) exhibited significant prognostic superiority over clinical nomogram, R-signature alone, and traditional TNM staging system (C-index, 0.882 vs 0.861, 882 vs 0.803, and 0.882 vs 0.870 respectively, P<0.001). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the combined radiomics-clinical nomogram for clinical practice. Conclusions The R-signature could be used to stratify patients with GNEN into high and low risk groups. Furthermore, the combined radiomics-clinical nomogram provided better predictive accuracy than other predictive models and might aid clinicians with therapeutic decision-making and patient counseling.
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Affiliation(s)
- Zhi-hao Yang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yi-jing Han
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ming Cheng
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Rui Wang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Department of Medical Information, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jing Li
- Department of Radiology, Affiliated Tumor Hospital of Zhengzhou University, Zhengzhou, China
| | - Hui-ping Zhao
- Department of Radiology, Shanxi Provincial People’s Hospital, Xi’an, China
| | - Jian-bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
- Henan Key Laboratory of Image Diagnosis and Treatment for Digestive System Tumor, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Liang X, He W, Huang C, Feng Z, Guan X, Liu Y, Sun Z, Li Z. Preoperative prediction of invasive behavior of pancreatic solid pseudopapillary neoplasm by MRI-based multiparametric radiomics models. ABDOMINAL RADIOLOGY (NEW YORK) 2022; 47:3782-3791. [PMID: 35976419 DOI: 10.1007/s00261-022-03639-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/12/2022] [Revised: 07/25/2022] [Accepted: 07/27/2022] [Indexed: 01/18/2023]
Abstract
OBJECTIVE A log-combined model was developed to predict the invasive behavior of pancreatic solid pseudopapillary neoplasm (pSPN) based on clinical and radiomic features extracted from multiparametric magnetic resonance imaging (MRI). MATERIALS AND METHODS A total of 111 patients with pathologically confirmed pSPN who underwent preoperative plain and contrast-enhanced MRI were included, and divided into an invasive group (n = 34) and non-invasive group (n = 77). Clinical features and laboratory data related to pSPN invasive behavior were analyzed. Regions of interest were delineated based on T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and contrast-enhanced T1WI (CE-T1WI) to extract radiomic features. Correlation analysis was performed for these features, followed by L1_based feature selection (C = 0.15). A logistic regression algorithm was used to construct models based on each of the four sequences and a log-combined model was used to integrate the sequences. A receiver operating characteristic (ROC) curve was plotted to evaluate the model performance, and the Brier score was used to assess the overall accuracy of the model predictions. RESULTS The area under the ROC curve was 0.68, 0.73, 0.71, and 0.49 for Log-T1WI, Log-T2WI, Log-DWI, and Log-CE models, respectively, and 0.81 for the log-combined model. The accuracy, precision, sensitivity, and specificity of the log-combined model were 0.77, 0.88, 0.75, and 0.78, respectively. The best performance was obtained with the log-combined model with a Brier score of 0.18. Tumor location was identified as a significant clinical feature in comparison between the two groups (p < 0.05), and invasive pSPN was more frequent in the tail of the pancreas. CONCLUSION The log-combined model based on multiparametric MRI and clinical features can be used as a non-invasive diagnostic tool for preoperative prediction of pSPN invasive behavior and to facilitate the development of individualized treatment strategies and monitoring management plans.
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Affiliation(s)
- Xiuqun Liang
- Department of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region & Research Center of Radiology, Guangxi Academy of Medical Sciences, Nanning, 530021, Guangxi, China
| | - Wenguang He
- Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310007, Zhejiang, China
| | - Chencui Huang
- Department of Research Collaboration, R&D center, Beijing Deepwise & League of PHD Technology Co, Ltd, Beijing, 100080, China
| | - Zhan Feng
- Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310007, Zhejiang, China
| | - Xiaohui Guan
- Department of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region & Research Center of Radiology, Guangxi Academy of Medical Sciences, Nanning, 530021, Guangxi, China
| | - Ying Liu
- Department of Radiology, The People's Hospital of Guangxi Zhuang Autonomous Region & Research Center of Radiology, Guangxi Academy of Medical Sciences, Nanning, 530021, Guangxi, China
| | - Zeyong Sun
- Department of Radionuclide, The People's Hospital of Guangxi Zhuang Autonomous Region & Research Center of Radionuclide, Guangxi Academy of Medical Sciences, Nanning, 530021, Guangxi, China
| | - Zhi Li
- Department of Radiology, First Affiliated Hospital, School of Medicine, Zhejiang University, 79 Qingchun Road, Hangzhou, 310007, Zhejiang, China.
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Du KP, Huang WP, Liu SY, Chen YJ, Li LM, Liu XN, Han YJ, Zhou Y, Liu CC, Gao JB. Application of computed tomography-based radiomics in differential diagnosis of adenocarcinoma and squamous cell carcinoma at the esophagogastric junction. World J Gastroenterol 2022; 28:4363-4375. [PMID: 36159013 PMCID: PMC9453771 DOI: 10.3748/wjg.v28.i31.4363] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 06/11/2022] [Accepted: 07/25/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND The biological behavior of carcinoma of the esophagogastric junction (CEGJ) is different from that of gastric or esophageal cancer. Differentiating squamous cell carcinoma of the esophagogastric junction (SCCEG) from adenocarcinoma of the esophagogastric junction (AEG) can indicate Siewert stage and whether the surgical route for patients with CEGJ is transthoracic or transabdominal, as well as aid in determining the extent of lymph node dissection. With the development of neoadjuvant therapy, preoperative determination of pathological type can help in the selection of neoadjuvant radiotherapy and chemotherapy regimens.
AIM To establish and evaluate computed tomography (CT)-based multiscale and multiphase radiomics models to distinguish SCCEG and AEG preoperatively.
METHODS We retrospectively analyzed the preoperative contrasted-enhanced CT imaging data of single-center patients with pathologically confirmed SCCEG (n = 130) and AEG (n = 130). The data were divided into either a training (n = 182) or a test group (n = 78) at a ratio of 7:3. A total of 1409 radiomics features were separately extracted from two dimensional (2D) or three dimensional (3D) regions of interest in arterial and venous phases. Intra-/inter-observer consistency analysis, correlation analysis, univariate analysis, least absolute shrinkage and selection operator regression, and backward stepwise logical regression were applied for feature selection. Totally, six logistic regression models were established based on 2D and 3D multi-phase features. The receiver operating characteristic curve analysis, the continuous net reclassification improvement (NRI), and the integrated discrimination improvement (IDI) were used for assessing model discrimination performance. Calibration and decision curves were used to assess the calibration and clinical usefulness of the model, respectively.
RESULTS The 2D-venous model (5 features, AUC: 0.849) performed better than 2D-arterial (5 features, AUC: 0.808). The 2D-arterial-venous combined model could further enhance the performance (AUC: 0.869). The 3D-venous model (7 features, AUC: 0.877) performed better than 3D-arterial (10 features, AUC: 0.876). And the 3D-arterial-venous combined model (AUC: 0.904) outperformed other single-phase-based models. The venous model showed a positive improvement compared with the arterial model (NRI > 0, IDI > 0), and the 3D-venous and combined models showed a significant positive improvement compared with the 2D-venous and combined models (P < 0.05). Decision curve analysis showed that combined 3D-arterial-venous model and 3D-venous model had a higher net clinical benefit within the same threshold probability range in the test group.
CONCLUSION The combined arterial-venous CT radiomics model based on 3D segmentation can improve the performance in differentiating EGJ squamous cell carcinoma from adenocarcinoma.
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Affiliation(s)
- Ke-Pu Du
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Wen-Peng Huang
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Si-Yun Liu
- Department of Pharmaceutical Diagnostics, General Electric Company Healthcare, Beijing 100176, China
| | - Yun-Jin Chen
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Li-Ming Li
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Xiao-Nan Liu
- Department of Pathology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Yi-Jing Han
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Yue Zhou
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Chen-Chen Liu
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
| | - Jian-Bo Gao
- Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou 450052, Henan Province, China
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A New Hypoxia-Related Prognostic Risk Score (HPRS) Model Was Developed to Indicate Prognosis and Response to Immunotherapy for Lung Adenocarcinoma. JOURNAL OF ONCOLOGY 2022; 2022:6373226. [PMID: 35942407 PMCID: PMC9356881 DOI: 10.1155/2022/6373226] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 06/14/2022] [Indexed: 12/03/2022]
Abstract
Background Hypoxia is a typical microenvironmental feature of most solid tumors, affecting a variety of physiological processes. We developed a hypoxia-related prognostic risk score (HPRS) model to reveal tumor microenvironment (TME) and predict prognosis of lung adenocarcinoma (LUAD). Methods LUAD sample expression data were from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) Cox regression identified hypoxia-related genes (HRGs) to create HPRS. The prognostic value, genetic mutation and TME, and therapeutic response of distinct HPRS groups were analyzed. Univariate and multivariate Cox regression analysis identified independent factors associated with the prognosis of LUAD. A decision tree based on HPRS and clinicopathological variables was established using the classification system based on decision tree algorithm. A nomogram was constructed with important clinical features and HPRS by the RMS package. Results A HPRS model with five HRGs was developed and verified in two separate cohorts of GEO. HPRS model divided patients with LUAD into two groups. High HPRS was related to high probability of genetic alterations. HPRS could predict the prognosis, TME, and sensitivity to immunotherapy/chemotherapy of LUAD. The decision tree defined four risk subgroups with significant OS differences. Nomogram with integrated HPRS and clinical features had acceptable accuracy in predicting LUAD prognosis. Conclusions A HPRS model was developed to evaluate prognosis, genetic alterations, TME, and response to immunotherapy, which may provide theoretical reference for the study of molecular mechanism of hypoxia in LUAD.
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Preuss K, Thach N, Liang X, Baine M, Chen J, Zhang C, Du H, Yu H, Lin C, Hollingsworth MA, Zheng D. Using Quantitative Imaging for Personalized Medicine in Pancreatic Cancer: A Review of Radiomics and Deep Learning Applications. Cancers (Basel) 2022; 14:cancers14071654. [PMID: 35406426 PMCID: PMC8997008 DOI: 10.3390/cancers14071654] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 03/16/2022] [Accepted: 03/18/2022] [Indexed: 12/12/2022] Open
Abstract
Simple Summary With a five-year survival rate of only 3% for the majority of patients, pancreatic cancer is a global healthcare challenge. Radiomics and deep learning, two novel quantitative imaging methods that treat medical images as minable data instead of just pictures, have shown promise in advancing personalized management of pancreatic cancer through diagnosing precursor diseases, early detection, accurate diagnosis, and treatment personalization. Radiomics and deep learning methods aim to collect hidden information in medical images that is missed by conventional radiology practices through expanding the data search and comparing information across different patients. Both methods have been studied and applied in pancreatic cancer. In this review, we focus on the current progress of these two methods in pancreatic cancer and provide a comprehensive narrative review on the topic. With better regulation, enhanced workflow, and larger prospective patient datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through personalized precision medicine. Abstract As the most lethal major cancer, pancreatic cancer is a global healthcare challenge. Personalized medicine utilizing cutting-edge multi-omics data holds potential for major breakthroughs in tackling this critical problem. Radiomics and deep learning, two trendy quantitative imaging methods that take advantage of data science and modern medical imaging, have shown increasing promise in advancing the precision management of pancreatic cancer via diagnosing of precursor diseases, early detection, accurate diagnosis, and treatment personalization and optimization. Radiomics employs manually-crafted features, while deep learning applies computer-generated automatic features. These two methods aim to mine hidden information in medical images that is missed by conventional radiology and gain insights by systematically comparing the quantitative image information across different patients in order to characterize unique imaging phenotypes. Both methods have been studied and applied in various pancreatic cancer clinical applications. In this review, we begin with an introduction to the clinical problems and the technology. After providing technical overviews of the two methods, this review focuses on the current progress of clinical applications in precancerous lesion diagnosis, pancreatic cancer detection and diagnosis, prognosis prediction, treatment stratification, and radiogenomics. The limitations of current studies and methods are discussed, along with future directions. With better standardization and optimization of the workflow from image acquisition to analysis and with larger and especially prospective high-quality datasets, radiomics and deep learning methods could show real hope in the battle against pancreatic cancer through big data-based high-precision personalization.
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Affiliation(s)
- Kiersten Preuss
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Nutrition and Health Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA
| | - Nate Thach
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Xiaoying Liang
- Department of Radiation Oncology, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Michael Baine
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Justin Chen
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Naperville North High School, Naperville, IL 60563, USA
| | - Chi Zhang
- School of Biological Sciences, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Huijing Du
- Department of Mathematics, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Hongfeng Yu
- Department of Computer Science, University of Nebraska Lincoln, Lincoln, NE 68588, USA;
| | - Chi Lin
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
| | - Michael A. Hollingsworth
- Eppley Institute for Research in Cancer, University of Nebraska Medical Center, Omaha, NE 68198, USA;
| | - Dandan Zheng
- Department of Radiation Oncology, University of Nebraska Medical Center, Omaha, NE 68198, USA; (K.P.); (N.T.); (M.B.); (J.C.); (C.L.)
- Department of Radiation Oncology, University of Rochester, Rochester, NY 14626, USA
- Correspondence: ; Tel.: +1-(585)-276-3255
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