1
|
Holm MB, Lenggenhager D, Detlefsen S, Sántha P, Verbeke CS. Identification of tumour regression in neoadjuvantly treated pancreatic cancer is based on divergent and nonspecific criteria. Histopathology 2024; 85:171-181. [PMID: 38571446 DOI: 10.1111/his.15190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/23/2023] [Accepted: 03/18/2024] [Indexed: 04/05/2024]
Abstract
AIMS Following the increased use of neoadjuvant therapy for pancreatic cancer, grading of tumour regression (TR) has become part of routine diagnostics. However, it suffers from marked interobserver variation, which is mainly ascribed to the subjectivity of the defining criteria of the categories in TR grading systems. We hypothesized that a further cause for the interobserver variation is the use of divergent and nonspecific morphological criteria to identify tumour regression. METHODS AND RESULTS Twenty treatment-naïve pancreatic cancers and 20 pancreatic cancers treated with neoadjuvant chemotherapy were reviewed by three experienced pancreatic pathologists who, blinded for treatment status, categorized each tumour as treatment-naïve or neoadjuvantly treated, and annotated all tissue areas they considered showing tumour regression. Only 50%-65% of the cases were categorized correctly, and the annotated tissue areas were highly discrepant (only 3%-41% overlap). When the prevalence of various morphological features deemed to indicate TR was compared between treatment-naïve and neoadjuvantly treated tumours, only one pattern, characterized by reduced cancer cell density and prominent stroma affecting a large area of the tumour bed, occurred significantly more frequently, but not exclusively, in the neoadjuvantly treated group. Finally, stromal features, both morphological and biological, were investigated as possible markers for tumour regression, but failed to distinguish TR from native tumour stroma. CONCLUSION There is considerable divergence in opinion between pathologists when it comes to the identification of tumour regression. Reliable identification of TR is only possible if it is extensive, while lesser degrees of treatment effect cannot be recognized with certainty.
Collapse
Affiliation(s)
- Maia Blomhoff Holm
- Department of Pathology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Pathology, Oslo University Hospital, Oslo, Norway
| | - Daniela Lenggenhager
- Department of Pathology and Molecular Pathology, University Hospital Zurich and University of Zurich, Zurich, Switzerland
| | - Sönke Detlefsen
- Department of Pathology, Odense University Hospital, Odense, Denmark
- Department of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Petra Sántha
- Department of Pathology, Oslo University Hospital, Oslo, Norway
| | - Caroline Sophie Verbeke
- Department of Pathology, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- Department of Pathology, Oslo University Hospital, Oslo, Norway
| |
Collapse
|
2
|
Anghel C, Grasu MC, Anghel DA, Rusu-Munteanu GI, Dumitru RL, Lupescu IG. Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images. Diagnostics (Basel) 2024; 14:438. [PMID: 38396476 PMCID: PMC10887967 DOI: 10.3390/diagnostics14040438] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 02/10/2024] [Accepted: 02/14/2024] [Indexed: 02/25/2024] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) stands out as the predominant malignant neoplasm affecting the pancreas, characterized by a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation, and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last five years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in the detection of PDAC, segmentation of the lesion, and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show a lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.
Collapse
Affiliation(s)
- Cristian Anghel
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Mugur Cristian Grasu
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Denisa Andreea Anghel
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Gina-Ionela Rusu-Munteanu
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Radu Lucian Dumitru
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| | - Ioana Gabriela Lupescu
- Faculty of Medicine, Department of Medical Imaging and Interventional Radiology, Carol Davila University of Medicine and Pharmacy Bucharest, 020021 Bucharest, Romania; (C.A.); (R.L.D.); (I.G.L.)
- Department of Radiology and Medical Imaging, Fundeni Clinical Institute, 022328 Bucharest, Romania; (D.A.A.); (G.-I.R.-M.)
| |
Collapse
|
3
|
Wen Y, Song Z, Li Q, Zhang D, Li X, Yu J, Li Z, Ren X, Zhang J, Liu Q, Huang J, Zeng D, Tang Z. Development and validation of a model for predicting the expression of Ki-67 in pancreatic ductal adenocarcinoma with radiological features and dual-energy computed tomography quantitative parameters. Insights Imaging 2024; 15:41. [PMID: 38353857 PMCID: PMC10866831 DOI: 10.1186/s13244-024-01617-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 12/21/2023] [Indexed: 02/17/2024] Open
Abstract
OBJECTIVE To construct and validate a model based on the dual-energy computed tomography (DECT) quantitative parameters and radiological features to predict Ki-67 expression levels in pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS Data from 143 PDAC patients were analysed. The variables of clinic, radiology and DECT were evaluated. In the arterial phase and portal venous phase (PVP), the normalized iodine concentration (NIC), normalized effective atomic number and slope of the spectral attenuation curves were measured. The extracellular volume fraction (ECVf) was measured in the equilibrium phase. Univariate analysis was used to screen independent risk factors to predict Ki-67 expression. The Radiology, DECT and DECT-Radiology models were constructed, and their diagnostic effectiveness and clinical applicability were obtained through area under the curve (AUC) and decision curve analysis, respectively. The nomogram was established based on the optimal model, and its goodness-of-fit was assessed by a calibration curve. RESULTS Computed tomography reported regional lymph node status, NIC of PVP, and ECVf were independent predictors for Ki-67 expression prediction. The AUCs of the Radiology, DECT, and DECT-Radiology models were 0.705, 0.884, and 0.905, respectively, in the training cohort, and 0.669, 0.835, and 0.865, respectively, in the validation cohort. The DECT-Radiology nomogram was established based on the DECT-Radiology model, which showed the highest net benefit and satisfactory consistency. CONCLUSIONS The DECT-Radiology model shows favourable predictive efficacy for Ki-67 expression, which may be of value for clinical decision-making in PDAC patients. CRITICAL RELEVANCE STATEMENT The DECT-Radiology model could contribute to the preoperative and non-invasive assessment of Ki-67 expression of PDAC, which may help clinicians to screen out PDAC patients with high Ki-67 expression. KEY POINTS • Dual-energy computed tomography (DECT) can predict Ki-67 in pancreatic ductal adenocarcinoma (PDAC). • The DECT-Radiology model facilitates preoperative and non-invasive assessment of PDAC Ki-67 expression. • The nomogram may help screen out PDAC patients with high Ki-67 expression.
Collapse
Affiliation(s)
- Youjia Wen
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Zuhua Song
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Qian Li
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Dan Zhang
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Xiaojiao Li
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Jiayi Yu
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Zongwen Li
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Xiaofang Ren
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Jiayan Zhang
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Qian Liu
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Jie Huang
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Dan Zeng
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China
| | - Zhuoyue Tang
- Chongqing General Hospital, No.118, Xingguang Avenue, Liangjiang New Area, Chongqing, China.
| |
Collapse
|
4
|
Tripathi S, Tabari A, Mansur A, Dabbara H, Bridge CP, Daye D. From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer. Diagnostics (Basel) 2024; 14:174. [PMID: 38248051 PMCID: PMC10814554 DOI: 10.3390/diagnostics14020174] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024] Open
Abstract
Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.
Collapse
Affiliation(s)
- Satvik Tripathi
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Harvard Medical School, Boston, MA 02115, USA
| | - Arian Mansur
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Harvard Medical School, Boston, MA 02115, USA
| | - Harika Dabbara
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA;
| | - Christopher P. Bridge
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| |
Collapse
|
5
|
Chen J, Li Z, Han Z, Kang D, Ma J, Yi Y, Fu F, Guo W, Zheng L, Xi G, He J, Qiu L, Li L, Zhang Q, Wang C, Chen J. Prognostic value of tumor necrosis based on the evaluation of frequency in invasive breast cancer. BMC Cancer 2023; 23:530. [PMID: 37296414 DOI: 10.1186/s12885-023-10943-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Accepted: 05/10/2023] [Indexed: 06/12/2023] Open
Abstract
BACKGROUND Tumor necrosis (TN) was associated with poor prognosis. However, the traditional classification of TN ignored spatial intratumor heterogeneity, which may be associated with important prognosis. The purpose of this study was to propose a new method to reveal the hidden prognostic value of spatial heterogeneity of TN in invasive breast cancer (IBC). METHODS Multiphoton microscopy (MPM) was used to obtain multiphoton images from 471 patients. According to the relative spatial positions of TN, tumor cells, collagen fibers and myoepithelium, four spatial heterogeneities of TN (TN1-4) were defined. Based on the frequency of individual TN, TN-score was obtained to investigate the prognostic value of TN. RESULTS Patients with high-risk TN had worse 5-year disease-free survival (DFS) than patients with no necrosis (32.5% vs. 64.7%; P < 0.0001 in training set; 45.8% vs. 70.8%; P = 0.017 in validation set), while patients with low-risk TN had a 5-year DFS comparable to patients with no necrosis (60.0% vs. 64.7%; P = 0.497 in training set; 59.8% vs. 70.8%; P = 0.121 in validation set). Furthermore, high-risk TN "up-staged" the patients with IBC. Patients with high-risk TN and stage I tumors had a 5-year DFS comparable to patients with stage II tumors (55.6% vs. 62.0%; P = 0.565 in training set; 62.5% vs. 66.3%; P = 0.856 in validation set), as well as patients with high-risk TN and stage II tumors had a 5-year DFS comparable to patients with stage III tumors (33.3% vs. 24.6%; P = 0.271 in training set; 44.4% vs. 39.3%; P = 0.519 in validation set). CONCLUSIONS TN-score was an independent prognostic factor for 5-year DFS. Only high-risk TN was associated with poor prognosis. High-risk TN "up-staged" the patients with IBC. Incorporating TN-score into staging category could improve its performance to stratify patients.
Collapse
Affiliation(s)
- Jianhua Chen
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, 350117, China
- College of Life Science, Fujian Normal University, Fuzhou, 350117, China
| | - Zhijun Li
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, 350117, China
| | - Zhonghua Han
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Deyong Kang
- Department of Pathology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Jianli Ma
- Department of Radiation Oncology, Harbin Medical University Cancer Hospital, Harbin, 150081, China
| | - Yu Yi
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, 350117, China
| | - Fangmeng Fu
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Wenhui Guo
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Liqin Zheng
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, 350117, China
| | - Gangqin Xi
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, 350117, China
| | - Jiajia He
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, 350117, China
| | - Lida Qiu
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, 350117, China
- College of Physics and Electronic Information Engineering, Minjiang University, Fuzhou, 350108, China
| | - Lianhuang Li
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, 350117, China
| | - Qingyuan Zhang
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150081, China
| | - Chuan Wang
- Department of Breast Surgery, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
| | - Jianxin Chen
- Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, 350117, China.
| |
Collapse
|
6
|
Paragomi P, Wang R, Huang JY, Midttun Ø, Ulvik A, Ueland PM, Koh WP, Yuan JM, Luu HN. The Association Between Serum Riboflavin and Flavin Mononucleotide With Pancreatic Cancer: Findings From a Prospective Cohort Study. Pancreas 2023; 52:e127-e134. [PMID: 37523604 PMCID: PMC10399971 DOI: 10.1097/mpa.0000000000002220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/02/2023]
Abstract
OBJECTIVES Vitamin B2 (riboflavin) has a prime role in metabolic reactions imperative to cell cycle and proliferation. We investigated the associations between serum concentrations of riboflavin flavin mononucleotide with the risk of pancreatic cancer in a nested case-control study involving 58 cases and 104 matched controls. METHODS The Singapore Chinese Health Study, an ongoing prospective cohort study of 63,257 Chinese Singaporeans. Conditional logistic regression method was used to evaluate these associations with adjustment for potential confounders including the level of education, body mass index, smoking status, alcohol consumption, history of diabetes, serum cotinine and pyridoxal 5'-phosphate, estimated glomerular filtration rate, and total methyl donors (ie, the sum of serum choline, betaine, and methionine). RESULTS The risk of pancreatic cancer increased with increasing level of serum riboflavin in a dose-dependent manner, especially in men (Ptrend = 0.003). The odds ratio (95% confidence intervals) of pancreatic cancer for the second and third tertiles of serum riboflavin, compared with the lowest tertile, were 9.92 (1.65-59.77) and 25.59 (3.09-212.00), respectively. This positive association was stronger in individuals with a longer follow-up period (≥7 years). CONCLUSIONS The findings suggest a potential role of riboflavin in the development of pancreatic cancer, especially in men.
Collapse
Affiliation(s)
- Pedram Paragomi
- From the UPMC Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA
| | - Renwei Wang
- From the UPMC Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA
| | - Joyce Y Huang
- From the UPMC Hillman Cancer Center, University of Pittsburgh Medical Center, Pittsburgh, PA
| | | | - Arve Ulvik
- Laboratory of Clinical Biochemistry, Haukeland University Hospital, Bergen, Norway
| | | | | | | | | |
Collapse
|
7
|
Szymoński K, Chmura Ł, Lipiec E, Adamek D. Vibrational spectroscopy – are we close to finding a solution for early pancreatic cancer diagnosis? World J Gastroenterol 2023; 29:96-109. [PMID: 36683712 PMCID: PMC9850953 DOI: 10.3748/wjg.v29.i1.96] [Citation(s) in RCA: 1] [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: 07/08/2022] [Revised: 10/03/2022] [Accepted: 10/31/2022] [Indexed: 01/04/2023] Open
Abstract
Pancreatic cancer (PC) is an aggressive and lethal neoplasm, ranking seventh in the world for cancer deaths, with an overall 5-year survival rate of below 10%. The knowledge about PC pathogenesis is rapidly expanding. New aspects of tumor biology, including its molecular and morphological heterogeneity, have been reported to explain the complicated “cross-talk” that occurs between the cancer cells and the tumor stroma or the nature of pancreatic ductal adenocarcinoma-associated neural remodeling. Nevertheless, currently, there are no specific and sensitive diagnosis options for PC. Vibrational spectroscopy (VS) shows a promising role in the development of early diagnosis technology. In this review, we summarize recent reports about improvements in spectroscopic methodologies, briefly explain and highlight the drawbacks of each of them, and discuss available solutions. The important aspects of spectroscopic data evaluation with multivariate analysis and a convolutional neural network methodology are depicted. We conclude by presenting a study design for systemic verification of the VS-based methods in the diagnosis of PC.
Collapse
Affiliation(s)
- Krzysztof Szymoński
- Department of Pathomorphology, Jagiellonian University Medical College, Cracow 33-332, Poland
- Department of Pathomorphology, University Hospital in Cracow, Cracow 31-501, Poland
| | - Łukasz Chmura
- Department of Pathomorphology, Jagiellonian University Medical College, Cracow 33-332, Poland
- Department of Pathomorphology, University Hospital in Cracow, Cracow 31-501, Poland
| | - Ewelina Lipiec
- M. Smoluchowski Institute of Physics, Jagiellonian University, Cracow 30-348, Poland
| | - Dariusz Adamek
- Department of Pathomorphology, University Hospital in Cracow, Cracow 31-501, Poland
- Department of Neuropathology, Jagiellonian University Medical College, Cracow 33-332, Poland
| |
Collapse
|
8
|
Amrutkar M, Verbeke CS, Finstadsveen AV, Dorg L, Labori KJ, Gladhaug IP. Neoadjuvant chemotherapy is associated with an altered metabolic profile and increased cancer stemness in patients with pancreatic ductal adenocarcinoma. Mol Oncol 2022; 17:59-81. [PMID: 36400567 PMCID: PMC9812839 DOI: 10.1002/1878-0261.13344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/03/2022] [Accepted: 11/17/2022] [Indexed: 11/21/2022] Open
Abstract
The modest clinical benefits of neoadjuvant chemotherapy (NAT) in pancreatic ductal adenocarcinoma (PDAC) are associated with a lack of robust data on treatment-induced changes in the tumor. To this end, comparative proteomic profiling of tumor tissue samples from treatment-naïve (TN, n = 20) and NAT-treated (n = 22) PDACs was performed. Differentially expressed proteins were identified and correlation with overall survival (OS) was performed. Tumors were also examined for histopathological changes and expression of cancer stem cell (CSC) markers. Serum from 33 matched patients was analyzed for metabolic markers. Cytotoxicity, proliferation, and expression of CSC markers were assessed in chemoresistant Panc-1 and Mia PaCa-2 cells. Of the 2265 proteins identified, 227 and 144 proteins showed significantly altered expression and differential phosphorylation, respectively, in NAT compared with TN samples. The majority of these were metabolism-related proteins, and 14 of these correlated moderately with OS. NAT-treated tumors and chemoresistant cancer cells showed increased expression of CSC markers. Serum ALDH1A1 was higher in NAT compared with TN. Differentially phosphorylated proteins were mainly involved in cytoskeleton organization, cell locomotion, motility, and migration, and 17 of these showed a strong positive correlation with OS. This study provides evidence of the effects of NAT on PDAC metabolism at both the tumor and the systemic levels. NAT-treated tumors showed significantly lower expression of metabolic proteins, and patients who underwent NAT showed reduced serum lactate and high-density lipoprotein-cholesterol. Lastly, cancer cells that survived cytotoxic treatment expressed higher CSC markers, both in vivo and in vitro.
Collapse
Affiliation(s)
- Manoj Amrutkar
- Department of PathologyOslo University HospitalNorway,Department of Pharmacology, Institute of Clinical MedicineUniversity of OsloNorway
| | - Caroline S. Verbeke
- Department of PathologyOslo University HospitalNorway,Department of Pathology, Institute of Clinical MedicineUniversity of OsloNorway
| | | | - Linda Dorg
- Department of Pathology, Institute of Clinical MedicineUniversity of OsloNorway
| | - Knut Jørgen Labori
- Department of Hepato‐Pancreato‐Biliary Surgery, Institute of Clinical MedicineUniversity of OsloNorway,Department of Hepato‐Pancreato‐Biliary SurgeryOslo University HospitalNorway
| | - Ivar P. Gladhaug
- Department of Hepato‐Pancreato‐Biliary Surgery, Institute of Clinical MedicineUniversity of OsloNorway,Department of Hepato‐Pancreato‐Biliary SurgeryOslo University HospitalNorway
| |
Collapse
|
9
|
Schuurmans M, Alves N, Vendittelli P, Huisman H, Hermans J. Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging. Cancers (Basel) 2022; 14:cancers14143498. [PMID: 35884559 PMCID: PMC9316850 DOI: 10.3390/cancers14143498] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 07/07/2022] [Accepted: 07/15/2022] [Indexed: 11/16/2022] Open
Abstract
Simple Summary Pancreatic ductal adenocarcinoma (PDAC) is one of the deadliest cancers worldwide, associated with a 98% loss of life expectancy and a 30% increase in disability-adjusted life years. Image-based artificial intelligence (AI) can help improve outcomes for PDAC given that current clinical guidelines are non-uniform and lack evidence-based consensus. However, research on image-based AI for PDAC is too scattered and lacking in sufficient quality to be incorporated into clinical workflows. In this review, an international, multi-disciplinary team of the world’s leading experts in pancreatic cancer breaks down the patient pathway and pinpoints the current clinical touchpoints in each stage. The available PDAC imaging AI literature addressing each pathway stage is then rigorously analyzed, and current performance and pitfalls are identified in a comprehensive overview. Finally, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed. Abstract Pancreatic ductal adenocarcinoma (PDAC), estimated to become the second leading cause of cancer deaths in western societies by 2030, was flagged as a neglected cancer by the European Commission and the United States Congress. Due to lack of investment in research and development, combined with a complex and aggressive tumour biology, PDAC overall survival has not significantly improved the past decades. Cross-sectional imaging and histopathology play a crucial role throughout the patient pathway. However, current clinical guidelines for diagnostic workup, patient stratification, treatment response assessment, and follow-up are non-uniform and lack evidence-based consensus. Artificial Intelligence (AI) can leverage multimodal data to improve patient outcomes, but PDAC AI research is too scattered and lacking in quality to be incorporated into clinical workflows. This review describes the patient pathway and derives touchpoints for image-based AI research in collaboration with a multi-disciplinary, multi-institutional expert panel. The literature exploring AI to address these touchpoints is thoroughly retrieved and analysed to identify the existing trends and knowledge gaps. The results show absence of multi-institutional, well-curated datasets, an essential building block for robust AI applications. Furthermore, most research is unimodal, does not use state-of-the-art AI techniques, and lacks reliable ground truth. Based on this, the future research agenda for clinically relevant, image-driven AI in PDAC is proposed.
Collapse
Affiliation(s)
- Megan Schuurmans
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Natália Alves
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
- Correspondence: (M.S.); (N.A.)
| | - Pierpaolo Vendittelli
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - Henkjan Huisman
- Diagnostic Image Analysis Group, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands; (P.V.); (H.H.)
| | - John Hermans
- Department of Medical Imaging, Radboud University Medical Center, 6500 HB Nijmegen, The Netherlands;
| |
Collapse
|
10
|
MRI Radiogenomics in Precision Oncology: New Diagnosis and Treatment Method. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:2703350. [PMID: 35845886 PMCID: PMC9282990 DOI: 10.1155/2022/2703350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/02/2022] [Revised: 05/04/2022] [Accepted: 05/25/2022] [Indexed: 11/21/2022]
Abstract
Precision medicine for cancer affords a new way for the most accurate and effective treatment to each individual cancer. Given the high time-evolving intertumor and intratumor heterogeneity features of personal medicine, there are still several obstacles hindering its diagnosis and treatment in clinical practice regardless of extensive exploration on it over the past years. This paper is to investigate radiogenomics methods in the literature for precision medicine for cancer focusing on the heterogeneity analysis of tumors. Based on integrative analysis of multimodal (parametric) imaging and molecular data in bulk tumors, a comprehensive analysis and discussion involving the characterization of tumor heterogeneity in imaging and molecular expression are conducted. These investigations are intended to (i) fully excavate the multidimensional spatial, temporal, and semantic related information regarding high-dimensional breast magnetic resonance imaging data, with integration of the highly specific structured data of genomics and combination of the diagnosis and cognitive process of doctors, and (ii) establish a radiogenomics data representation model based on multidimensional consistency analysis with multilevel spatial-temporal correlations.
Collapse
|
11
|
Szymoński K, Milian-Ciesielska K, Lipiec E, Adamek D. Current Pathology Model of Pancreatic Cancer. Cancers (Basel) 2022; 14:2321. [PMID: 35565450 PMCID: PMC9105915 DOI: 10.3390/cancers14092321] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 04/29/2022] [Accepted: 05/05/2022] [Indexed: 02/01/2023] Open
Abstract
Pancreatic cancer (PC) is one of the most aggressive and lethal malignant neoplasms, ranking in seventh place in the world in terms of the incidence of death, with overall 5-year survival rates still below 10%. The knowledge about PC pathomechanisms is rapidly expanding. Daily reports reveal new aspects of tumor biology, including its molecular and morphological heterogeneity, explain complicated "cross-talk" that happens between the cancer cells and tumor stroma, or the nature of the PC-associated neural remodeling (PANR). Staying up-to-date is hard and crucial at the same time. In this review, we are focusing on a comprehensive summary of PC aspects that are important in pathologic reporting, impact patients' outcomes, and bring meaningful information for clinicians. Finally, we show promising new trends in diagnostic technologies that might bring a difference in PC early diagnosis.
Collapse
Affiliation(s)
- Krzysztof Szymoński
- Department of Pathomorphology, Jagiellonian University Medical College, 31-531 Cracow, Poland;
- Department of Pathomorphology, University Hospital, 30-688 Cracow, Poland;
| | | | - Ewelina Lipiec
- M. Smoluchowski Institute of Physics, Jagiellonian University, 30-348 Cracow, Poland;
| | - Dariusz Adamek
- Department of Pathomorphology, Jagiellonian University Medical College, 31-531 Cracow, Poland;
| |
Collapse
|
12
|
Dent A, Diamandis P. Integrating computational pathology and proteomics to address tumor heterogeneity. J Pathol 2022; 257:445-453. [DOI: 10.1002/path.5905] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 03/20/2022] [Accepted: 03/30/2022] [Indexed: 11/11/2022]
Affiliation(s)
- Anglin Dent
- Department of Laboratory Medicine and Pathobiology University of Toronto Toronto Ontario M5S 1A8 Canada
- Princess Margaret Cancer Center University Health Network, Toronto, Ontario, 610 University Avenue, M5G 2C1 Canada
| | - Phedias Diamandis
- Department of Laboratory Medicine and Pathobiology University of Toronto Toronto Ontario M5S 1A8 Canada
- Princess Margaret Cancer Center University Health Network, Toronto, Ontario, 610 University Avenue, M5G 2C1 Canada
- Laboratory Medicine Program University Health Network, 200 Elizabeth Street, Toronto, ON Toronto Ontario M5G 2C4 Canada
- Department of Medical Biophysics University of Toronto Toronto Ontario M5S 1A8 Canada
| |
Collapse
|
13
|
Perspective: The Mechanobiology of Hepatocellular Carcinoma. Cancers (Basel) 2021; 13:cancers13174275. [PMID: 34503085 PMCID: PMC8428343 DOI: 10.3390/cancers13174275] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 08/20/2021] [Accepted: 08/23/2021] [Indexed: 12/18/2022] Open
Abstract
Hepatocellular carcinoma (HCC) is the second most deadly primary cancer in the world and is thus a major global health challenge. HCC primarily develops in patients with an underlying chronic liver disease, the vast majority with advanced cirrhosis, characterized by increased matrix deposition and liver stiffness. Liver stiffness is highly associated with cancer development and poor patient outcome and is measured clinically to assess cancer risk; cirrhotic livers greatly exceed the threshold stiffness shown to alter hepatocyte cell behavior and to increase the malignancy of cancer cells. Recent studies have shown that cirrhotic liver cells have highly irregular nuclear morphologies and that nuclear deformation mediates mechanosensitive signaling. Separate research has shown that nuclear deformation can increase genetic instability and the accumulation of DNA damage in migrating cancer cells. We hypothesize that the mechanical changes associated with chronic liver disease are drivers of oncogenesis, activating mechanosensitive signaling pathways, increasing rates of DNA damage, and ultimately inducing malignant transformation.
Collapse
|
14
|
Kamionka EM, Qian B, Gross W, Bergmann F, Hackert T, Beretta CA, Dross N, Ryschich E. Collagen Organization Does Not Influence T-Cell Distribution in Stroma of Human Pancreatic Cancer. Cancers (Basel) 2021; 13:cancers13153648. [PMID: 34359549 PMCID: PMC8344977 DOI: 10.3390/cancers13153648] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 06/30/2021] [Accepted: 07/09/2021] [Indexed: 12/18/2022] Open
Abstract
Simple Summary The excessive desmoplasia is the hallmark of human pancreatic cancer that influences the local T-cell-based immune response. In the present work, the stromal collagen organization in normal and malignant pancreatic tissues as well as its relationsship to T-cell distribution in pancreatic cancer were studied. It was found that differences in collagen organization do not change the spatial orientation of T-cell migration and do not influence the availability of tumor cells for T-cells. The results of the study do not support the concept of use of stroma collagen organization for improvement of spatial T-cell distribution in the tumor. Abstract The dominant intrastromal T-cell infiltration in pancreatic cancer is mainly caused by the contact guidance through the excessive desmoplastic reaction and could represent one of the obstacles to an effective immune response in this tumor type. This study analyzed the collagen organization in normal and malignant pancreatic tissues as well as its influence on T-cell distribution in pancreatic cancer. Human pancreatic tissue was analyzed using immunofluorescence staining and multiphoton and SHG microscopy supported by multistep image processing. The influence of collagen alignment on activated T-cells was studied using 3D matrices and time-lapse microscopy. It was found that the stroma of malignant and normal pancreatic tissues was characterized by complex individual organization. T-cells were heterogeneously distributed in pancreatic cancer and there was no relationship between T-cell distribution and collagen organization. There was a difference in the angular orientation of collagen alignment in the peritumoral and tumor-cell-distant stroma regions in the pancreatic ductal adenocarcinoma tissue, but there was no correlation in the T-cell densities between these regions. The grade of collagen alignment did not influence the directionality of T-cell migration in the 3D collagen matrix. It can be concluded that differences in collagen organization do not change the spatial orientation of T-cell migration or influence stromal T-cell distribution in human pancreatic cancer. The results of the present study do not support the rationale of remodeling of stroma collagen organization for improvement of T-cell–tumor cell contact in pancreatic ductal adenocarcinoma.
Collapse
Affiliation(s)
- Eva-Maria Kamionka
- Department of General, Visceral, and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 365/420, 69120 Heidelberg, Germany; (E.-M.K.); (B.Q.); (W.G.); (T.H.)
| | - Baifeng Qian
- Department of General, Visceral, and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 365/420, 69120 Heidelberg, Germany; (E.-M.K.); (B.Q.); (W.G.); (T.H.)
| | - Wolfgang Gross
- Department of General, Visceral, and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 365/420, 69120 Heidelberg, Germany; (E.-M.K.); (B.Q.); (W.G.); (T.H.)
| | - Frank Bergmann
- Department of Pathology, Heidelberg University Hospital, Im Neuenheimer Feld 224, 69120 Heidelberg, Germany;
| | - Thilo Hackert
- Department of General, Visceral, and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 365/420, 69120 Heidelberg, Germany; (E.-M.K.); (B.Q.); (W.G.); (T.H.)
| | - Carlo A. Beretta
- CellNetworks Math-Clinic, University of Heidelberg, Bioquant BQ001, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany;
- Institute for Anatomy and Cell Biology, University of Heidelberg, Im Neuenheimer Feld 307, 69120 Heidelberg, Germany
| | - Nicolas Dross
- Nikon Imaging Center, University of Heidelberg, 69120 Heidelberg, Germany;
| | - Eduard Ryschich
- Department of General, Visceral, and Transplantation Surgery, Heidelberg University Hospital, Im Neuenheimer Feld 365/420, 69120 Heidelberg, Germany; (E.-M.K.); (B.Q.); (W.G.); (T.H.)
- Correspondence: ; Tel.: +49-6221-56-6110; Fax: +49-6221-56-5199
| |
Collapse
|