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Zhang Z, Zhang Y, Hu F, Xie T, Liu W, Xiang H, Li X, Chen L, Zhou Z. Value of diffusion kurtosis MR imaging and conventional diffusion weighed imaging for evaluating response to first-line chemotherapy in unresectable pancreatic cancer. Cancer Imaging 2024; 24:29. [PMID: 38409049 PMCID: PMC10898033 DOI: 10.1186/s40644-024-00674-y] [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: 01/02/2024] [Accepted: 02/15/2024] [Indexed: 02/28/2024] Open
Abstract
OBJECTIVE To investigate the diagnostic value of diffusion kurtosis magnetic resonance imaging (DKI) and conventional diffusion-weighted imaging (DWI) for evaluating the response to first-line chemotherapy in unresectable pancreatic cancer. MATERIALS AND METHODS We retrospectively analyzed 21 patients with clinically and pathologically confirmed unresected pancreatic cancer who received palliative chemotherapy. Three-tesla MRI examinations containing DWI sequences with b values of 0, 100, 700, 1400, and 2100 s/mm2 were performed before and after chemotherapy. Parameters included the apparent diffusion coefficient (ADC), mean diffusion coefficient (MD), and mean diffusional kurtosis (MK). The performances of the DWI and DKI parameters in distinguishing the response to chemotherapy were evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC) curve. Overall survival (OS) was calculated from the date of first treatment to the date of death or the latest follow-up date. RESULTS The ADCchange and MDchange were significantly higher in the responding group (PR group) than in the nonresponding group (non-PR group) (ADCchange: 0.21 ± 0.05 vs. 0.11 ± 0.09, P = 0.02; MDchange: 0.37 ± 0.24 vs. 0.10 ± 0.12, P = 0.002). No statistical significance was shown when comparing ADCpre, ADCpost, MKpre, MKpost, MKchange, MDpre, and MDpost between the PR and non-PR groups. The ROC curve analysis indicated that MDchange (AUC = 0.898, cutoff value = 0.7143) performed better than ADCchange (AUC = 0.806, cutoff value = 0.1369) in predicting the response to chemotherapy. CONCLUSION The ADCchange and MDchange demonstrated strong potential for evaluating the response to chemotherapy in unresectable pancreatic cancer. The MDchange showed higher specificity in the classification of PR and non-PR than the ADCchange. Other parameters, including ADCpre, ADCpost, MKpre, MKpost, MKchange, MDpre, and MDpost, are not suitable for response evaluation. The combined model SUMchange demonstrated superior performance compared to the individual DWI and DKI models. Further experiments are needed to evaluate the potential of DWI and DKI parameters in predicting the prognosis of patients with unresectable pancreatic cancer.
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Affiliation(s)
- Zehua Zhang
- Department of Radiology, Minhang Branch, Fudan University Shanghai Cancer Center, No. 106, Ruili Road, 201100, Shanghai, China
| | - Yuqin Zhang
- Department of Colorectal Surgery, Minhang Branch, Fudan University Shanghai Cancer Center, No. 106, Ruili Road, 201100, Shanghai, China
| | - Feixiang Hu
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270, Dongan Road, 200032, Shanghai, China
| | - Tiansong Xie
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270, Dongan Road, 200032, Shanghai, China
| | - Wei Liu
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270, Dongan Road, 200032, Shanghai, China
| | - Huijing Xiang
- Department of Radiology, Minhang Branch, Fudan University Shanghai Cancer Center, No. 106, Ruili Road, 201100, Shanghai, China
| | - Xiangxiang Li
- Nursing department, Minhang Branch, Fudan University Shanghai Cancer Center, No. 106. Ruili Road, 201100, Shanghai, China
| | - Lei Chen
- Department of Radiology, Minhang Branch, Fudan University Shanghai Cancer Center, No. 106, Ruili Road, 201100, Shanghai, China.
| | - Zhengrong Zhou
- Department of Radiology, Minhang Branch, Fudan University Shanghai Cancer Center, No. 106, Ruili Road, 201100, Shanghai, China.
- Department of Radiology, Fudan University Shanghai Cancer Center, No. 270, Dongan Road, 200032, Shanghai, China.
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Cai C, Hu T, Rong Z, Gong J, Tong T. Prognostic prediction value of the clinical-radiomics tumour-stroma ratio in locally advanced rectal cancer. Eur J Radiol 2024; 170:111254. [PMID: 38091662 DOI: 10.1016/j.ejrad.2023.111254] [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: 08/29/2023] [Revised: 11/08/2023] [Accepted: 12/05/2023] [Indexed: 01/16/2024]
Abstract
PURPOSE To develop and validate a radiomics model based on high-resolution T2WI and a clinical-radiomics model for tumour-stroma ratio (TSR) evaluation with a gold standard of TSR evaluated by rectal specimens without therapeutic interference and further apply them in prognosis prediction of locally advanced rectal cancer (LARC) patients who received neoadjuvant chemoradiotherapy. METHODS A total of 178 patients (mean age: 59.35, range 20-85 years; 65 women and 113 men) with rectal cancer who received surgery alone from January 2016 to October 2020 were enrolled and randomly separated at a ratio of 7:3 into training and validation sets. A senior radiologist reviewed after 2 readers manually delineated the whole tumour in consensus on preoperative high-resolution T2WI in the training set. A total of 1046 features were then extracted, and recursive feature elimination embedded with leave-one-out cross validation was applied to select features, with which an MR-TSR evaluation model was built containing 6 filtered features via a support vector machine classifier trained by comparing patients' pathological TSR. Stepwise logistic regression was employed to integrate clinical factors with the radiomics model (Fusion-TSR) in the training set. Later, the MR-TSR and Fusion-TSR models were replicated in the validation set for diagnostic effectiveness evaluation. Subsequently, 243 patients (mean age: 53.74, range 23-74 years; 63 women and 180 men) with LARC from October 2012 to September 2017 who were treated with NCRT prior to surgery and underwent standard pretreatment rectal MR examination were enrolled. The MR-TSR and Fusion-TSR were applied, and the Kaplan-Meier method and log-rank test were used to compare the survival of patients with different MR-TSR and Fusion-TSR. Cox proportional hazards regression was used to calculate the hazard ratio (HR). RESULTS Both the MR-TSR and Fusion-TSR models were validated with favourable diagnostic power: the AUC of the MR-TSR was 0.77 (p = 0.01; accuracy = 69.8 %, sensitivity = 88.9 %, specificity = 65.9 %, PPV = 34.8 %, NPV = 96.7 %), while the AUC of the Fusion-TSR was 0.76 (p = 0.014; accuracy = 67.9 %, sensitivity = 88.9 %, specificity = 63.6 %, PPV = 33.3 %, NPV = 96.6 %), outperforming their effectiveness in the training set: the AUC of the MR-TSR was 0.65 (p = 0.035; accuracy = 66.4 %, sensitivity = 61.9 %, specificity = 67.3 %, PPV = 27.7 %, NPV = 90.0 %), while the AUC of the Fusion-TSR was 0.73 (p = 0.001; accuracy = 73.6 %, sensitivity = 71.4 %, specificity = 74.0 %, PPV = 35.73 %, NPV = 92.8 %). With further prognostic analysis, the MR-TSR was validated as a significant prognostic factor for DFS in LARC patients treated with NCRT (p = 0.020, HR = 1.662, 95 % CI = 1.077-2.565), while the Fusion-TSR was a significant prognostic factor for OS (p = 0.005, HR = 2.373, 95 % CI = 1.281-4.396). CONCLUSIONS We developed and validated a radiomics TSR and a clinical-radiomics TSR model and successfully applied them to better risk stratification for LARC patients receiving NCRT and for better decision making.
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Affiliation(s)
- Chongpeng Cai
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dongan Rd, Shanghai 200032, China
| | - Tingdan Hu
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dongan Rd, Shanghai 200032, China
| | - Zening Rong
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dongan Rd, Shanghai 200032, China
| | - Jing Gong
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dongan Rd, Shanghai 200032, China.
| | - Tong Tong
- Department of Radiology, Fudan University Shanghai Cancer Center, Department of Oncology, Shanghai Medical College, Fudan University, No. 270, Dongan Rd, Shanghai 200032, China.
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Liao H, Yuan J, Liu C, Zhang J, Yang Y, Liang H, Jiang S, Chen S, Li Y, Liu Y. Feasibility and effectiveness of automatic deep learning network and radiomics models for differentiating tumor stroma ratio in pancreatic ductal adenocarcinoma. Insights Imaging 2023; 14:223. [PMID: 38129708 PMCID: PMC10739634 DOI: 10.1186/s13244-023-01553-z] [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: 08/23/2023] [Accepted: 10/28/2023] [Indexed: 12/23/2023] Open
Abstract
OBJECTIVE This study aims to compare the feasibility and effectiveness of automatic deep learning network and radiomics models in differentiating low tumor stroma ratio (TSR) from high TSR in pancreatic ductal adenocarcinoma (PDAC). METHODS A retrospective analysis was conducted on a total of 207 PDAC patients from three centers (training cohort: n = 160; test cohort: n = 47). TSR was assessed on hematoxylin and eosin-stained specimens by experienced pathologists and divided as low TSR and high TSR. Deep learning and radiomics models were developed including ShuffulNetV2, Xception, MobileNetV3, ResNet18, support vector machine (SVM), k-nearest neighbor (KNN), random forest (RF), and logistic regression (LR). Additionally, the clinical models were constructed through univariate and multivariate logistic regression. Kaplan-Meier survival analysis and log-rank tests were conducted to compare the overall survival time between different TSR groups. RESULTS To differentiate low TSR from high TSR, the deep learning models based on ShuffulNetV2, Xception, MobileNetV3, and ResNet18 achieved AUCs of 0.846, 0.924, 0.930, and 0.941, respectively, outperforming the radiomics models based on SVM, KNN, RF, and LR with AUCs of 0.739, 0.717, 0.763, and 0.756, respectively. Resnet 18 achieved the best predictive performance. The clinical model based on T stage alone performed worse than deep learning models and radiomics models. The survival analysis based on 142 of the 207 patients demonstrated that patients with low TSR had longer overall survival. CONCLUSIONS Deep learning models demonstrate feasibility and superiority over radiomics in differentiating TSR in PDAC. The tumor stroma ratio in the PDAC microenvironment plays a significant role in determining prognosis. CRITICAL RELEVANCE STATEMENT The objective was to compare the feasibility and effectiveness of automatic deep learning networks and radiomics models in identifying the tumor-stroma ratio in pancreatic ductal adenocarcinoma. Our findings demonstrate deep learning models exhibited superior performance compared to traditional radiomics models. KEY POINTS • Deep learning demonstrates better performance than radiomics in differentiating tumor-stroma ratio in pancreatic ductal adenocarcinoma. • The tumor-stroma ratio in the pancreatic ductal adenocarcinoma microenvironment plays a protective role in prognosis. • Preoperative prediction of tumor-stroma ratio contributes to clinical decision-making and guiding precise medicine.
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Affiliation(s)
- Hongfan Liao
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, China
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Jiang Yuan
- College of Computer and Information Science, Southwest University, Chongqing, 400715, China
| | - Chunhua Liu
- Department of Radiology, Daping Hospital, Army Medical University, Chongqing, China
| | - Jiao Zhang
- Department of Radiology, the Third Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yaying Yang
- Department of Pathology, Molecular Medicine and Cancer Research Center, Chongqing Medical University, Chongqing, 400016, China
| | - Hongwei Liang
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China
| | - Song Jiang
- Department of Radiology, Chongqing Ping An Medical Imaging Diagnosis Center, Chongqing, China
| | - Shanxiong Chen
- College of Computer and Information Science, Southwest University, Chongqing, 400715, China.
| | - Yongmei Li
- Department of Radiology, the First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
| | - Yanbing Liu
- College of Medical Informatics, Chongqing Medical University, Chongqing, 400016, China.
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Pancreatic Mass Characterization Using IVIM-DKI MRI and Machine Learning-Based Multi-Parametric Texture Analysis. Bioengineering (Basel) 2023; 10:bioengineering10010083. [PMID: 36671655 PMCID: PMC9854749 DOI: 10.3390/bioengineering10010083] [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: 12/19/2022] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
Non-invasive characterization of pancreatic masses aids in the management of pancreatic lesions. Intravoxel incoherent motion-diffusion kurtosis imaging (IVIM-DKI) and machine learning-based texture analysis was used to differentiate pancreatic masses such as pancreatic ductal adenocarcinoma (PDAC), pancreatic neuroendocrine tumor (pNET), solid pseudopapillary epithelial neoplasm (SPEN), and mass-forming chronic pancreatitis (MFCP). A total of forty-eight biopsy-proven patients with pancreatic masses were recruited and classified into pNET (n = 13), MFCP (n = 6), SPEN (n = 4), and PDAC (n = 25) groups. All patients were scanned for IVIM-DKI sequences acquired with 14 b-values (0 to 2500 s/mm2) on a 1.5T MRI. An IVIM-DKI model with a 3D total variation (TV) penalty function was implemented to estimate the precise IVIM-DKI parametric maps. Texture analysis (TA) of the apparent diffusion coefficient (ADC) and IVIM-DKI parametric map was performed and reduced using the chi-square test. These features were fed to an artificial neural network (ANN) for characterization of pancreatic mass subtypes and validated by 5-fold cross-validation. Receiver operator characteristics (ROC) analyses were used to compute the area under curve (AUC). Perfusion fraction (f) was significantly higher (p < 0.05) in pNET than PDAC. The f showed better diagnostic performance for PDAC vs. MFCP with AUC:0.77. Both pseudo-diffusion coefficient (D*) and f for PDAC vs. pNET showed an AUC of 0.73. ADC and diffusion coefficient (D) showed good diagnostic performance for pNET vs. MFCP with AUC: 0.79 and 0.76, respectively. In the TA of PDAC vs. non-PDAC, f and combined IVIM-DKI parameters showed high accuracy ≥ 84.3% and AUC ≥ 0.84. Mean f and combined IVIM-DKI parameters estimated that the IVIM-DKI model with TV texture features has the potential to be helpful in characterizing pancreatic masses.
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Meng Y, Zhang H, Li Q, Liu F, Fang X, Li J, Yu J, Feng X, Lu J, Bian Y, Shao C. Magnetic Resonance Radiomics and Machine-learning Models: An Approach for Evaluating Tumor-stroma Ratio in Patients with Pancreatic Ductal Adenocarcinoma. Acad Radiol 2022; 29:523-535. [PMID: 34563443 DOI: 10.1016/j.acra.2021.08.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 07/14/2021] [Accepted: 08/09/2021] [Indexed: 12/12/2022]
Abstract
OBJECTIVE To develop and validate a magnetic resonance imaging (MRI)-based machine learning classifier for evaluating the tumor-stroma ratio (TSR) in patients with pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS In this retrospective study, 148 patients with PDAC underwent an MR scan and surgical resection. We used hematoxylin and eosin to quantify the TSR. For each patient, we extracted 1,409 radiomics features and reduced them using the least absolute shrinkage and selection operator logistic regression algorithm. The extreme gradient boosting (XGBoost) classifier was developed using a training set comprising 110 consecutive patients, admitted between December 2016 and December 2017. The model was validated in 38 consecutive patients, admitted between January 2018 and April 2018. We determined the performance of the XGBoost classifier based on its discriminative ability, calibration, and clinical utility. RESULTS A log-rank test revealed significantly longer survival in the TSR-low group. The prediction model displayed good discrimination in the training (area under the curve [AUC], 0.82) and validation set (AUC, 0.78). While the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 77.14%, 75.00%, 0.76%, 0.84%, and 0.65%, respectively, those for the validation set were 58.33%, 92.86%, 0.71%, 0.93%, and 0.57%, respectively. CONCLUSION We developed an XGBoost classifier based on MRI radiomics features, a non-invasive prediction tool that can evaluate the TSR of patients with PDAC. Moreover, it will provide a basis for interstitial targeted therapy selection and monitoring.
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Meng Y, Zhang H, Li Q, Liu F, Fang X, Li J, Yu J, Feng X, Zhu M, Li N, Jing G, Wang L, Ma C, Lu J, Bian Y, Shao C. CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarcinoma. Front Oncol 2021; 11:707288. [PMID: 34820324 PMCID: PMC8606777 DOI: 10.3389/fonc.2021.707288] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 10/18/2021] [Indexed: 12/18/2022] Open
Abstract
Purpose To develop and validate a machine learning classifier based on multidetector computed tomography (MDCT), for the preoperative prediction of tumor-stroma ratio (TSR) expression in patients with pancreatic ductal adenocarcinoma (PDAC). Materials and Methods In this retrospective study, 227 patients with PDAC underwent an MDCT scan and surgical resection. We quantified the TSR by using hematoxylin and eosin staining and extracted 1409 arterial and portal venous phase radiomics features for each patient, respectively. Moreover, we used the least absolute shrinkage and selection operator logistic regression algorithm to reduce the features. The extreme gradient boosting (XGBoost) was developed using a training set consisting of 167 consecutive patients, admitted between December 2016 and December 2017. The model was validated in 60 consecutive patients, admitted between January 2018 and April 2018. We determined the XGBoost classifier performance based on its discriminative ability, calibration, and clinical utility. Results We observed low and high TSR in 91 (40.09%) and 136 (59.91%) patients, respectively. A log-rank test revealed significantly longer survival for patients in the TSR-low group than those in the TSR-high group. The prediction model revealed good discrimination in the training (area under the curve [AUC]= 0.93) and moderate discrimination in the validation set (AUC= 0.63). While the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 94.06%, 81.82%, 0.89, 0.89, and 0.90, respectively, those for the validation set were 85.71%, 48.00%, 0.70, 0.70, and 0.71, respectively. Conclusions The CT radiomics-based XGBoost classifier provides a potentially valuable noninvasive tool to predict TSR in patients with PDAC and optimize risk stratification.
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Affiliation(s)
- Yinghao Meng
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China.,Department of Radiology, No.971 Hospital of Navy, Qingdao, Shandong, China
| | - Hao Zhang
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Qi Li
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Fang Liu
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Xu Fang
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Jing Li
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Jieyu Yu
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Xiaochen Feng
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Mengmeng Zhu
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Na Li
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Guodong Jing
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Li Wang
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Chao Ma
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Jianping Lu
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Yun Bian
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Chengwei Shao
- Department of Radiology, Changhai Hospital, Naval Medical University, Shanghai, China
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Gündel B, Liu X, Löhr M, Heuchel R. Pancreatic Ductal Adenocarcinoma: Preclinical in vitro and ex vivo Models. Front Cell Dev Biol 2021; 9:741162. [PMID: 34746135 PMCID: PMC8569794 DOI: 10.3389/fcell.2021.741162] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Accepted: 10/04/2021] [Indexed: 12/13/2022] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the most overlooked cancers despite its dismal median survival time of 6 months. The biggest challenges in improving patient survival are late diagnosis due to lack of diagnostic markers, and limited treatment options due to almost complete therapy resistance. The past decades of research identified the dense stroma and the complex interplay/crosstalk between the cancer- and the different stromal cells as the main culprits for the slow progress in improving patient outcome. For better ex vivo simulation of this complex tumor microenvironment the models used in PDAC research likewise need to become more diverse. Depending on the focus of the investigation, several in vitro and in vivo models for PDAC have been established in the past years. Particularly, 3D cell culture such as spheroids and organoids have become more frequently used. This review aims to examine current PDAC in vitro models, their inherent limitations, and their successful implementations in research.
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Affiliation(s)
- Beate Gündel
- Pancreas Cancer Research Lab, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Huddinge, Sweden
| | - Xinyuan Liu
- Pancreas Cancer Research Lab, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Huddinge, Sweden
| | - Matthias Löhr
- Pancreas Cancer Research Lab, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Huddinge, Sweden
- Department of Upper GI, C1:77, Karolinska Comprehensive Cancer Center, Stockholm, Sweden
| | - Rainer Heuchel
- Pancreas Cancer Research Lab, Department of Clinical Science, Intervention and Technology (CLINTEC), Karolinska Institutet, Huddinge, Sweden
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Pijnappel EN, Wassenaar NPM, Gurney-Champion OJ, Klaassen R, van der Lee K, Pleunis-van Empel MCH, Richel DJ, Legdeur MC, Nederveen AJ, van Laarhoven HWM, Wilmink JW. Phase I/II Study of LDE225 in Combination with Gemcitabine and Nab-Paclitaxel in Patients with Metastatic Pancreatic Cancer. Cancers (Basel) 2021; 13:4869. [PMID: 34638351 PMCID: PMC8507646 DOI: 10.3390/cancers13194869] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Revised: 09/17/2021] [Accepted: 09/24/2021] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Desmoplasia is a central feature of the tumor microenvironment in pancreatic ductal adenocarcinoma (PDAC). LDE225 is a pharmacological Hedgehog signaling pathway inhibitor and is thought to specifically target tumor stroma. We investigated the combined use of LDE225 and chemotherapy to treat PDAC patients. METHODS This was a multi-center, phase I/II study for patients with metastatic PDAC establishing the maximum tolerated dose of LDE225 co-administered with gemcitabine and nab-paclitaxel (phase I) and evaluating the efficacy and safety of the treatment combination after prior FOLFIRINOX treatment (phase II). Tumor microenvironment assessment was performed with quantitative MRI using intra-voxel incoherent motion diffusion weighted MRI (IVIM-DWI) and dynamic contrast-enhanced (DCE) MRI. RESULTS The MTD of LDE225 was 200 mg once daily co-administered with gemcitabine 1000 mg/m2 and nab-paclitaxel 125 mg/m2. In phase II, six therapy-related grade 4 adverse events (AE) and three grade 5 were observed. In 24 patients, the target lesion response was evaluable. Three patients had partial response (13%), 14 patients showed stable disease (58%), and 7 patients had progressive disease (29%). Median overall survival (OS) was 6 months (IQR 3.9-8.1). Blood plasma fraction (DCE) and diffusion coefficient (IVIM-DWI) significantly increased during treatment. Baseline perfusion fraction could predict OS (>222 days) with 80% sensitivity and 85% specificity. CONCLUSION LDE225 in combination with gemcitabine and nab-paclitaxel was well-tolerated in patients with metastatic PDAC and has promising efficacy after prior treatment with FOLFIRINOX. Quantitative MRI suggested that LDE225 causes increased tumor diffusion and works particularly well in patients with poor baseline tumor perfusion.
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Affiliation(s)
- Esther N. Pijnappel
- Cancer Center Amsterdam, Department of Medical Oncology, Amsterdam University Medical Centers, University of Amsterdam, 1012 Amsterdam, The Netherlands; (E.N.P.); (R.K.); (K.v.d.L.); (D.J.R.); (H.W.M.v.L.)
| | - Nienke P. M. Wassenaar
- Cancer Center Amsterdam, Department of Radiology, Amsterdam University Medical Centers, University of Amsterdam, 1012 Amsterdam, The Netherlands; (N.P.M.W.); (O.J.G.-C.); (A.J.N.)
| | - Oliver J. Gurney-Champion
- Cancer Center Amsterdam, Department of Radiology, Amsterdam University Medical Centers, University of Amsterdam, 1012 Amsterdam, The Netherlands; (N.P.M.W.); (O.J.G.-C.); (A.J.N.)
| | - Remy Klaassen
- Cancer Center Amsterdam, Department of Medical Oncology, Amsterdam University Medical Centers, University of Amsterdam, 1012 Amsterdam, The Netherlands; (E.N.P.); (R.K.); (K.v.d.L.); (D.J.R.); (H.W.M.v.L.)
| | - Koen van der Lee
- Cancer Center Amsterdam, Department of Medical Oncology, Amsterdam University Medical Centers, University of Amsterdam, 1012 Amsterdam, The Netherlands; (E.N.P.); (R.K.); (K.v.d.L.); (D.J.R.); (H.W.M.v.L.)
| | | | - Dick J. Richel
- Cancer Center Amsterdam, Department of Medical Oncology, Amsterdam University Medical Centers, University of Amsterdam, 1012 Amsterdam, The Netherlands; (E.N.P.); (R.K.); (K.v.d.L.); (D.J.R.); (H.W.M.v.L.)
| | - Marie C. Legdeur
- Department of Medical Oncology, Medisch Spectrum Twente, Twente, 7512 Enschede, The Netherlands; (M.C.H.P.-v.E.); (M.C.L.)
| | - Aart J. Nederveen
- Cancer Center Amsterdam, Department of Radiology, Amsterdam University Medical Centers, University of Amsterdam, 1012 Amsterdam, The Netherlands; (N.P.M.W.); (O.J.G.-C.); (A.J.N.)
| | - Hanneke W. M. van Laarhoven
- Cancer Center Amsterdam, Department of Medical Oncology, Amsterdam University Medical Centers, University of Amsterdam, 1012 Amsterdam, The Netherlands; (E.N.P.); (R.K.); (K.v.d.L.); (D.J.R.); (H.W.M.v.L.)
| | - Johanna W. Wilmink
- Cancer Center Amsterdam, Department of Medical Oncology, Amsterdam University Medical Centers, University of Amsterdam, 1012 Amsterdam, The Netherlands; (E.N.P.); (R.K.); (K.v.d.L.); (D.J.R.); (H.W.M.v.L.)
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[The microarchitecture of pancreatic cancer from the point of view of the pathologist and the radiologist]. DER PATHOLOGE 2021; 42:524-529. [PMID: 33956172 PMCID: PMC8390414 DOI: 10.1007/s00292-021-00949-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 03/26/2021] [Indexed: 11/06/2022]
Abstract
Die diagnostische Radiologie ist gemeinsam mit der diagnostischen Pathologie eines der klinisch-morphologischen Fächer, welche in unterschiedlicher makroskopischer bzw. mikroskopischer Auflösung zur Detektion, Charakterisierung sowie zum Ausbreitungsmuster eines Tumors führen. Die klinischen Disziplinen sind oft voneinander getrennt, wenngleich es vor allem in klinischen Tumorboards immer stärkere Verzahnungen gibt. Am Beispiel des Pankreaskarzinoms sind die Korrelationen radiologischer und pathologischer Diagnostik dargestellt.
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Mayer P, Giannakis A, Klauß M, Gaida MM, Bergmann F, Kauczor HU, Feisst M, Hackert T, Loos M. Radiological evaluation of pancreatic cancer: What is the significance of arterial encasement >180° after neoadjuvant treatment? Eur J Radiol 2021; 137:109603. [PMID: 33618209 DOI: 10.1016/j.ejrad.2021.109603] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2020] [Revised: 01/16/2021] [Accepted: 02/08/2021] [Indexed: 12/27/2022]
Abstract
PURPOSE This study aimed to evaluate contrast-enhanced computed tomography (CE-CT) features for prediction of arterial tumor invasion in pancreatic cancer (PDAC) patients in the event of arterial encasement >180° after neoadjuvant (radio-)chemotherapy (NAT). METHODS Seventy PDAC patients with seventy-five arteries showing encasement >180° after completion of NAT were analyzed. All patients underwent surgical exploration with either tumor resection including arterial resection, periadventitial dissection (arterial divestment) or confirmation of locally irresectable disease. CE-CT scans were assessed regarding tumor extent and artery-specific imaging features. The results were analyzed on a per-artery basis. Based on the intraoperative and histopathological findings, encased arteries were classified as either invaded or non-invaded. RESULTS Eighteen radiologically encased arteries were resected; of these, nine had pathologic evidence for tumor invasion. In 42 encased arteries, the tumor could be removed by arterial divestment. In 13 patients with 15 encased arteries, the tumor was deemed technically irresectable. Median tumor size, length of solid soft tissue contact, and degree of circumferential contiguity by solid soft tissue along the artery in CE-CT were significantly lower in the non-invaded than in the invaded artery group (p ≤ 0.017). Imaging features showed moderate accuracies for prediction of arterial invasion (≤72.0 %). The thresholds ≤26 mm for post-NAT solid soft tissue contact and ≤270° for circumferential contiguity by solid soft tissue had high negative predictive values (≥87.5 %). CONCLUSION Although post-NAT prediction of arterial invasion remains difficult, arteries with ≤270° contiguity by soft tissue and arteries with ≤26 mm length of solid soft tissue contact are unlikely to be invaded, with possible implications for surgical planning.
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Affiliation(s)
- P Mayer
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany.
| | - A Giannakis
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - M Klauß
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - M M Gaida
- Institute of Pathology, University Medical Center Mainz, Mainz, Germany
| | - F Bergmann
- Institute of Pathology, Heidelberg University Hospital, Heidelberg, Germany
| | - H U Kauczor
- Department of Diagnostic and Interventional Radiology, Heidelberg University Hospital, Heidelberg, Germany
| | - M Feisst
- Institute of Medical Biometry and Informatics, Heidelberg University, Heidelberg, Germany
| | - T Hackert
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
| | - M Loos
- Department of General, Visceral and Transplantation Surgery, Heidelberg University Hospital, Heidelberg, Germany
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Mayer P, Kraft A, Witzel HR, Marnet N, Hörner N, Roth W, Heinrich S, Hackert T, Bergmann F, Kauczor HU, Klauss M, Gaida MM. Restricted Water Diffusion in Diffusion-Weighted Magnetic Resonance Imaging in Pancreatic Cancer is Associated with Tumor Hypoxia. Cancers (Basel) 2020; 13:cancers13010089. [PMID: 33396818 PMCID: PMC7801953 DOI: 10.3390/cancers13010089] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 12/21/2020] [Accepted: 12/26/2020] [Indexed: 01/05/2023] Open
Abstract
Simple Summary Pancreatic cancer is characterized by a dense network of connective tissue surrounding clusters of cancer cells, the so-called stroma. This ubiquitous connective tissue impairs the delivery of oxygen to cancer cells. This results in hypoxia, which renders the cancer more aggressive and more resistant to treatment. In the present study, we investigated whether the extent of hypoxia in pancreatic cancer can be predicted by magnetic resonance imaging (MRI), a widely used medical imaging technique. More specifically, we used an MRI sequence which can quantitate the random motion (i.e., diffusion) of water molecules within the cancer tissue, namely diffusion-weighted (DW) MRI. We found that the random motion of water molecules is lower in cancer lesions with high hypoxia compared to those with low hypoxia. The findings from our study imply that DW-MRI can be used to identify pancreatic cancer lesions with high hypoxia which are at high risk for treatment failure. Abstract Hypoxia is a hallmark of pancreatic cancer (PDAC) due to its compact and extensive fibrotic tumor stroma. Hypoxia contributes to high lethality of this disease, by inducing a more malignant phenotype and resistance to radiation and chemotherapy. Thus, non-invasive methods to quantify hypoxia could be helpful for treatment decisions, for monitoring, especially in non-resectable tumors, or to optimize personalized therapy. In the present study, we investigated whether tumor hypoxia in PDAC is reflected by diffusion-weighted magnetic resonance imaging (DW-MRI), a functional imaging technique, frequently used in clinical practice for identification and characterization of pancreatic lesions. DW-MRI assesses the tissue microarchitecture by measuring the diffusion of water molecules, which is more restricted in highly compact tissues. As reliable surrogate markers for hypoxia, we determined Blimp-1 (B-lymphocyte induced maturation protein), a transcription factor, as well as vascular endothelial growth factor (VEGF), which are up-regulated in response to hypoxia. In 42 PDAC patients, we observed a close association between restricted water diffusion in DW-MRI and tumor hypoxia in matched samples, as expressed by high levels of Blimp-1 and VEGF in tissue samples of the respective patients. In summary, our data show that DW-MRI is well suited for the evaluation of tumor hypoxia in PDAC and could potentially be used for the identification of lesions with a high hypoxic fraction, which are at high risk for failure of radiochemotherapy.
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Affiliation(s)
- Philipp Mayer
- Clinic for Diagnostic and Interventional Radiology, University Hospital Heidelberg, 69120 Heidelberg, Germany; (H.-U.K.); (M.K.)
- Correspondence: ; Tel.: +49-6221-5637-345
| | - Anne Kraft
- Institute of Pathology, University Medical Center Mainz, JGU-Mainz, 55131 Mainz, Germany; (A.K.); (H.R.W.); (N.M.); (N.H.); (W.R.); (M.M.G.)
| | - Hagen R. Witzel
- Institute of Pathology, University Medical Center Mainz, JGU-Mainz, 55131 Mainz, Germany; (A.K.); (H.R.W.); (N.M.); (N.H.); (W.R.); (M.M.G.)
| | - Nicole Marnet
- Institute of Pathology, University Medical Center Mainz, JGU-Mainz, 55131 Mainz, Germany; (A.K.); (H.R.W.); (N.M.); (N.H.); (W.R.); (M.M.G.)
| | - Nina Hörner
- Institute of Pathology, University Medical Center Mainz, JGU-Mainz, 55131 Mainz, Germany; (A.K.); (H.R.W.); (N.M.); (N.H.); (W.R.); (M.M.G.)
| | - Wilfried Roth
- Institute of Pathology, University Medical Center Mainz, JGU-Mainz, 55131 Mainz, Germany; (A.K.); (H.R.W.); (N.M.); (N.H.); (W.R.); (M.M.G.)
| | - Stefan Heinrich
- Department of Surgery, University Medical Center Mainz, JGU-Mainz, 55131 Mainz, Germany;
| | - Thilo Hackert
- Department of General, Visceral, and Transplantation Surgery, University Hospital Heidelberg, 69120 Heidelberg, Germany;
| | - Frank Bergmann
- Institute of Pathology, University Hospital Heidelberg, 69120 Heidelberg, Germany;
- Clinical Pathology, Klinikum Darmstadt GmbH, 64283 Darmstadt, Germany
| | - Hans-Ulrich Kauczor
- Clinic for Diagnostic and Interventional Radiology, University Hospital Heidelberg, 69120 Heidelberg, Germany; (H.-U.K.); (M.K.)
| | - Miriam Klauss
- Clinic for Diagnostic and Interventional Radiology, University Hospital Heidelberg, 69120 Heidelberg, Germany; (H.-U.K.); (M.K.)
| | - Matthias M. Gaida
- Institute of Pathology, University Medical Center Mainz, JGU-Mainz, 55131 Mainz, Germany; (A.K.); (H.R.W.); (N.M.); (N.H.); (W.R.); (M.M.G.)
- Research Center for Immunotherapy, University Medical Center Mainz, JGU-Mainz, 55131 Mainz, Germany
- Joint Unit Immunopathology, Institute of Pathology, University Medical Center, JGU-Mainz and TRON, Translational Oncology at the University Medical Center, JGU-Mainz, 55131 Mainz, Germany
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