Assessment of response to chemotherapy in pancreatic ductal adenocarcinoma: Comparison between diffusion-weighted MR quantitative parameters and RECIST

2018 ◽  
Vol 104 ◽  
pp. 49-57 ◽  
Author(s):  
Maria Antonietta Bali ◽  
Serena Pullini ◽  
Thierry Metens ◽  
Julie Absil ◽  
Shih-Li Chao ◽  
...  
Keyword(s):  
Pancreatic Ductal Adenocarcinoma ◽  
Ductal Adenocarcinoma ◽  
Quantitative Parameters ◽  
Diffusion Weighted ◽  
Response To Chemotherapy

scholarly journals Homologous Recombination Deficiency in Patients With Pancreatic Ductal Adenocarcinoma and Response to Chemotherapy

2018 ◽  
pp. 1-11 ◽  
Author(s):  
Safi Shahda ◽  
Kirsten M. Timms ◽  
Ashley A. Ibrahim ◽  
Julia E. Reid ◽  
Harvey M. Cramer ◽  
...  
Keyword(s):  
Homologous Recombination ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Locally Advanced ◽  
Progression Free Survival ◽  
Needle Aspiration ◽  
Response To Therapy ◽  
Ductal Adenocarcinoma ◽  
Response To Chemotherapy ◽  
Formalin Fixed Paraffin Embedded ◽  
Mutation Data

Purpose Mutations or copy number abnormalities of genes involved in homologous recombination (HR) occur in pancreatic ductal adenocarcinoma (PDAC). DNA-based measures of HR deficiency (HRD) have been developed and may help identify tumors with better response to DNA-damaging agents. This study aimed to describe the HR pathway mutations and HRD status and determine their association with treatment response and outcome in patients with PDAC. Patients and Methods We performed a retrospective analysis of tumor samples from patients treated at Indiana University for locally advanced or metastatic PDAC. Patients were included if they received gemcitabine plus nanoparticle albumin-bound paclitaxel (control) or fluorouracil, oxaliplatin, leucovorin, and irinotecan (FOLFIRINOX) and had adequate follow-up to assess survival and response to therapy. Tumor analysis generated a three-biomarker HRD score and mutation data for 44 genes. Results Ninety-one samples met inclusion criteria, and 78 samples (formalin-fixed paraffin-embedded, n = 15; fine-needle aspiration, n = 63) generated mutation data. HRD analysis was successful for 57 samples (HRD score: median, 18; range, 5 to 61); the primary cause of failure was low tumor cellularity. Six BRCA1/ 2 mutations were detected, four with HRD scores in the top decile ( P = .011). There was no statistically significant correlation between HRD score and radiographic response (odds ratio per interquartile range, 1.40; P = .32 adjusted for treatment) in either treatment group. In patients treated with FOLFIRINOX, HRD score dichotomized at the median was not associated with progression-free survival (median, 5.3 v 9.4 months for low v high HRD score, respectively; P = .083) or overall survival (median, 11.9 v 10.7 months for low v high HRD score, respectively; P = .76). Conclusion Mutations in DNA repair genes occur in PDAC, and HRD scores can be generated in the majority of patients. The HRD score was not significantly associated with higher response rate or prolonged survival in patients treated with FOLFIRINOX.

scholarly journals Evaluating the Histopathology of Pancreatic Ductal Adenocarcinoma by Intravoxel Incoherent Motion-Diffusion Weighted Imaging Comparing With Diffusion-Weighted Imaging

2021 ◽  
Vol 11 ◽  
Author(s):  
Qi Liu ◽  
Jinggang Zhang ◽  
Man Jiang ◽  
Yue Zhang ◽  
Tongbing Chen ◽  
...  
Keyword(s):  
Diffusion Coefficient ◽  
Tumor Cell ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Cell Density ◽  
Diffusion Weighted Imaging ◽  
Intravoxel Incoherent Motion ◽  
Ductal Adenocarcinoma ◽  
Diffusion Weighted ◽  
Adc Values ◽  
D Values

ObjectivesTo explore the differences between intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and diffusion-weighted imaging (DWI) in evaluating the histopathological characters of pancreatic ductal adenocarcinoma (PDAC).MethodsThis retrospective study enrolled 50 patients with PDAC confirmed by pathology from December 2018 to May 2020. All patients underwent DWI and IVIM-DWI before surgeries. Patients were classified into low- and high-fibrosis groups. Apparent diffusion coefficient (ADC), diffusion coefficient (D), false diffusion coefficient (D*), and perfusion fraction (f) were measured by two radiologists, respectively in GE AW 4.7 post-processing station, wherein ADC values were derived by mono-exponential fits and f, D, D* values were derived by biexponential fits. The tumor tissue was stained with Sirius red, CD34, and CK19 to evaluate fibrosis, microvascular density (MVD), and tumor cell density. Furthermore, the correlation between ADC, D, D*, and f values and histopathological results was analyzed.ResultsThe D values were lower in the high-fibrosis group than in the low-fibrosis group, while the f values were opposite. Further, no statistically significant differences were detected in ADC and D* values between the high- and low-fibrosis groups. The AUC of D and f values had higher evaluation efficacy in the high- and low-fibrosis groups than ADC values. A significant negative correlation was established between D values, and fibrosis and a significant positive correlation were observed between f values and fibrosis. No statistical difference was detected between DWI/IVIM parameters values and MVD or tumor cell density except for the positive correlation between D* values and tumor cell density.ConclusionsD and f values derived from the IVIM model had higher sensitivity and diagnostic performance for grading fibrosis in PDAC compared to the conventional DWI model. IVIM-DWI may have the potential as an imaging biomarker for predicting the fibrosis grade of PDAC.

scholarly journals A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy

2019 ◽  
Author(s):  
Georgios Kaissis ◽  
Sebastian Ziegelmayer ◽  
Fabian Lohöfer ◽  
Katja Steiger ◽  
Hana Algül ◽  
...  
Keyword(s):  
Machine Learning ◽  
Overall Survival ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Median Overall Survival ◽  
Molecular Subtypes ◽  
Supervised Machine Learning ◽  
Ductal Adenocarcinoma ◽  
Chemotherapy Response ◽  
Response To Chemotherapy ◽  
Sensitivity Specificity

AbstractPurposeDevelopment of a supervised machine-learning model capable of predicting clinically relevant molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) from diffusion-weighted-imaging-derived radiomic features.MethodsThe retrospective observational study assessed 55 surgical PDAC patients. Molecular subtypes were defined by immunohistochemical staining of KRT81. Tumors were manually segmented and 1606 radiomic features were extracted withPyRadiomics. A gradient-boosted-tree algorithm (XGBoost) was trained on 70% of the patients (N=28) and tested on 30% (N=17) to predict KRT81+ vs. KRT81-tumor subtypes. The average sensitivity, specificity and ROC-AUC value were calculated. Chemotherapy response was assessed stratified by subtype. Radiomic feature importance was ranked.ResultsThe mean±STDEV sensitivity, specificity and ROC-AUC were 0.90±0.07, 0.92±0.11, and 0.93±0.07, respectively. Patients with a KRT81+ subtype experienced significantly diminished median overall survival compared to KRT81-patients (7.0 vs. 22.6 months, HR 1.44, log-rank-test P=<0.001) and a significantly improved response to gemcitabine-based chemotherapy over FOLFIRINOX (10.14 vs. 3.8 months median overall survival, HR 0.85, P=0.037) compared to KRT81-patients, who responded significantly better to FOLFIRINOX over gemcitabine-based treatment (30.8 vs. 13.4 months median overall survival, HR 0.88, P=0.027).ConclusionsThe machine-learning based analysis of radiomic features enables the prediction of subtypes of PDAC, which are highly relevant for overall patient survival and response to chemotherapy.

scholarly journals Reciprocal regulation of pancreatic ductal adenocarcinoma growth and molecular subtype by HNF4α and SIX1/4

2019 ◽  
Author(s):  
Soledad A. Camolotto ◽  
Veronika K. Belova ◽  
Luke Torre-Healy ◽  
Jeffery M. Vahrenkamp ◽  
Kristofer C. Berrett ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Ductal Adenocarcinoma ◽  
Molecular Subtype ◽  
Ductal Adenocarcinoma ◽  
Reciprocal Regulation ◽  
Expression Of Genes ◽  
Response To Chemotherapy ◽  
Expression Program

AbstractPancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a five-year survival of less than 5%. Transcriptomic analysis has identified two clinically relevant molecular subtypes of PDAC: Classical and Basal-like. The Classical subtype is characterized by a more favorable prognosis and better response to chemotherapy than the Basal-like subtype. The Classical subtype also expresses higher levels of lineage specifiers that regulate endodermal differentiation, including the nuclear receptor HNF4α. Using in vitro and in vivo PDAC models, we show that HNF4α restrains tumor growth and drives tumor cells toward an epithelial identity. Gene expression analysis from murine models and human tumors shows that HNF4α activates expression of genes associated with the Classical subtype. Although HNF4α loss is not sufficient for complete conversion to the Basal-like subtype gene expression profile, HNF4α directly represses SIX4 and SIX1, mesodermal lineage specifiers expressed in the Basal-like subtype. Finally, HNF4α-negative PDAC cells rely on expression of SIX4 and SIX1 for proliferation in vitro and in vivo. Overall, our data show that HNF4α regulates the growth and molecular subtype of PDAC by multiple mechanisms, including activation of the Classical gene expression program and repression of SIX4 and SIX1, which may represent novel dependencies of the Basal-like subtype.

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