Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 20 de 23
Filtrar
1.
J Nucl Med ; 65(8): 1244-1249, 2024 Aug 01.
Artigo em Inglês | MEDLINE | ID: mdl-38991748

RESUMO

177Lu-DOTATATE therapy is an effective treatment for advanced neuroendocrine tumors, despite its dose-limiting hematotoxicity. Herein, the significance of off-target splenic irradiation is unknown. Our study aims to identify predictive markers of peptide receptor radionuclide therapy-induced leukopenia. Methods: We retrospectively analyzed blood counts and imaging data of 88 patients with histologically confirmed, unresectable metastatic neuroendocrine tumors who received 177Lu-DOTATATE treatment at our institution from February 2009 to July 2021. Inclusion criterium was a tumor uptake equivalent to or greater than that in the liver on baseline receptor imaging. We excluded patients with less than 24 mo of follow-up and those patients who received fewer than 4 treatment cycles, additional therapies, or blood transfusions during follow-up. Results: Our study revealed absolute and relative white blood cell counts and relative spleen volume reduction as independent predictors of radiation-induced leukopenia at 24 mo. However, a 30% decline in spleen volume 12 mo after treatment most accurately predicted patients proceeding to leukopenia at 24 mo (receiver operating characteristic area under the curve of 0.91, sensitivity of 0.93, and specificity of 0.90), outperforming all other parameters by far. Conclusion: Automated splenic volume assessments demonstrated superior predictive capabilities for the development of leukopenia in patients undergoing 177Lu-DOTATATE treatment compared with conventional laboratory parameters. The reduction in spleen size proves to be a valuable, routinely available, and quantitative imaging-based biomarker for predicting radiation-induced leukopenia. This suggests potential clinical applications for risk assessment and management.


Assuntos
Leucopenia , Tumores Neuroendócrinos , Octreotida , Compostos Organometálicos , Receptores de Peptídeos , Baço , Humanos , Feminino , Leucopenia/etiologia , Masculino , Baço/diagnóstico por imagem , Baço/efeitos da radiação , Pessoa de Meia-Idade , Octreotida/análogos & derivados , Octreotida/uso terapêutico , Octreotida/efeitos adversos , Estudos Retrospectivos , Idoso , Tumores Neuroendócrinos/radioterapia , Tumores Neuroendócrinos/diagnóstico por imagem , Receptores de Peptídeos/metabolismo , Compostos Organometálicos/efeitos adversos , Compostos Organometálicos/uso terapêutico , Tamanho do Órgão , Adulto , Biomarcadores , Idoso de 80 Anos ou mais
2.
J Clin Med ; 12(23)2023 Nov 26.
Artigo em Inglês | MEDLINE | ID: mdl-38068377

RESUMO

Percutaneous CT-guided biopsy is a frequently performed procedure for the confirmation and molecular workup of hepatic metastases of pancreatic ductal adenocarcinoma (PDAC). Tumor necrosis of primary PDAC has shown a negative prognostic impact in recent studies. This study aims to examine predictability in CT scans and the prognostic impact of necrosis in hepatic metastases of PDAC. In this tertiary-center retrospective cohort study, we included 36 patients with hepatic metastases of PDAC who underwent CT-guided hepatic biopsies. Normalized attenuation of the biopsied metastasis was determined in venous phase contrast-enhanced planning scans obtained prior to biopsy by automatic, threshold-based 3D segmentation and manual, blinded 2D segmentation. A board-certified pathologist specialized in hepatic pathology histologically quantified the tumor necrosis and cellularity of the biopsy cylinders. We found a significant inverse-linear correlation between normalized attenuation and the fraction of necrosis (Pearson's r = 0.51, p < 0.001 for automatic 3D segmentation or Pearson's r = 0.52, p < 0.001 for manual 2D segmentation), whereas no correlation was found with tumor cellularity. Additionally, we discovered that patients with a fraction of necrosis ≥ 20% in metastases had a significantly shorter overall survival (p < 0.035). In summary, tumor necrosis of PDAC metastases can be estimated from contrast-enhanced CT scans, which could help to improve biopsy sample pattern planning. In addition, liver metastatic necrosis may serve as a prognostic biomarker in PDAC.

3.
Metallomics ; 15(10)2023 Oct 04.
Artigo em Inglês | MEDLINE | ID: mdl-37715341

RESUMO

The gadolinium-based contrast agent Gadoxetic acid and the platinum-based antitumor agent Cisplatin were quantitatively imaged in liver and liver cancer (hepatocellular carcinoma, HCC) tissue of rats by means of laser ablation-inductively coupled plasma-mass spectrometry. HCC bearing rats simultaneously received a tail vein injection of the hepatocyte-specific magnetic resonance imaging contrast agent Gadoxetic acid and a transarterial injection of Cisplatin 15 min before sacrifice and liver removal. Resecting HCC with adjacent liver tissue allows the comparison of Gd, Pt, and endogenous elements like Fe, Cu, and Zn in the various tissue types. Region of interest analysis reveals lower concentrations of Gd in HCC and higher Gd content in the adjacent liver, fitting the selective uptake of Gadoxetic acid into hepatocytes. Furthermore, two malignancy grades and their possible impact on the Gadoxetic acid and Cisplatin uptake are compared. For this, four high grade (G3) and two moderate grade (G2) HCCs were analysed, including a control sample each. Gd concentrations were lower in HCC irrespective of the grade of dedifferentiation (G2, G3) compared to adjacent liver. Despite local arterial Cisplatin injection, concentrations of Pt were similar or also reduced in HCC compared to liver tissue. In addition, endogenous Fe, Cu, and Zn were quantified. While Zn was homogenously distributed, higher Fe concentrations were determined in liver tissue compared to HCC. Hotspots of Cu suggest a deregulated copper homeostasis in certain liver lesions. The Gd and Fe distributions are compared in detail with cellular alterations examined by hematoxylin and eosin staining.

4.
Eur Radiol ; 33(10): 6892-6901, 2023 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-37133518

RESUMO

OBJECTIVES: To examine the effect of high-b-value computed diffusion-weighted imaging (cDWI) on solid lesion detection and classification in pancreatic intraductal papillary mucinous neoplasm (IPMN), using endoscopic ultrasound (EUS) and histopathology as a standard of reference. METHODS: Eighty-two patients with known or suspected IPMN were retrospectively enrolled. Computed high-b-value images at b = 1000 s/mm2 were calculated from standard (b = 0, 50, 300, and 600 s/mm2) DWI images for conventional full field-of-view (fFOV, 3 × 3 × 4 mm3 voxel size) DWI. A subset of 39 patients received additional high-resolution reduced-field-of-view (rFOV, 2.5 × 2.5 × 3 mm3 voxel size) DWI. In this cohort, rFOV cDWI was compared against fFOV cDWI additionally. Two experienced radiologists evaluated (Likert scale 1-4) image quality (overall image quality, lesion detection and delineation, fluid suppression within the lesion). In addition, quantitative image parameters (apparent signal-to-noise ratio (aSNR), apparent contrast-to-noise ratio (aCNR), contrast ratio (CR)) were assessed. Diagnostic confidence regarding the presence/absence of diffusion-restricted solid nodules was assessed in an additional reader study. RESULTS: High-b-value cDWI at b = 1000 s/mm2 outperformed acquired DWI at b = 600 s/mm2 regarding lesion detection, fluid suppression, aCNR, CR, and lesion classification (p = < .001-.002). Comparing cDWI from fFOV and rFOV revealed higher image quality in high-resolution rFOV-DWI compared to conventional fFOV-DWI (p ≤ .001-.018). High-b-value cDWI images were rated non-inferior to directly acquired high-b-value DWI images (p = .095-.655). CONCLUSIONS: High-b-value cDWI may improve the detection and classification of solid lesions in IPMN. Combining high-resolution imaging and high-b-value cDWI may further increase diagnostic precision. CLINICAL RELEVANCE STATEMENT: This study shows the potential of computed high-resolution high-sensitivity diffusion-weighted magnetic resonance imaging for solid lesion detection in pancreatic intraductal papillary mucinous neoplasia (IPMN). The technique may enable early cancer detection in patients under surveillance. KEY POINTS: • Computed high-b-value diffusion-weighted imaging (cDWI) may improve the detection and classification of intraductal papillary mucinous neoplasms (IPMN) of the pancreas. • cDWI calculated from high-resolution imaging increases diagnostic precision compared to cDWI calculated from conventional-resolution imaging. • cDWI has the potential to strengthen the role of MRI for screening and surveillance of IPMN, particularly in view of the rising incidence of IPMNs combined with now more conservative therapeutic approaches.


Assuntos
Neoplasias Intraductais Pancreáticas , Neoplasias Pancreáticas , Humanos , Estudos Retrospectivos , Neoplasias Pancreáticas/diagnóstico por imagem , Razão Sinal-Ruído , Imagem de Difusão por Ressonância Magnética/métodos , Pâncreas
5.
JAMA Netw Open ; 6(1): e2253370, 2023 01 03.
Artigo em Inglês | MEDLINE | ID: mdl-36705919

RESUMO

Importance: Differentiating between malignant and benign etiology in large-bowel wall thickening on computed tomography (CT) images can be a challenging task. Artificial intelligence (AI) support systems can improve the diagnostic accuracy of radiologists, as shown for a variety of imaging tasks. Improvements in diagnostic performance, in particular the reduction of false-negative findings, may be useful in patient care. Objective: To develop and evaluate a deep learning algorithm able to differentiate colon carcinoma (CC) and acute diverticulitis (AD) on CT images and analyze the impact of the AI-support system in a reader study. Design, Setting, and Participants: In this diagnostic study, patients who underwent surgery between July 1, 2005, and October 1, 2020, for CC or AD were included. Three-dimensional (3-D) bounding boxes including the diseased bowel segment and surrounding mesentery were manually delineated and used to develop a 3-D convolutional neural network (CNN). A reader study with 10 observers of different experience levels was conducted. Readers were asked to classify the testing cohort under reading room conditions, first without and then with algorithmic support. Main Outcomes and Measures: To evaluate the diagnostic performance, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated for all readers and reader groups with and without AI support. Metrics were compared using the McNemar test and relative and absolute predictive value comparisons. Results: A total of 585 patients (AD: n = 267, CC: n = 318; mean [SD] age, 63.2 [13.4] years; 341 men [58.3%]) were included. The 3-D CNN reached a sensitivity of 83.3% (95% CI, 70.0%-96.6%) and specificity of 86.6% (95% CI, 74.5%-98.8%) for the test set, compared with the mean reader sensitivity of 77.6% (95% CI, 72.9%-82.3%) and specificity of 81.6% (95% CI, 77.2%-86.1%). The combined group of readers improved significantly with AI support from a sensitivity of 77.6% to 85.6% (95% CI, 81.3%-89.3%; P < .001) and a specificity of 81.6% to 91.3% (95% CI, 88.1%-94.5%; P < .001). Artificial intelligence support significantly reduced the number of false-negative and false-positive findings (NPV from 78.5% to 86.4% and PPV from 80.9% to 90.8%; P < .001). Conclusions and Relevance: The findings of this study suggest that a deep learning model able to distinguish CC and AD in CT images as a support system may significantly improve the diagnostic performance of radiologists, which may improve patient care.


Assuntos
Carcinoma , Aprendizado Profundo , Diverticulite , Masculino , Humanos , Pessoa de Meia-Idade , Inteligência Artificial , Estudos Retrospectivos , Algoritmos , Tomografia Computadorizada por Raios X , Colo
6.
J Hepatol ; 78(4): 820-835, 2023 04.
Artigo em Inglês | MEDLINE | ID: mdl-36681162

RESUMO

BACKGROUND & AIMS: Hepatocyte growth and proliferation depends on membrane phospholipid biosynthesis. Short-chain fatty acids (SCFAs) generated by bacterial fermentation, delivered through the gut-liver axis, significantly contribute to lipid biosynthesis. We therefore hypothesized that dysbiotic insults like antibiotic treatment not only affect gut microbiota, but also impair hepatic lipid synthesis and liver regeneration. METHODS: Stable isotope labeling and 70% partial hepatectomy (PHx) was carried out in C57Bl/6J wild-type mice, in mice treated with broad-spectrum antibiotics, in germ-free mice and mice colonized with minimal microbiota. The microbiome was analyzed by 16S rRNA gene sequencing and microbial culture. Gut content, liver, blood and primary hepatocyte organoids were tested by mass spectrometry-based lipidomics, quantitative reverse-transcription PCR (qRT-PCR), immunoblot and immunohistochemistry for expression of proliferative and lipogenic markers. Matched biopsies from hyperplastic and hypoplastic liver tissue of patients subjected to surgical intervention to induce hyperplasia were analyzed by qRT-PCR for lipogenic enzymes. RESULTS: Three days of antibiotic treatment induced persistent dysbiosis with significantly decreased beta-diversity and richness, but a massive increase of Proteobacteria, accompanied by decreased colonic SCFAs. After PHx, antibiotic-treated mice showed delayed liver regeneration, increased mortality, impaired hepatocyte proliferation and decreased hepatic phospholipid synthesis. Expression of the lipogenic enzyme SCD1 was upregulated after PHx but delayed by antibiotic treatment. Germ-free mice essentially recapitulated the phenotype of antibiotic treatment. Phospholipid biosynthesis, hepatocyte proliferation, liver regeneration and survival were rescued in gnotobiotic mice colonized with a minimal SCFA-producing microbial community. SCFAs induced the growth of murine hepatocyte organoids and hepatic SCD1 expression in mice. Further, SCD1 was required for proliferation of human hepatoma cells and was associated with liver regeneration in human patients. CONCLUSION: Gut microbiota are pivotal for hepatic membrane phospholipid biosynthesis and liver regeneration. IMPACT AND IMPLICATIONS: Gut microbiota affect hepatic lipid metabolism through the gut-liver axis, but the underlying mechanisms are poorly understood. Perturbations of the gut microbiome, e.g. by antibiotics, impair the production of bacterial metabolites, which normally serve as building blocks for membrane lipids in liver cells. As a consequence, liver regeneration and survival after liver surgery is severely impaired. Even though this study is preclinical, its results might allow physicians in the future to improve patient outcomes after liver surgery, by modulation of gut microbiota or their metabolites.


Assuntos
Membrana Celular , Microbioma Gastrointestinal , Hepatócitos , Regeneração Hepática , Fosfolipídeos , Animais , Humanos , Camundongos , Antibacterianos/farmacologia , Microbioma Gastrointestinal/genética , Microbioma Gastrointestinal/fisiologia , Hiperplasia/metabolismo , Hiperplasia/patologia , Fígado/patologia , Regeneração Hepática/fisiologia , Camundongos Endogâmicos C57BL , Fosfolipídeos/biossíntese , Fosfolipídeos/metabolismo , RNA Ribossômico 16S , Hepatócitos/metabolismo , Membrana Celular/metabolismo
7.
Med Image Anal ; 84: 102680, 2023 02.
Artigo em Inglês | MEDLINE | ID: mdl-36481607

RESUMO

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://medicaldecathlon.com/. In addition, both data and online evaluation are accessible via https://competitions.codalab.org/competitions/17094.


Assuntos
Benchmarking , Neoplasias Hepáticas , Humanos , Estudos Retrospectivos , Neoplasias Hepáticas/diagnóstico por imagem , Neoplasias Hepáticas/patologia , Fígado/diagnóstico por imagem , Fígado/patologia , Algoritmos , Processamento de Imagem Assistida por Computador/métodos
8.
Eur J Nucl Med Mol Imaging ; 50(1): 115-129, 2022 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-36074156

RESUMO

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is a molecularly heterogeneous tumor entity with no clinically established imaging biomarkers. We hypothesize that tumor morphology and physiology, including vascularity and perfusion, show variations that can be detected by differences in contrast agent (CA) accumulation measured non-invasively. This work seeks to establish imaging biomarkers for tumor stratification and therapy response monitoring in PDAC, based on this hypothesis. METHODS AND MATERIALS: Regional CA accumulation in PDAC was correlated with tumor vascularization, stroma content, and tumor cellularity in murine and human subjects. Changes in CA distribution in response to gemcitabine (GEM) were monitored longitudinally with computed tomography (CT) Hounsfield Units ratio (HUr) of tumor to the aorta or with magnetic resonance imaging (MRI) ΔR1 area under the curve at 60 s tumor-to-muscle ratio (AUC60r). Tissue analyses were performed on co-registered samples, including endothelial cell proliferation and cisplatin tissue deposition as a surrogate of chemotherapy delivery. RESULTS: Tumor cell poor, stroma-rich regions exhibited high CA accumulation both in human (meanHUr 0.64 vs. 0.34, p < 0.001) and mouse PDAC (meanAUC60r 2.0 vs. 1.1, p < 0.001). Compared to the baseline, in vivo CA accumulation decreased specifically in response to GEM treatment in a subset of human (HUr -18%) and mouse (AUC60r -36%) tumors. Ex vivo analyses of mPDAC showed reduced cisplatin delivery (GEM: 0.92 ± 0.5 mg/g, vs. vehicle: 3.1 ± 1.5 mg/g, p = 0.004) and diminished endothelial cell proliferation (GEM: 22.3% vs. vehicle: 30.9%, p = 0.002) upon GEM administration. CONCLUSION: In PDAC, CA accumulation, which is related to tumor vascularization and perfusion, inversely correlates with tumor cellularity. The standard of care GEM treatment results in decreased CA accumulation, which impedes drug delivery. Further investigation is warranted into potentially detrimental effects of GEM in combinatorial therapy regimens.


Assuntos
Carcinoma Ductal Pancreático , Neoplasias Pancreáticas , Humanos , Camundongos , Animais , Cisplatino/uso terapêutico , Ensaios Antitumorais Modelo de Xenoenxerto , Carcinoma Ductal Pancreático/diagnóstico por imagem , Carcinoma Ductal Pancreático/tratamento farmacológico , Carcinoma Ductal Pancreático/patologia , Neoplasias Pancreáticas/diagnóstico por imagem , Neoplasias Pancreáticas/tratamento farmacológico , Neoplasias Pancreáticas/patologia , Neovascularização Patológica/diagnóstico por imagem , Neovascularização Patológica/tratamento farmacológico , Biomarcadores , Tomografia Computadorizada por Raios X , Imageamento por Ressonância Magnética , Tomografia , Linhagem Celular Tumoral , Gencitabina , Neoplasias Pancreáticas
9.
HPB (Oxford) ; 24(8): 1362-1364, 2022 08.
Artigo em Inglês | MEDLINE | ID: mdl-35289281

RESUMO

BACKGROUND: The first-line therapy for liver malignancies is a radical extended liver resection. This high-risk operation has a high incidence of post-hepatectomy liver failure (PHLF) due to a small future liver remnant (FLR). One of the procedures to increase the FLR is the associating liver partition and portal vein ligation for staged hepatectomy (ALPPS) which is still associated with high morbidity and mortality. Here, we present a new, less invasive ALPPS variant that may be associated with lower morbidity. METHODS: SoftALPPS is characterized by reduced trauma to the liver tissue and individual adaptation to the patient's health constitution. In softALPPS, portal vein embolization (PVE) is performed instead of portal vein ligation (PVL) after complete recovery of liver function. In addition, a non-absorbable foil was avoided in order to be able to extend the interval to step two or skip step two when required. RESULTS: Four patients successfully underwent softALPPS. Two of these patients have been followed-up for over a year (one patient with Klatskin tumor, one patient with extensive HCC). Both patients show no evidence of recurrence after 12 months and are in good medical condition. The other two patients who recently had surgery are also doing well. CONCLUSION: SoftALPPS offers the chance to curatively resect patients with high tumor burden of the liver even when the FLR is inadequate. This individual therapy method can give patients the possibility of complete tumor resection and can help to reduce perioperative morbidity.


Assuntos
Carcinoma Hepatocelular , Neoplasias Hepáticas , Carcinoma Hepatocelular/cirurgia , Hepatectomia , Humanos , Ligadura/métodos , Fígado/patologia , Veia Porta/patologia , Veia Porta/cirurgia , Resultado do Tratamento
10.
Cancers (Basel) ; 14(3)2022 Jan 18.
Artigo em Inglês | MEDLINE | ID: mdl-35158737

RESUMO

BACKGROUND: Our purpose was to investigate the potential of high-resolution, high b-value computed DWI (cDWI) in pancreatic ductal adenocarcinoma (PDAC) detection. MATERIALS AND METHODS: We retrospectively enrolled 44 patients with confirmed PDAC. Respiratory-triggered, diffusion-weighted, single-shot echo-planar imaging (ss-EPI) with both conventional (i.e., full field-of-view, 3 × 3 × 4 mm voxel size, b = 0, 50, 300, 600 s/mm2) and high-resolution (i.e., reduced field-of-view, 2.5 × 2.5 × 3 mm voxel size, b = 0, 50, 300, 600, 1000 s/mm2) imaging was performed for suspected PDAC. cDWI datasets at b = 1000 s/mm2 were generated for the conventional and high-resolution datasets. Three radiologists were asked to subjectively rate (on a Likert scale of 1-4) the following metrics: image quality, lesion detection and delineation, and lesion-to-pancreas intensity relation. Furthermore, the following quantitative image parameters were assessed: apparent signal-to-noise ratio (aSNR), contrast-to-noise ratio (aCNR), and lesion-to-pancreas contrast ratio (CR). RESULTS: High-resolution, high b-value computed DWI (r-cDWI1000) enabled significant improvement in lesion detection and a higher incidence of a high lesion-to-pancreas intensity relation (type 1, clear hyperintense) compared to conventional high b-value computed and high-resolution high b-value acquired DWI (f-cDWI1000 and r-aDWI1000, respectively). Image quality was rated inferior in the r-cDWI1000 datasets compared to r-aDWI1000. Furthermore, the aCNR and CR were higher in the r-cDWI1000 datasets than in f-cDWI1000 and r-aDWI1000. CONCLUSION: High-resolution, high b-value computed DWI provides significantly better visualization of PDAC compared to the conventional high b-value computed and high-resolution high b-value images acquired by DWI.

11.
EJNMMI Res ; 11(1): 70, 2021 Jul 28.
Artigo em Inglês | MEDLINE | ID: mdl-34322781

RESUMO

PURPOSE: In this prospective exploratory study, we evaluated the feasibility of [18F]fluorodeoxyglucose ([18F]FDG) PET/MRI-based chemotherapy response prediction in pancreatic ductal adenocarcinoma at two weeks upon therapy onset. MATERIAL AND METHODS: In a mixed cohort, seventeen patients treated with chemotherapy in neoadjuvant or palliative intent were enrolled. All patients were imaged by [18F]FDG PET/MRI before and two weeks after onset of chemotherapy. Response per RECIST1.1 was then assessed at 3 months [18F]FDG PET/MRI-derived parameters (MTV50%, TLG50%, MTV2.5, TLG2.5, SUVmax, SUVpeak, ADCmax, ADCmean and ADCmin) were assessed, using multiple t-test, Man-Whitney-U test and Fisher's exact test for binary features. RESULTS: At 72 ± 43 days, twelve patients were classified as responders and five patients as non-responders. An increase in ∆MTV50% and ∆ADC (≥ 20% and 15%, respectively) and a decrease in ∆TLG50% (≤ 20%) at 2 weeks after chemotherapy onset enabled prediction of responders and non-responders, respectively. Parameter combinations (∆TLG50% and ∆ADCmax or ∆MTV50% and ∆ADCmax) further improved discrimination. CONCLUSION: Multiparametric [18F]FDG PET/MRI-derived parameters, in particular indicators of a change in tumor glycolysis and cellularity, may enable very early chemotherapy response prediction. Further prospective studies in larger patient cohorts are recommended to their clinical impact.

12.
Cancers (Basel) ; 13(9)2021 Apr 25.
Artigo em Inglês | MEDLINE | ID: mdl-33922981

RESUMO

BACKGROUND: PDAC remains a tumor entity with poor prognosis and a 5-year survival rate below 10%. Recent research has revealed invasive biomarkers, such as distinct molecular subtypes, predictive for therapy response and patient survival. Non-invasive prediction of individual patient outcome however remains an unresolved task. METHODS: Discrete cellularity regions of PDAC resection specimen (n = 43) were analyzed by routine histopathological work up. Regional tumor cellularity and CT-derived Hounsfield Units (HU, n = 66) as well as iodine concentrations were regionally matched. One-way ANOVA and pairwise t-tests were performed to assess the relationship between different cellularity level in conventional, virtual monoenergetic 40 keV (monoE 40 keV) and iodine map reconstructions. RESULTS: A statistically significant negative correlation between regional tumor cellularity in histopathology and CT-derived HU from corresponding image regions was identified. Radiological differentiation was best possible in monoE 40 keV CT images. However, HU values differed significantly in conventional reconstructions as well, indicating the possibility of a broad clinical application of this finding. CONCLUSION: In this study we establish a novel method for CT-based prediction of tumor cellularity for in-vivo tumor characterization in PDAC patients.

13.
Sci Rep ; 11(1): 1191, 2021 01 13.
Artigo em Inglês | MEDLINE | ID: mdl-33441943

RESUMO

The in vivo assessment of tissue metabolism represents a novel strategy for the evaluation of oncologic disease. Hepatocellular carcinoma (HCC) is a high-prevalence, high-mortality tumor entity often discovered at a late stage. Recent evidence indicates that survival differences depend on metabolic alterations in tumor tissue, with particular focus on glucose metabolism and lactate production. Here, we present an in vivo imaging technique for metabolic tumor phenotyping in rat models of HCC. Endogenous HCC was induced in Wistar rats by oral diethyl-nitrosamine administration. Peak lactate-to-alanine signal ratios (L/A) were assessed with hyperpolarized magnetic resonance spectroscopic imaging (HPMRSI) after [1-13C]pyruvate injection. Cell lines were derived from a subset of primary tumors, re-implanted in nude rats, and assessed in vivo with dynamic hyperpolarized magnetic resonance spectroscopy (HPMRS) after [1-13C]pyruvate injection and kinetic modelling of pyruvate metabolism, taking into account systemic lactate production and recirculation. For ex vivo validation, enzyme activity and metabolite concentrations were spectroscopically quantified in cell and tumor tissue extracts. Mean peak L/A was higher in endogenous HCC compared to non-tumorous tissue. Dynamic HPMRS revealed higher pyruvate-to-lactate conversion rates (kpl) and lactate signal in subcutaneous tumors derived from high L/A tumor cells, consistent with ex vivo measurements of higher lactate dehydrogenase (LDH) levels in these cells. In conclusion, HPMRS and HPMRSI reveal distinct tumor phenotypes corresponding to differences in glycolytic metabolism in HCC tumor tissue.


Assuntos
Isótopos de Carbono/administração & dosagem , Carcinoma Hepatocelular/metabolismo , Neoplasias Hepáticas/metabolismo , Imageamento por Ressonância Magnética/métodos , Espectroscopia de Ressonância Magnética/métodos , Ácido Pirúvico/administração & dosagem , Alanina/metabolismo , Animais , Linhagem Celular Tumoral , Glicólise/fisiologia , L-Lactato Desidrogenase/metabolismo , Ácido Láctico/metabolismo , Masculino , Ratos , Ratos Nus , Ratos Wistar
14.
J Clin Med ; 9(5)2020 Apr 25.
Artigo em Inglês | MEDLINE | ID: mdl-32344944

RESUMO

RATIONALE: Pancreatic ductal adenocarcinoma (PDAC) remains a tumor entity of exceptionally poor prognosis, and several biomarkers are under current investigation for the prediction of patient prognosis. Many studies focus on promoting newly developed imaging biomarkers without a rigorous comparison to other established parameters. To assess the true value and leverage the potential of all efforts in this field, a multi-parametric evaluation of the available biomarkers for PDAC survival prediction is warranted. Here we present a multiparametric analysis to assess the predictive value of established parameters and the added contribution of newly developed imaging features such as biomarkers for overall PDAC patient survival. METHODS: 103 patients with resectable PDAC were retrospectively enrolled. Clinical and histopathological data (age, sex, chemotherapy regimens, tumor size, lymph node status, grading and resection status), morpho-molecular and genetic data (tumor morphology, molecular subtype, tp53, kras, smad4 and p16 genetics), image-derived features and the combination of all parameters were tested for their prognostic strength based on the concordance index (CI) of multivariate Cox proportional hazards survival modelling after unsupervised machine learning preprocessing. RESULTS: The average CIs of the out-of-sample data were: 0.63 for the clinical and histopathological features, 0.53 for the morpho-molecular and genetic features, 0.65 for the imaging features and 0.65 for the combined model including all parameters. CONCLUSIONS: Imaging-derived features represent an independent survival predictor in PDAC and enable the multiparametric, machine learning-assisted modelling of postoperative overall survival with a high performance compared to clinical and morpho-molecular/genetic parameters. We propose that future studies systematically include imaging-derived features to benchmark their additive value when evaluating biomarker-based model performance.

15.
J Clin Med ; 9(3)2020 Mar 07.
Artigo em Inglês | MEDLINE | ID: mdl-32155990

RESUMO

To bridge the translational gap between recent discoveries of distinct molecular phenotypes of pancreatic cancer and tangible improvements in patient outcome, there is an urgent need to develop strategies and tools informing and improving the clinical decision process. Radiomics and machine learning approaches can offer non-invasive whole tumor analytics for clinical imaging data-based classification. The retrospective study assessed baseline computed tomography (CT) from 207 patients with proven pancreatic ductal adenocarcinoma (PDAC). Following expert level manual annotation, Pyradiomics was used for the extraction of 1474 radiomic features. The molecular tumor subtype was defined by immunohistochemical staining for KRT81 and HNF1a as quasi-mesenchymal (QM) vs. non-quasi-mesenchymal (non-QM). A Random Forest machine learning algorithm was developed to predict the molecular subtype from the radiomic features. The algorithm was then applied to an independent cohort of histopathologically unclassifiable tumors with distinct clinical outcomes. The classification algorithm achieved a sensitivity, specificity and ROC-AUC (area under the receiver operating characteristic curve) of 0.84 ± 0.05, 0.92 ± 0.01 and 0.93 ± 0.01, respectively. The median overall survival for predicted QM and non-QM tumors was 16.1 and 20.9 months, respectively, log-rank-test p = 0.02, harzard ratio (HR) 1.59. The application of the algorithm to histopathologically unclassifiable tumors revealed two groups with significantly different survival (8.9 and 39.8 months, log-rank-test p < 0.001, HR 4.33). The machine learning-based analysis of preoperative (CT) imaging allows the prediction of molecular PDAC subtypes highly relevant for patient survival, allowing advanced pre-operative patient stratification for precision medicine applications.

16.
Eur J Radiol ; 124: 108848, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-32006931

RESUMO

PURPOSE: To test combined dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and 18F-FDG positron emission tomography (FDG-PET)-derived parameters for prediction of histopathological grading in a rat Diethyl Nitrosamine (DEN)-induced hepatocellular carcinoma (HCC) model. METHODS: 15 male Wistar rats, aged 10 weeks were treated with oral DEN 0.01 % in drinking water and monitored until HCCs were detectable. DCE-MRI and PET were performed consecutively on small animal scanners. 38 tumors were identified and manually segmented based on HCC-specific contrast enhancement patterns. Grading (G2/3: 24 tumors, G1:14 tumors) alongside other histopathological parameters, tumor volume, contrast agent and 18F-FDG uptake metrics were noted. Class imbalance was addressed using SMOTE and collinearity was removed using hierarchical clustering and principal component analysis. A logistic regression model was fit separately to the individual parameter groups (DCE-MRI-derived, PET-derived, tumor volume) and the combined parameters. RESULTS: The combined model using all imaging-derived parameters achieved a mean ± STD sensitivity of 0.88 ± 0.16, specificity of 0.70 ± 0.20 and AUC of 0.90 ± 0.03. No correlation was found between tumor grading and tumor volume, morphology, necrosis, extracellular matrix, immune cell infiltration or underlying liver fibrosis. CONCLUSION: A combination of DCE-MRI- and 18F-FDG-PET-derived parameters provides high accuracy for histopathological grading of hepatocellular carcinoma in a relevant translational model system.


Assuntos
Carcinoma Hepatocelular/diagnóstico por imagem , Meios de Contraste , Fluordesoxiglucose F18 , Aumento da Imagem/métodos , Neoplasias Hepáticas/diagnóstico por imagem , Imageamento por Ressonância Magnética/métodos , Tomografia por Emissão de Pósitrons/métodos , Animais , Carcinoma Hepatocelular/patologia , Modelos Animais de Doenças , Fígado/diagnóstico por imagem , Fígado/patologia , Neoplasias Hepáticas/patologia , Masculino , Gradação de Tumores , Compostos Radiofarmacêuticos , Ratos , Ratos Wistar , Sensibilidade e Especificidade , Carga Tumoral
17.
J Clin Med ; 9(1)2019 Dec 25.
Artigo em Inglês | MEDLINE | ID: mdl-31881761

RESUMO

PURPOSE: To evaluate factors associated with survival following transarterial 90Y (yttrium) radioembolization (TARE) in patients with advanced intrahepatic cholangiocarcinoma (ICC). METHODS: This retrospective multicenter study analyzed the outcome of three tertiary care cancer centers in patients with advanced ICC following resin microsphere TARE. Patients were included either after failed previous anticancer therapy, including relapse after surgical resection, or for having a minimum of 25% of total liver volume affected by ICC. Patients were stratified and response was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST) criteria at 3 months. Kaplan-Meier analysis was performed to analyze survival followed by cox regression to determine independent prognostic factors for survival. RESULTS: 46 patients were included (19 male, 27 female), median age 62.5 years (range 29-88 years). A total of 65% of patients had undergone previous therapy, while 63% had a tumor volume > 25% of the entire liver volume. Median survival was 9.5 months (95% CI: 6.1-12.9 months). Due to loss in follow-up, n = 37 patients were included in the survival analysis. Cox regression revealed the extent of liver disease to one or both liver lobes being associated with survival, irrespective of tumor volume (p = 0.041). Patients with previous surgical resection of ICC had significantly decreased survival (3.9 vs. 12.8 months, p = 0.002). No case of radiation-induced liver disease was observed. DISCUSSION: Survival after 90Y TARE in patients with advanced ICC primarily depends on disease extent. Only limited prognostic factors are associated with a general poor overall survival.

18.
Eur Radiol Exp ; 3(1): 41, 2019 10 17.
Artigo em Inglês | MEDLINE | ID: mdl-31624935

RESUMO

BACKGROUND: To develop a supervised machine learning (ML) algorithm predicting above- versus below-median overall survival (OS) from diffusion-weighted imaging-derived radiomic features in patients with pancreatic ductal adenocarcinoma (PDAC). METHODS: One hundred two patients with histopathologically proven PDAC were retrospectively assessed as training cohort, and 30 prospectively accrued and retrospectively enrolled patients served as independent validation cohort (IVC). Tumors were segmented on preoperative apparent diffusion coefficient (ADC) maps, and radiomic features were extracted. A random forest ML algorithm was fit to the training cohort and tested in the IVC. Histopathological subtype of tumor samples was assessed by immunohistochemistry in 21 IVC patients. Individual radiomic feature importance was evaluated by assessment of tree node Gini impurity decrease and recursive feature elimination. Fisher's exact test, 95% confidence intervals (CI), and receiver operating characteristic area under the curve (ROC-AUC) were used. RESULTS: The ML algorithm achieved 87% sensitivity (95% IC 67.3-92.7), 80% specificity (95% CI 74.0-86.7), and ROC-AUC 90% for the prediction of above- versus below-median OS in the IVC. Heterogeneity-related features were highly ranked by the model. Of the 21 patients with determined histopathological subtype, 8/9 patients predicted to experience below-median OS exhibited the quasi-mesenchymal subtype, whilst 11/12 patients predicted to experience above-median OS exhibited a non-quasi-mesenchymal subtype (p < 0.001). CONCLUSION: ML application to ADC radiomics allowed OS prediction with a high diagnostic accuracy in an IVC. The high overlap of clinically relevant histopathological subtypes with model predictions underlines the potential of quantitative imaging in PDAC pre-operative subtyping and prognosis.


Assuntos
Carcinoma Ductal Pancreático/diagnóstico por imagem , Carcinoma Ductal Pancreático/mortalidade , Imagem de Difusão por Ressonância Magnética , Aprendizado de Máquina , Neoplasias Pancreáticas/diagnóstico por imagem , Neoplasias Pancreáticas/mortalidade , Carcinoma Ductal Pancreático/classificação , Carcinoma Ductal Pancreático/cirurgia , Humanos , Modelos Teóricos , Neoplasias Pancreáticas/classificação , Neoplasias Pancreáticas/cirurgia , Valor Preditivo dos Testes , Período Pré-Operatório , Estudos Retrospectivos , Taxa de Sobrevida
19.
PLoS One ; 14(10): e0218642, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31577805

RESUMO

PURPOSE: Development 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. METHODS: The 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 with PyRadiomics. A gradient-boosted-tree algorithm was trained on 70% of the patients (N = 28) and tested on 30% (N = 17) to predict KRT81+ vs. KRT81- tumor subtypes. A gradient-boosted survival regression model was fit to the disease-free and overall survival data. Chemotherapy response and survival were assessed stratified by subtype and radiomic signature. Radiomic feature importance was ranked. RESULTS: The mean±STDEV sensitivity, specificity and ROC-AUC were 0.90±0.07, 0.92±0.11, and 0.93±0.07, respectively. The mean±STDEV concordance indices between the disease-free and overall survival predicted by the model based on the radiomic parameters and actual patient survival were 0.76±0.05 and 0.71±0.06, respectively. Patients with a KRT81+ subtype experienced significantly diminished median overall survival compared to KRT81- patients (7.0 vs. 22.6 months, HR 4.03, 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 2.33, 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 2.41, P = 0.027). Entropy was ranked as the most important radiomic feature. CONCLUSIONS: The machine-learning based analysis of radiomic features enables the prediction of subtypes of PDAC, which are highly relevant for disease-free and overall patient survival and response to chemotherapy.


Assuntos
Protocolos de Quimioterapia Combinada Antineoplásica/administração & dosagem , Carcinoma Ductal Pancreático , Desoxicitidina/análogos & derivados , Queratinas Específicas do Cabelo/metabolismo , Queratinas Tipo II/metabolismo , Aprendizado de Máquina , Proteínas de Neoplasias/metabolismo , Neoplasias Pancreáticas , Adulto , Carcinoma Ductal Pancreático/metabolismo , Carcinoma Ductal Pancreático/mortalidade , Carcinoma Ductal Pancreático/patologia , Carcinoma Ductal Pancreático/terapia , Desoxicitidina/administração & dosagem , Intervalo Livre de Doença , Feminino , Fluoruracila/administração & dosagem , Humanos , Irinotecano/administração & dosagem , Leucovorina/administração & dosagem , Masculino , Pessoa de Meia-Idade , Oxaliplatina/administração & dosagem , Neoplasias Pancreáticas/metabolismo , Neoplasias Pancreáticas/mortalidade , Neoplasias Pancreáticas/patologia , Neoplasias Pancreáticas/terapia , Estudos Retrospectivos , Sensibilidade e Especificidade , Taxa de Sobrevida , Gencitabina
20.
PLoS One ; 14(1): e0208717, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30601813

RESUMO

PURPOSE: The purpose of the current study was to compare CT-signs of portal venous confluence infiltration for actual histopathological infiltration of the vein or the tumor/vein interface (TVI) in borderline resectable pancreatic ductal adenocarcinoma (PDAC). METHODS AND MATERIALS: 101 patients with therapy-naïve, primarily resected PDAC of the pancreatic head without arterial involvement were evaluated. The portal venous confluence was assessed for contour irregularity (defined as infiltration) and degree of contact. The sensitivity and specificity of contour irregularity versus tumor to vein contact >180° as well as the combination of the signs for tumor cell infiltration of the vessel wall or TVI was calculated. Overall survival (OS) was compared between groups. RESULTS: Sensitivity and specificity of contour irregularity for identification of tumor infiltration of the portal venous confluence or the TVI was higher compared to tumor to vessel contact >180° for tumor cell infiltration (96%/79% vs. 91%/38% respectively, p<0.001). The combination of the signs increased specificity to 92% (sensitivity 88%). Patients with contour irregularity/ tumor to vein contact >180°/ both signs had significantly worse overall survival (16.2 vs. 26.5 months/ 17.9 vs. 37.4 months/ 18.5 vs. 26.5 months respectively, all p<0.05). CONCLUSION: Portal venous confluence contour irregularity is a strong predictor of actual tumor cell infiltration of the vessel wall or the TVI and should be noted as such in radiological reports.


Assuntos
Neoplasias Pancreáticas/diagnóstico por imagem , Neoplasias Pancreáticas/patologia , Tomografia Computadorizada por Raios X/métodos , Carcinoma Ductal Pancreático/diagnóstico por imagem , Carcinoma Ductal Pancreático/patologia , Feminino , Humanos , Masculino , Neoplasias Pancreáticas/cirurgia , Pancreaticoduodenectomia , Estudos Retrospectivos , Sensibilidade e Especificidade , Neoplasias Pancreáticas
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA