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2.
Cancer Imaging ; 20(1): 30, 2020 Apr 25.
Artigo em Inglês | MEDLINE | ID: mdl-32334635

RESUMO

BACKGROUND: Preoperative detection of lymph node (LN) metastasis is critical for planning treatments in colon cancer (CC). The clinical diagnostic criteria based on the size of the LNs are not sensitive to determine metastasis using CT images. In this retrospective study, we investigated the potential value of CT texture features to diagnose LN metastasis using preoperative CT data and patient characteristics by developing quantitative prediction models. METHODS: A total of 390 CC patients, undergone surgical resection, were enrolled in this monocentric study. 390 histologically validated LNs were collected from patients and randomly separated into training (312 patients, 155 metastatic and 157 normal LNs) and test cohorts (78 patients, 39 metastatic and 39 normal LNs). Six patient characteristics and 146 quantitative CT imaging features were analyzed and key variables were determined using either exhaustive search or least absolute shrinkage algorithm. Two kernel-based support vector machine classifiers (patient-characteristic model and radiomic-derived model), generated with 10-fold cross-validation, were compared with the clinical model that utilizes long-axis diameter for diagnosis of metastatic LN. The performance of the models was evaluated on the test cohort by computing accuracy, sensitivity, specificity, and area under the receiver operating curve (AUC). RESULTS: The clinical model had an overall diagnostic accuracy of 64.87%; specifically, accuracy of 65.38% and 62.82%, sensitivity of 83.87% and 84.62%, and specificity of 47.13% and 41.03% for training and test cohorts, respectively. The patient-demographic model obtained accuracy of 67.31% and 73.08%, the sensitivity of 62.58% and 69.23%, and specificity of 71.97% and 76.23% for training and test cohorts, respectively. Besides, the radiomic-derived model resulted in an accuracy of 81.09% and 79.49%, sensitivity of 83.87% and 74.36%, and specificity of 78.34% and 84.62% for training and test cohorts, respectively. Furthermore, the diagnostic performance of the radiomic-derived model was significantly higher than clinical and patient-demographic models (p < 0.02) according to the DeLong method. CONCLUSIONS: The texture of the LNs provided characteristic information about the histological status of the LNs. The radiomic-derived model leveraging LN texture provides better preoperative diagnostic accuracy for the detection of metastatic LNs compared to the clinically accepted diagnostic criteria and patient-demographic model.


Assuntos
Neoplasias do Colo/patologia , Aprendizado de Máquina , Adulto , Idoso , Neoplasias do Colo/diagnóstico por imagem , Neoplasias do Colo/cirurgia , Feminino , Humanos , Metástase Linfática/diagnóstico por imagem , Masculino , Pessoa de Meia-Idade , Projetos Piloto , Período Pré-Operatório , Estudos Retrospectivos , Sensibilidade e Especificidade , Tomografia Computadorizada por Raios X
3.
Acad Radiol ; 27(12): 1727-1733, 2020 12.
Artigo em Inglês | MEDLINE | ID: mdl-32033861

RESUMO

RATIONALE AND OBJECTIVES: To investigate whether transcatheter intraarterial perfusion (TRIP) magnetic resonance imaging (MRI) can differentiate reversible electroporation (RE) zones from irreversible electroporation (IRE) zones immediately after IRE procedure in the rabbit liver. MATERIALS AND METHODS: All studies were approved by the institutional animal care and use committee and performed in accordance with institutional guidelines. A total of 13 healthy New Zealand White rabbits were used. After selective catheterization of the hepatic artery under X-ray fluoroscopy, we acquired TRIP-MRI at 20 minutes post-IRE using 3 mL of 5% intraarterial gadopentetate dimeglumine. Semi-quantitative (peak enhancement, PE; time to peak, TTP; wash-in slope, WIS; areas under the time-intensity curve, AUT, over 30, 60, 90, 120, 150, and 180 seconds after the initiation of enhancement) and quantitative (Ktrans, ve, and vp) TRIP-MRI parameters were calculated. The relationships between TRIP-MRI parameters and histological measurements and the differential ability of TRIP-MRI parameters was assessed. RESULTS: PE, AUT60, AUT90, AUT120, AUT150, AUT180, Ktrans, and ve were significantly higher in RE zones than in IRE zones (all P < 0.05), and AUC for these parameters ranged from 0.91(95% CI, 0.80, 1.00) to 0.99 (95% CI, 0.98, 1.00). There was no significant difference in AUC between any two parameters (Z, 0-1.47; P, 0.14-1.00). Hepatocyte apoptosis strongly correlated with PE, AUT60, AUT90, AUT120, AUT150, AUT180, Ktrans, and vp (the absolute value r, 0.6-0.7, all P < 0.0001). CONCLUSION: AUT150 or AUT180 could be a potential imaging biomarker to differentiate RE from IRE zones, and TRIP-MRI permits to differentiate RE from IRE zones immediately after IRE procedure in the rabbit liver.


Assuntos
Neoplasias Hepáticas , Angiografia por Ressonância Magnética , Animais , Gadolínio DTPA , Artéria Hepática/diagnóstico por imagem , Coelhos
4.
J Transl Med ; 18(1): 61, 2020 02 10.
Artigo em Inglês | MEDLINE | ID: mdl-32039734

RESUMO

BACKGROUND: There is a lack of well-established clinical tools for predicting dendritic cell (DC) vaccination response of pancreatic ductal adenocarcinoma (PDAC). DC vaccine treatment efficiency was demonstrated using histological analysis in pre-clinical studies; however, its usage was limited due to invasiveness. In this study, we aimed to investigate the potential of MRI texture features for detection of early immunotherapeutic response as well as overall survival (OS) of PDAC subjects following dendritic cell (DC) vaccine treatment in LSL-KrasG12D;LSL-Trp53R172H;Pdx-1-Cre (KPC) transgenic mouse model of pancreatic ductal adenocarcinoma (PDAC). MATERIALS AND METHODS: KPC mice were treated with DC vaccines, and tumor growth was dynamically monitored. A total of a hundred and fifty-two image features of T2-weighted MRI images were analyzed using a kernel-based support vector machine model to detect treatment effects following the first and third weeks of the treatment. Moreover, univariate analysis was performed to describe the association between MRI texture and survival of KPC mice as well as histological tumor biomarkers. RESULTS: OS for mice in the treatment group was 54.8 ± 22.54 days while the control group had 35.39 ± 17.17 days. A subset of three MRI features distinguished treatment effects starting from the first week with increasing accuracy throughout the treatment (75% to 94%). Besides, we observed that short-run emphasis of approximate wavelet coefficients had a positive correlation with the survival of the KPC mice (r = 0.78, p < 0.001). Additionally, tissue-specific MRI texture features showed positive association with fibrosis percentage (r = 0.84, p < 0.002), CK19 positive percentage (r = - 0.97, p < 0.001), and Ki67 positive cells (r = 0.81, p < 0.02) as histological disease biomarkers. CONCLUSION: Our results demonstrate that MRI texture features can be used as imaging biomarkers for early detection of therapeutic response following DC vaccination in the KPC mouse model of PDAC. Besides, MRI texture can be utilized to characterize tumor microenvironment reflected with histology analysis.

5.
Magn Reson Med ; 84(1): 365-374, 2020 07.
Artigo em Inglês | MEDLINE | ID: mdl-31850550

RESUMO

PURPOSE: Irreversible electroporation (IRE) is a nonthermal tissue ablation technique that represents a promising treatment option for unresectable liver tumors, but the effectively treated zone cannot be reliably predicted. We investigate the potential benefit of transcatheter intra-arterial perfusion (TRIP) -MRI for the early noninvasive differentiation of IRE zone from surrounding reversibly electroporated (RE) zone. METHODS: Seventeen rabbits with VX2 liver tumors were scanned with morphological and contrast-enhanced MRI sequences approximately 30 min after IRE tumor ablation. Quantitative TRIP-MRI perfusion parameters were evaluated in IRE zone and RE zone, defined according to histology. MRI and histology results were compared among zones using Wilcoxon rank-sum tests and correlations were evaluated by Pearson's correlation coefficient. RESULTS: There were significant differences in area under the curve, time to peak, maximum and late enhancement, wash-in and wash-out rates in the tumor IRE zones compared with the boundary tumor RE zones and untreated tumors. Histology showed significantly fewer tumor cells, microvessels and significantly more apoptosis in tumor IRE zones compared with tumor RE zones (-51%, -66% and +185%, respectively) and untreated tumors (-60%, -67%, and +228%, respectively). A strong correlation was observed between MRI and histology measurements of IRE zones (r = 0.948) and RE zones (r = 0.951). CONCLUSION: TRIP-MRI demonstrated the potential to detect immediate perfusion changes following IRE liver tumor ablation and effectively differentiate the IRE zone from the surrounding tumor RE zone.

6.
Am J Cancer Res ; 9(11): 2482-2492, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31815048

RESUMO

The aim of this study was to develop and validate a new non-invasive artificial intelligence (AI) model based on preoperative computed tomography (CT) data to predict the presence of liver metastasis (LM) in colon cancer (CC). A total of forty-eight eligible CC patients were enrolled, including twenty-four patients with LM and twenty-four patients without LM. Six clinical factors and one hundred and fifty-two tumor image features extracted from CT data were utilized to develop three models: clinical, radiomics, and hybrid (a combination of clinical and radiomics features) using support vector machines with 5-fold cross-validation. The performance of each model was evaluated in terms of accuracy, specificity, sensitivity, and area under the curve (AUC). For the radiomics model, a total of four image features utilized to construct the model resulting in an accuracy of 83.87% for training and 79.50% for validation. The clinical model that employed two selected clinical variables had an accuracy of 69.82% and 69.50% for training and validation, respectively. The hybrid model that combined relevant image features and clinical variables improved accuracy of both training (90.63%) and validation (85.50%) sets. In terms of AUC, hybrid (0.96; 0.87) and radiomics models (0.91; 0.85) demonstrated a significant improvement compared with the clinical model (0.71; 0.69), and the hybrid model had the best prediction performance. In conclusion, the AI model developed using preoperative conventional CT data can accurately predict LM in CC patients without additional procedures. Furthermore, combining image features with clinical characteristics greatly improved the model's prediction performance. We have thus generated a promising tool that allows guidance and individualized surveillance of CC patients with high risks of LM.

7.
Am J Cancer Res ; 9(7): 1429-1438, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-31392079

RESUMO

The purpose of this study was to develop a radiomics signature for distinguishing stage in advanced colon cancer (CC). 195 colon cancer patients were enrolled in this study (stage III, n = 146 vs. stage IV, n = 49) and divided into training cohort (n = 136) and validation cohort (n = 59). A total of 286 radiomic features were extracted from tumor and LN images. A radiomics signature was generated using the least absolute shrinkage and selection operator (LASSO) technique. The relationship between radiomics signature and CC staging was explored using a kernel-based support vector machine (SVM) classifier model. The classification performance was assessed by accuracy and the receiver operating characteristics (ROC) curve. A total of 5 features (2 for tumor and 3 for LN) were selected among 286 features. Radiomics signature built from extracted features successfully differentiated stage III from stage IV CC with no known distant metastases on imaging preoperatively. Furthermore, the SVM classifier model generated using tumor and LN images together achieved better performance than the tumor alone, with accuracies of 86.03% vs. 78.68% and 83.05% vs. 76.27% in training and validation cohorts, respectively. In ROC analysis, the model showed a significant improvement for training (AUC 89.16% vs. 69.5%) and validation cohorts (AUC 75.15% vs. 55%) in comparison with the combined analysis and the tumor alone. In conclusion, the radiomics signature based on preoperative CT may distinguish stage III from stage IV CC with no known distant metastases. In addition, the radiomic features from combined images achieved better classification performance than tumor alone.

8.
Am J Cancer Res ; 9(3): 562-573, 2019.
Artigo em Inglês | MEDLINE | ID: mdl-30949410

RESUMO

Pancreatic ductal adenocarcinoma (PDAC) carries the worst prognosis and caused one of the highest cancer-related mortalities. Dendritic cell (DC) vaccination is a promising cancer immunotherapy; however, the clinical outcomes are often poor. The administration route of DC vaccine can significantly alter the anti-tumor immune response. Here we report on the cytotoxic T lymphocyte (CTL) responses induced by DC vaccination administered via intraperitoneal (IP) for murine PDAC, and the longitudinal assessment of tumor growth and therapeutic responses using magnetic resonance imaging (MRI). In this study, we established murine orthotopic Panc02 models of PDAC and delivered apoptotic Panc02 cell-pulsed DCs via IP injection. The migration of Panc02-pulsed DCs into spleens significantly increased from 6 h to 12 h after initiation of treatment (P = 0.002), and Panc02-pulsed DCs injected via IP induced a significantly higher level of CTL responses against Panc02 cells compared to unpulsed DCs. Tumor size and tumor apparent diffusion coefficient (ADC) were measured on MR images. Tumor sizes were significantly smaller in the treated mice than in the untreated mice (P < 0.05). The reduction of tumor ADC was less in the treated mice than in the untreated mice (P < 0.05), and the changes in tumor ADC showed significant negative correlation with the changes in tumor volume (r = -0.882, 95% confidence interval, -0.967 to -0.701, P < 0.0001). These results demonstrated the efficacy of DC vaccination administered via IP injection in murine PDAC, and the feasibility of ADC measurement as an imaging biomarker for assessment of therapeutic responses in immunotherapy.

9.
Am J Transl Res ; 10(9): 2859-2867, 2018.
Artigo em Inglês | MEDLINE | ID: mdl-30323872

RESUMO

Dendritic cell (DC) based immunotherapy is a promising approach for cancer treatment and has been approved in clinical settings for decades. Clinical trials have demonstrated relatively poor therapeutic efficacy. The efficacy of DC immunotherapy is strongly influenced by their ability to migrate to the draining lymph nodes (LNs). Therefore, it is critical to deliver DCs and monitor the in vivo biodistributions of DCs after administration. The purpose of this study is to determine whether a novel injection route of DCs improves DC migration to LNs, tissues, organs and lymphatics. In the present study, a modified method was investigated to acquire DCs from mouse bone marrow. Cultured antibody labeled DCs were analyzed by flow cytometry. India ink was used to visualize mouse abdominal LNs and PKH26 was utilized to label DCs for intraperitoneal (IP) injection, results were evaluated by histology. Our results showed that large amounts of DCs with a relatively high purity were acquired. IP injection of india ink marked the abdominal LNs and PKH26 labeled DCs showed IP was an effective administration route to increase the absorption of viable DCs, and different time points after IP inject showed no significant difference of the migrated DCs. The findings indicated that large amounts of high purity DCs can be acquired through our method and IP injection accelerates DCs migration to abdominal LNs, which can be directly translated to clinical settings, especially for abdominal cancers. This study makes a foundation for future researches of DC-based immunotherapy as a treatment modality against cancer.

10.
Cancer Med ; 7(5): 1860-1869, 2018 05.
Artigo em Inglês | MEDLINE | ID: mdl-29601672

RESUMO

While natural killer (NK) cell-based adoptive transfer immunotherapy (ATI) provides only modest clinical success in cancer patients. This study was hypothesized that MRI-guided transcatheter intra-hepatic arterial (IHA) infusion permits local delivery to liver tumors to improve outcomes during NK-based ATI in a rat model of hepatocellular carcinoma (HCC). Mouse NK cells were labeled with clinically applicable iron nanocomplexes. Twenty rat HCC models were assigned to three groups: transcatheter IHA saline infusion as the control group, transcatheter IHA NK infusion group, and intravenous (IV) NK infusion group. MRI studies were performed at baseline and at 24 h, 48 h, and 8 days postinfusion. There was a significant difference in tumor R2* values between baseline and 24 h following the selective transcatheter IHA NK delivery to the tumors (P = 0.039) when compared to IV NK infusion (P = 0.803). At 8 days postinfusion, there were significant differences in tumor volumes between the control, IV, and IHA NK infusion groups (control vs. IV, P = 0.196; control vs. IHA, P < 0.001; and IV vs. IHA, P = 0.001). Moreover, there was a strong correlation between tumor R2* value change (∆R2*) at 24 h postinfusion and tumor volume change (∆volume) at 8 days in IHA group (R2  = 0.704, P < 0.001). Clinically applicable labeled NK cells with 12-h labeling time can be tracked by MRI. Transcatheter IHA infusion improves NK cell homing efficacy and immunotherapeutic efficiency. The change in tumor R2* value 24 h postinfusion is an important early biomarker for prediction of longitudinal response.


Assuntos
Carcinoma Hepatocelular/terapia , Células Matadoras Naturais/transplante , Neoplasias Hepáticas/terapia , Imagem por Ressonância Magnética Intervencionista/métodos , Administração Intravenosa , Animais , Linhagem Celular Tumoral , Imunoterapia Adotiva , Infusões Parenterais , Masculino , Camundongos , Ratos , Resultado do Tratamento , Ensaios Antitumorais Modelo de Xenoenxerto
11.
J Chem Phys ; 124(14): 144702, 2006 Apr 14.
Artigo em Inglês | MEDLINE | ID: mdl-16626226

RESUMO

A binary mixture of oppositely charged components confined to a plane such as cationic and anionic lipid bilayers may exhibit local segregation. The relative strengths of the net short range interactions, which favors macroscopic segregation, and the long range electrostatic interactions, which favors mixing, determine the length scale of the finite size or microphase segregation. The free energy of the system can be examined analytically in two separate regimes, when considering small density fluctuations at high temperatures and when considering the periodic ordering of the system at low temperatures [F. J. Solis, S. I. Stupp, and M. Olvera de la Cruz, J. Chem. Phys. 122, 054905 (2005)]. A simple molecular dynamics simulation of oppositely charged monomers, interacting with a short range Lennard-Jones potential and confined to a two dimensional plane, is examined at different strengths of short and long range interactions. The system exhibits well-defined domains that can be characterized by their periodic length scale as well as the orientational ordering of their interfaces. By adding salt, the ordering of the domains disappears and the mixture macroscopically phase segregates in agreement with analytical predictions.

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