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1.
Ann Palliat Med ; 10(8): 9281-9287, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-34488414

RESUMO

Preoperative intra-arterial chemoembolization has been successfully applied in many malignant tumors but is rarely reported in patients with locally advanced rectal cancer (LARC). Herein we report a 69-year-old female diagnosed as rectal adenocarcinoma by endoscopic biopsy and the clinical stage was cT4aN2M0, IIIB. After computed tomography (CT) and magnetic resonance imaging (MRI) examinations, the neoplasm was considered unresectable. Then neoadjuvant chemoradiotherapy was recommended to the patient after multidisciplinary treatment. Due to the financial situation and physical condition, the patient only chose chemotherapy for preoperative treatment. During the first time of the mFOLFOX6 regimen, the patient had severe side effects of vomiting, despite tropisetron being routinely given. Then we recommended regional intra-arterial chemoembolization combined with CAPEOX regimen for conversion treatment. After intra-arterial chemoembolization with oxaliplatin and 3 months of chemotherapy with CAPEOX regimen, CT and MRI were performed again to re-evaluate the local condition. Images showed distinct remission in the tumor area, and its surrounding lymph nodes were reduced in number and volume. Also, the tumor had shrunk distinctly with a negative circumferential resection margin (CRM). We concluded that the tumor was converted into a resectable one, and the patient met the conditions for the operation. The fact indicates that it is effective in creating good operative conditions for LARC by adding intra-arterial chemotherapy to the standard treatment.


Assuntos
Protocolos de Quimioterapia Combinada Antineoplásica , Neoplasias Retais , Idoso , Protocolos de Quimioterapia Combinada Antineoplásica/uso terapêutico , Feminino , Humanos , Terapia Neoadjuvante , Estadiamento de Neoplasias , Neoplasias Retais/diagnóstico por imagem , Neoplasias Retais/terapia , Resultado do Tratamento
2.
J Appl Clin Med Phys ; 22(9): 324-331, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34343402

RESUMO

PURPOSE: Manual delineation of a rectal tumor on a volumetric image is time-consuming and subjective. Deep learning has been used to segment rectal tumors automatically on T2-weighted images, but automatic segmentation on diffusion-weighted imaging is challenged by noise, artifact, and low resolution. In this study, a volumetric U-shaped neural network (U-Net) is proposed to automatically segment rectal tumors on diffusion-weighted images. METHODS: Three hundred patients of locally advanced rectal cancer were enrolled in this study and divided into a training group, a validation group, and a test group. The region of rectal tumor was delineated on the diffusion-weighted images by experienced radiologists as the ground truth. A U-Net was designed with a volumetric input of the diffusion-weighted images and an output of segmentation with the same size. A semi-automatic segmentation method was used for comparison by manually choosing a threshold of gray level and automatically selecting the largest connected region. Dice similarity coefficient (DSC) was calculated to evaluate the methods. RESULTS: On the test group, deep learning method (DSC = 0.675 ± 0.144, median DSC is 0.702, maximum DSC is 0.893, and minimum DSC is 0.297) showed higher segmentation accuracy than the semi-automatic method (DSC = 0.614 ± 0.225, median DSC is 0.685, maximum DSC is 0.869, and minimum DSC is 0.047). Paired t-test shows significant difference (T = 2.160, p = 0.035) in DSC between the deep learning method and the semi-automatic method in the test group. CONCLUSION: Volumetric U-Net can automatically segment rectal tumor region on DWI images of locally advanced rectal cancer.


Assuntos
Aprendizado Profundo , Neoplasias Retais , Imagem de Difusão por Ressonância Magnética , Humanos , Processamento de Imagem Assistida por Computador , Redes Neurais de Computação , Neoplasias Retais/diagnóstico por imagem , Reto/diagnóstico por imagem
3.
Am J Case Rep ; 22: e932153, 2021 Jul 29.
Artigo em Inglês | MEDLINE | ID: mdl-34321452

RESUMO

BACKGROUND Food particles may sometime lodge in the intestinal wall, resulting in a granuloma. Pulse granuloma is associated with the seed of a legume and has a characteristic appearance on histology. This report describes a case of pulse granuloma of the descending colon identified by fluorodeoxyglucose-positron emission tomography (FDG-PET) imaging. Imaging was done 19 months after surgical resection for rectal carcinoma, and the results of imaging alone suggested a tumor metastasis. CASE REPORT A 77-year-old man underwent sigmoid colostomy for sigmoid colon perforation due to obstruction by rectal cancer affecting the upper rectum approximately 2 years ago. Two months later, after his general condition improved, he underwent laparoscopic low anterior resection. On postoperative pathological examination, the lesion was diagnosed as stage II. Nineteen months later, computed tomography showed an irregular nodule on the dorsolateral side of the descending colon. FDG-PET revealed positive results, and peritoneal dissemination was suspected. Because the lesion was localized and there was no other evidence of metastasis, resection was performed. A pathological examination revealed a pulse granuloma with a central legume seed, and no obvious malignant findings were observed. CONCLUSIONS This report has highlighted the importance of imaging and histopathology in cases in which a solitary nodule is present in the bowel in a patient with previous successful treatment for malignancy. Pulse granuloma, or other types of granuloma associated with impacted food material, may be a cause of a solitary nodule, or pseudotumor, in the bowel wall.


Assuntos
Carcinoma , Neoplasias Retais , Idoso , Colo Descendente , Fluordesoxiglucose F18 , Granuloma , Humanos , Masculino , Tomografia por Emissão de Pósitrons , Compostos Radiofarmacêuticos , Neoplasias Retais/diagnóstico por imagem , Neoplasias Retais/cirurgia
4.
Eur J Radiol ; 142: 109869, 2021 Sep.
Artigo em Inglês | MEDLINE | ID: mdl-34303149

RESUMO

PURPOSE: To develop a model based on histogram parameters derived from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for predicting the nodal staging of rectal cancer (RC). MATERIAL AND METHODS: A total of 95 RC patients who underwent direct surgical resection were enrolled in this prospective study. The nodal staging on conventional magnetic resonance imaging (MRI) was evaluated according to the short axis diameter and morphological characteristics. Histogram parameters were extracted from apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudo-diffusion coefficient (D*), and perfusion fraction (f) maps. Multivariate binary logistic regression analysis was conducted to establish models for predicting nodal staging among all patients and those underestimated on conventional MRI. RESULTS: The combined model based on multiple maps demonstrated superior diagnostic performance to single map models, with an area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.959, 94.3%, 88.3%, and 90.5%, respectively. The AUC of the combined model was significantly higher than that of the conventional nodal staging (P < 0.001). Additionally, 85.0% of the underestimated patients had suspicious lymph nodes with 5-8 mm short-axis diameter. The histogram model for these subgroups of patients showed good diagnostic efficacy with an AUC, sensitivity, specificity, and accuracy of 0.890, 100%, 75%, and 80.5%. CONCLUSION: The histogram model based on IVIM-DWI could improve the diagnostic performance of nodal staging of RC. In addition, histogram parameters of IVIM-DWI may help to reduce the uncertainty of nodal staging in underestimated patients on conventional MRI.


Assuntos
Imagem de Difusão por Ressonância Magnética , Neoplasias Retais , Humanos , Imageamento por Ressonância Magnética , Movimento (Física) , Estudos Prospectivos , Neoplasias Retais/diagnóstico por imagem
5.
World J Gastroenterol ; 27(25): 3802-3814, 2021 Jul 07.
Artigo em Inglês | MEDLINE | ID: mdl-34321845

RESUMO

Rectal cancer (RC) is the third most commonly diagnosed cancer and has a high risk of mortality, although overall survival rates have improved. Preoperative assessments and predictions, including risk stratification, responses to therapy, long-term clinical outcomes, and gene mutation status, are crucial to guide the optimization of personalized treatment strategies. Radiomics is a novel approach that enables the evaluation of the heterogeneity and biological behavior of tumors by quantitative extraction of features from medical imaging. As these extracted features cannot be captured by visual inspection, the field holds significant promise. Recent studies have proved the rapid development of radiomics and validated its diagnostic and predictive efficacy. Nonetheless, existing radiomics research on RC is highly heterogeneous due to challenges in workflow standardization and limitations of objective cohort conditions. Here, we present a summary of existing research based on computed tomography and magnetic resonance imaging. We highlight the most salient issues in the field of radiomics and analyze the most urgent problems that require resolution. Our review provides a cutting-edge view of the use of radiomics to detect and evaluate RC, and will benefit researchers dedicated to using this state-of-the-art technology in the era of precision medicine.


Assuntos
Neoplasias Retais , Humanos , Imageamento por Ressonância Magnética , Medicina de Precisão , Neoplasias Retais/diagnóstico por imagem , Neoplasias Retais/terapia , Tomografia Computadorizada por Raios X
6.
Sichuan Da Xue Xue Bao Yi Xue Ban ; 52(4): 698-705, 2021 Jul.
Artigo em Chinês | MEDLINE | ID: mdl-34323052

RESUMO

Objective: To explore the radiomics features of T2 weighted image (T2WI) and readout-segmented echo-planar imaging (RS-EPI) plus difusion-weighted imaging (DWI), to develop an automated mahchine-learning model based on the said radiomics features, and to test the value of this model in predicting preoperative T staging of rectal cancer. Methods: The study retrospectively reviewed 131 patients who were diagnosed with rectal cancer confirmed by the pathology results of their surgical specimens at West China Hospital of Sichuan University between October, 2017 and December, 2018. In addition, these patients had preoperative rectal MRI. Tumor regions from preoperative MRI were manually segmented by radiologists with the ITK-SNAP software from T2WI and RS-EPI DWI images. PyRadiomics was used to extract 200 features-100 from T2WI and 100 from the apparent diffusion coefficient (ADC) calculated from the RS-EPI DWI. MWMOTE and NEATER were used to resample and balance the dataset, and 13 cases of T 1-2 stage simulation cases were added. The overall dataset was divided into a training set (111 cases) and a test set (37 cases) by a ratio of 3∶1. Tree-based Pipeline Optimization Tool (TPOT) was applied on the training set to optimize model parameters and to select the most important radiomics features for modeling. Five independent T stage models were developed accordingly. Accuracy and the area under the curve ( AUC) of receiver operating characteristic (ROC) were used to pick out the optimal model, which was then applied on the training set and the original dataset to predict the T stage of rectal cancer. Results: The performance of the the five T staging models recommended by automated machine learning were as follows: The accuracy for the training set ranged from 0.802 to 0.838, sensitivity, from 0.762 to 0.825, specificity, from 0.833 to 0.896, AUC, from 0.841 to 0.893, and average precision (AP) from 0.870 to 0.901. After comparison, an optimal model was picked out, with sensitivity, specificity and AUC for the training set reaching 0.810, 0.875, and 0.893, respectively. The sensitivity, specificity and AUC for the test set were 0.810, 0.813, and 0.810, respectively. The sensitivity, specificity and AUC for the original dataset were 0.810, 0.830, and 0.860, respectively. Conclusion: Based on the radiomics data of T2WI and RS-EPI DWI, the model established by automated machine learning showed a fairly high accuracy in predicting rectal cancer T stage.


Assuntos
Imagem Ecoplanar , Neoplasias Retais , China , Imagem de Difusão por Ressonância Magnética , Humanos , Aprendizado de Máquina , Neoplasias Retais/diagnóstico por imagem , Neoplasias Retais/cirurgia , Estudos Retrospectivos
7.
Anticancer Res ; 41(8): 3969-3976, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-34281860

RESUMO

BACKGROUND/AIM: We aimed to investigate the role of radiogenomic and deep learning approaches in predicting the KRAS mutation status of a tumor using radiotherapy planning computed tomography (CT) images in patients with locally advanced rectal cancer. PATIENTS AND METHODS: After surgical resection, 30 (27.3%) of 110 patients were found to carry a KRAS mutation. For the radiogenomic model, a total of 378 texture features were extracted from the boost clinical target volume (CTV) in the radiotherapy planning CT images. For the deep learning model, we constructed a simple deep learning network that received a three-dimensional input from the CTV. RESULTS: The predictive ability of the radiogenomic score model revealed an AUC of 0.73 for KRAS mutation, whereas the deep learning model demonstrated worse performance, with an AUC of 0.63. CONCLUSION: The radiogenomic score model was a more feasible approach to predict KRAS status than the deep learning model.


Assuntos
Aprendizado Profundo , Genômica , Proteínas Proto-Oncogênicas p21(ras)/genética , Planejamento da Radioterapia Assistida por Computador/métodos , Neoplasias Retais/genética , Adulto , Idoso , Idoso de 80 Anos ou mais , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Mutação , Neoplasias Retais/diagnóstico por imagem , Neoplasias Retais/radioterapia
8.
Lakartidningen ; 1182021 07 06.
Artigo em Sueco | MEDLINE | ID: mdl-34228808

RESUMO

Besides clinical evaluation, all patients with rectal cancer must be examined with CT of the chest and abdomen to assess the presence of metastases, pelvic MRI to stage the tumour locally, and if possible, colonoscopy to detect synchronous lesions. The recommended treatment is then discussed at an MDT conference and neoadjuvant radio- or chemoradiotherapy given according to national guidelines. A new digital rectal examination (DRE) and proctoscopy, CT and pelvic MRI should be performed around six weeks after treatment. The purpose is to detect potential new metastases and to assess tumour response after treatment. It is crucial to do a second MDT with careful MRI evaluation to detect a possible clinical complete response. If the post-treatment MRI shows a complete or near complete response, corresponding to clinical findings on DRE and endoscopy, the patient should be offered a prospective watch and wait protocol in a dedicated institution. With proper management of patients with rectal cancer, 20-25 procent may be saved from a rectal resection and the potential risk of a permanent stoma.


Assuntos
Neoplasias Retais , Quimiorradioterapia , Exame Retal Digital , Humanos , Terapia Neoadjuvante , Estadiamento de Neoplasias , Estudos Prospectivos , Neoplasias Retais/diagnóstico por imagem , Neoplasias Retais/cirurgia , Estudos Retrospectivos , Resultado do Tratamento , Conduta Expectante
9.
Int J Colorectal Dis ; 36(10): 2189-2197, 2021 Oct.
Artigo em Inglês | MEDLINE | ID: mdl-34184127

RESUMO

BACKGROUND: Our aim was to provide data regarding use of diffusion-weighted imaging (DWI) for distinguishing metastatic and non-metastatic lymph nodes (LN) in rectal cancer. METHODS: MEDLINE library, EMBASE, and SCOPUS database were screened for associations between DWI and metastatic and non-metastatic LN in rectal cancer up to February 2021. Overall, 9 studies were included into the analysis. Number, mean value, and standard deviation of DWI parameters including apparent diffusion coefficient (ADC) values of metastatic and non-metastatic LN were extracted from the literature. The methodological quality of the studies was investigated according to the QUADAS-2 assessment. The meta-analysis was undertaken by using RevMan 5.3 software. DerSimonian, and Laird random-effects models with inverse-variance weights were used to account the heterogeneity between the studies. Mean DWI values including 95% confidence intervals were calculated for metastatic and non-metastatic LN. RESULTS: ADC values were reported for 1376 LN, 623 (45.3%) metastatic LN, and 754 (54.7%) non-metastatic LN. The calculated mean ADC value (× 10-3 mm2/s) of metastatic LN was 1.05, 95%CI (0.94, 1.15). The calculated mean ADC value of the non-metastatic LN was 1.17, 95%CI (1.01, 1.33). The calculated sensitivity and specificity were 0.81, 95%CI (0.74, 0.89) and 0.67, 95%CI (0.54, 0.79). CONCLUSION: No reliable ADC threshold can be recommended for distinguishing of metastatic and non-metastatic LN in rectal cancer.


Assuntos
Linfonodos , Neoplasias Retais , Imagem de Difusão por Ressonância Magnética , Humanos , Linfonodos/diagnóstico por imagem , Metástase Linfática/diagnóstico por imagem , Neoplasias Retais/diagnóstico por imagem , Sensibilidade e Especificidade
12.
Radiother Oncol ; 161: 132-139, 2021 08.
Artigo em Inglês | MEDLINE | ID: mdl-34126137

RESUMO

BACKGROUND AND PURPOSE: Elective irradiation of the external iliac lymph nodes (EIN) has always been advocated for T4b rectal cancer with anterior organ invasion without convincing evidence. This study aimed to explore the patterns of treatment failure for locally advanced T4b rectal cancer treated using neoadjuvant chemoradiotherapy (NCRT) and surgery. This information may help to clarify whether the current definition of the clinical target volume (CTV) is still appropriate. MATERIALS AND METHODS: We retrospectively analyzed data from 126 patients with locally advanced T4b rectal cancer who received NCRT, without elective EIN irradiation, followed by surgery between January 2010 and October 2018. Pretreatment magnetic resonance imaging was used to identify the T4b disease in all cases. The locoregional recurrence (LRR) rate and EIN failure rate were evaluated, and the LRR locations were identified using a three-dimensional model. RESULTS: After a median follow-up of 53.9 months, LRR occurred in 11.1% of patients (14/126). All LRRs were located in the previously irradiated fields and below the S2-S3 junction. The EIN failure rate was 0.8% (1/126) among all patients and 1.8% (1/56) in the group with anterior genitourinary organ invasion. The estimated 4-year distant relapse-free survival, disease-free survival and overall survival were 79.3%, 73.2% and 86.9%, respectively. CONCLUSIONS: It may be feasible to exclude the external iliac region from the CTV during NCRT for locally advanced T4b rectal cancer. However, further studies are needed to clarify whether the cranial border of the CTV can be lowered.


Assuntos
Terapia Neoadjuvante , Neoplasias Retais , Quimiorradioterapia , Humanos , Imageamento por Ressonância Magnética , Recidiva Local de Neoplasia/terapia , Estadiamento de Neoplasias , Neoplasias Retais/diagnóstico por imagem , Neoplasias Retais/patologia , Neoplasias Retais/terapia , Estudos Retrospectivos
13.
EBioMedicine ; 69: 103442, 2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-34157487

RESUMO

BACKGROUND: Accurate predictions of distant metastasis (DM) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) are helpful in developing appropriate treatment plans. This study aimed to perform DM prediction through deep learning radiomics. METHODS: We retrospectively sampled 235 patients receiving nCRT with the minimum 36 months' postoperative follow-up from three hospitals. Through transfer learning, a deep learning radiomic signature (DLRS) based on multiparametric magnetic resonance imaging (MRI) was constructed. A nomogram was established integrating deep MRI information and clinicopathologic factors for better prediction. Harrell's concordance index (C-index) and time-dependent receiver operating characteristic (ROC) were used as performance metrics. Furthermore, the risk of DM in patients with different response to nCRT was evaluated with the nomogram. FINDINGS: DLRS performed well in DM prediction, with a C-index of 0·747 and an area under curve (AUC) at three years of 0·894 in the validation cohort. The performance of nomogram was better, with a C-index of 0·775. In addition, the nomogram could stratify patients with different responses to nCRT into high- and low-risk groups of DM (P < 0·05). INTERPRETATION: MRI-based deep learning radiomics had potential in predicting the DM of LARC patients receiving nCRT and could help evaluate the risk of DM in patients who have different responses to nCRT. FUNDING: The funding bodies that contributed to this study are listed in the Acknowledgements section.


Assuntos
Interpretação de Imagem Assistida por Computador/métodos , Imageamento por Ressonância Magnética/métodos , Neoplasias Retais/diagnóstico por imagem , Idoso , Aprendizado Profundo , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Terapia Neoadjuvante , Metástase Neoplásica , Nomogramas , Neoplasias Retais/patologia , Neoplasias Retais/terapia
14.
Pan Afr Med J ; 38: 241, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34104289

RESUMO

Ganglioneuromas are benign slow-growing lesions that arise from sympathetic ganglion cells. They are usually found incidentally. Ultrasound and magnetic resonance imaging (MRI), provides only an unspecified diagnosis and it has to be confirmed by pathologic studies. Complete surgical excision is believed to be the curative treatment for symptomatic lesions. In the literature, the pelvic location reported is exceptional. We report a case of laparoscopic assisted excision of a retrorectal presacral ganglioneuroma for 22-year-old female patient.


Assuntos
Ganglioneuroma/cirurgia , Laparoscopia , Neoplasias Retais/cirurgia , Feminino , Ganglioneuroma/diagnóstico por imagem , Ganglioneuroma/patologia , Humanos , Imageamento por Ressonância Magnética , Neoplasias Retais/diagnóstico por imagem , Neoplasias Retais/patologia , Ultrassonografia , Adulto Jovem
15.
J Transl Med ; 19(1): 256, 2021 06 10.
Artigo em Inglês | MEDLINE | ID: mdl-34112180

RESUMO

BACKGROUND: We aimed to develop a radiomic model based on pre-treatment computed tomography (CT) to predict the pathological complete response (pCR) in patients with rectal cancer after neoadjuvant treatment and tried to integrate our model with magnetic resonance imaging (MRI)-based radiomic signature. METHODS: This was a secondary analysis of the FOWARC randomized controlled trial. Radiomic features were extracted from pre-treatment portal venous-phase contrast-enhanced CT images of 177 patients with rectal cancer. Patients were randomly allocated to the primary and validation cohort. The least absolute shrinkage and selection operator regression was applied to select predictive features to build a radiomic signature for pCR prediction (rad-score). This CT-based rad-score was integrated with clinicopathological variables using gradient boosting machine (GBM) or MRI-based rad-score to construct comprehensive models for pCR prediction. The performance of CT-based model was evaluated and compared by receiver operator characteristic (ROC) curve analysis. The LR (likelihood ratio) test and AIC (Akaike information criterion) were applied to compare CT-based rad-score, MRI-based rad-score and the combined rad-score. RESULTS: We developed a CT-based rad-score for pCR prediction and a gradient boosting machine (GBM) model was built after clinicopathological variables were incorporated, with improved AUCs of 0.997 [95% CI 0.990-1.000] and 0.822 [95% CI 0.649-0.995] in the primary and validation cohort, respectively. Moreover, we constructed a combined model of CT- and MRI-based radiomic signatures that achieve better AIC (75.49 vs. 81.34 vs.82.39) than CT-based rad-score (P = 0.005) and MRI-based rad-score (P = 0.003) alone did. CONCLUSIONS: The CT-based radiomic models we constructed may provide a useful and reliable tool to predict pCR after neoadjuvant treatment, identify patients that are appropriate for a 'watch and wait' approach, and thus avoid overtreatment. Moreover, the CT-based radiomic signature may add predictive value to the MRI-based models for clinical decision making.


Assuntos
Terapia Neoadjuvante , Neoplasias Retais , Área Sob a Curva , Humanos , Imageamento por Ressonância Magnética , Neoplasias Retais/diagnóstico por imagem , Neoplasias Retais/terapia , Estudos Retrospectivos , Tomografia Computadorizada por Raios X
16.
Anticancer Res ; 41(6): 3169-3178, 2021 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-34083312

RESUMO

BACKGROUND/AIM: We compared the risk factors for locally advanced lower rectal cancer (LALRC) recurrence evaluated by preoperative magnetic resonance imaging (MRI) and pathological factors analysed via the longitudinal slicing method to identify high risk groups for recurrence. PATIENTS AND METHODS: This retrospective single-institution cohort study analysed 45 consecutive patients who underwent curative surgery for LALRC. Data were analysed by an experienced radiologist and pathologist. RESULTS: Final preoperative extramural venous invasion (EMVI) and extramural depth of invasion (EMD) determined via MRI were significantly associated with EMVI and EMD determined via pathological analysis. The log-rank test for disease-free survival based on initial preoperative factors showed significantly poor prognoses for circumferential resection margin (CRM)-positive, EMVI-positive, and EMD-positive patients. CONCLUSION: Final preoperative EMVI and EMD determined via MRI correlated with pathological EMVI and EMD, especially in patients who did not undergo preoperative treatment. CRM, EMVI, and EMD determined via preoperative MRI were significant risk factors for recurrence.


Assuntos
Imageamento por Ressonância Magnética/métodos , Neoplasias Retais/patologia , Adulto , Idoso , Idoso de 80 Anos ou mais , Intervalo Livre de Doença , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Recidiva Local de Neoplasia , Neoplasias Retais/diagnóstico por imagem , Estudos Retrospectivos , Fatores de Risco
17.
Zhonghua Wei Chang Wai Ke Za Zhi ; 24(6): 536-543, 2021 Jun 25.
Artigo em Chinês | MEDLINE | ID: mdl-34148319

RESUMO

Objective: Total mesorectal excision (TME) is the gold standard for surgical treatment of mid-low rectal cancer, but the postoperative incidence of urination and sexual dysfunction is relatively high. Preserving the Denonvilliers fascia (DF) during TME can reduce the postoperative incidence of urination and sexual dysfunction. In this study, high resolution magnetic resonance imaging (MRI) was used to observe the imaging performance and display of DF, so as to determine the value of this technique in preoperative evaluation of the preservation of DF. Methods: A descriptive cohort study was carried out. Clinical data of patients with rectal cancer who underwent TME and received preoperative high-resolution MRI at department of Gastrointestinal Surgery, the Third Affiliated Hospital of Sun Yat-sen University from August 2015 to June 2017 were retrospectively analyzed. The characteristics of DF were examined, and the shortest distance (d) between the anterior edge of tumor and DF was measured on high-resolution MRI. The distance d was compared between patients with stage T1-T2 and those with stage T3. Receiver operating characteristic (ROC) analysis was used to determine the predictive value of d for stage T1-T2 disease. Results: Thirty-two patients were enrolled in the study, including 27 males and 5 females with mean age of (62.9±8.9) years. DF was visualized in 96.9% (31/32) of cases on the T2WI sequence. The mean distance d in patients with stage T1-T2 disease (n=23) was (6.73±2.65) mm, and in those with stage T3 disease (n=9) was (1.30±1.15) mm (t=5.893, P<0.001). A cutoff of d >3 mm yielded specificity and positive predictive value for diagnosing stage T1-T2 disease of both 100%, sensitivity of 95.7% and negative predictive value of 90%. The optimum threshold of d was >3.05 mm, and Youden index was 0.957. Conclusions: High-resolution MRI can show the DF and accurately evaluate the relationship of DF with tumor in rectal cancer patients. Analysis on d value can provide an objective basis for the safe preservation of DF.


Assuntos
Neoplasias Retais , Idoso , Estudos de Coortes , Fáscia/diagnóstico por imagem , Fáscia/patologia , Feminino , Humanos , Imageamento por Ressonância Magnética , Masculino , Pessoa de Meia-Idade , Estadiamento de Neoplasias , Neoplasias Retais/diagnóstico por imagem , Neoplasias Retais/patologia , Neoplasias Retais/cirurgia , Estudos Retrospectivos
18.
World J Gastroenterol ; 27(18): 2122-2130, 2021 May 14.
Artigo em Inglês | MEDLINE | ID: mdl-34025068

RESUMO

Rectal magnetic resonance imaging (MRI) is the preferred method for the diagnosis of rectal cancer as recommended by the guidelines. Rectal MRI can accurately evaluate the tumor location, tumor stage, invasion depth, extramural vascular invasion, and circumferential resection margin. We summarize the progress of research on the use of artificial intelligence (AI) in rectal cancer in recent years. AI, represented by machine learning, is being increasingly used in the medical field. The application of AI models based on high-resolution MRI in rectal cancer has been increasingly reported. In addition to staging the diagnosis and localizing radiotherapy, an increasing number of studies have reported that AI models based on high-resolution MRI can be used to predict the response to chemotherapy and prognosis of patients.


Assuntos
Inteligência Artificial , Neoplasias Retais , Humanos , Imageamento por Ressonância Magnética , Margens de Excisão , Estadiamento de Neoplasias , Neoplasias Retais/diagnóstico por imagem , Neoplasias Retais/patologia , Neoplasias Retais/terapia , Reto/patologia
19.
Radiol Med ; 126(8): 1044-1054, 2021 Aug.
Artigo em Inglês | MEDLINE | ID: mdl-34041663

RESUMO

PURPOSE: Standardized index of shape (SIS) tool validation to examine dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) in preoperative chemo-radiation therapy (pCRT) assessment of locally advanced rectal cancer (LARC) in order to guide the surgeon versus more or less conservative treatment. MATERIALS AND METHODS: A total of 194 patients (January 2008-November 2020), with III-IV locally advanced rectal cancer and subjected to pCRT were included. Three expert radiologists performed DCE-MRI analysis using SIS tool. Degree of absolute agreement among measurements, degree of consistency among measurements, degree of reliability and level of variability were calculated. Patients with a pathological tumour regression grade (TRG) 1 or 2 were classified as major responders (complete responders have TRG 1). RESULTS: Good significant correlation was obtained between SIS measurements (range 0.97-0.99). The degree of absolute agreement ranges from 0.93 to 0.99, the degree of consistency from 0.81 to 0.9 and the reliability from 0.98 to 1.00 (p value < < 0.001). The variability coefficient ranges from 3.5% to 26%. SIS value obtained to discriminate responders by non-responders a sensitivity of 95.9%, a specificity of 84.7% and an accuracy of 91.8% while to detect complete responders, a sensitivity of 99.2%, a specificity of 63.9% and an accuracy of 86.1%. CONCLUSION: SIS tool is suitable to assess pCRT response both to identify major responders and complete responders in order to guide the surgeon versus more or less conservative treatment.


Assuntos
Quimiorradioterapia , Meios de Contraste , Imageamento por Ressonância Magnética/métodos , Imageamento por Ressonância Magnética/normas , Neoplasias Retais/diagnóstico por imagem , Neoplasias Retais/terapia , Adulto , Idoso , Idoso de 80 Anos ou mais , Tomada de Decisão Clínica , Feminino , Humanos , Masculino , Pessoa de Meia-Idade , Terapia Neoadjuvante , Estadiamento de Neoplasias , Neoplasias Retais/patologia , Estudos Retrospectivos , Resultado do Tratamento
20.
Eur Radiol ; 31(7): 4739-4750, 2021 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-34003351

RESUMO

OBJECTIVES: To evaluate the baseline MRI characteristics for predicting survival outcomes and construct survival models for risk stratification to facilitate personalized treatment and follow-up strategies in patients with MRI-defined T3 (mrT3) locally advanced rectal cancer (LARC). METHODS: We retrospectively reviewed 256 mrT3 LARC patients evaluated between 2008 and 2012 in our institution, with an average follow-up period of 6.8 ± 1.2 years. The baseline MRI characteristics, clinical data, and follow-up information were evaluated. The patients were randomized into a training cohort (TC, 186 patients) and validation cohort (VC, 70 patients). The TC dataset was used to develop multivariate nomograms for disease-free survival (DFS) and overall survival (OS), while the VC dataset was used for independent validation of the models. Harrell concordance (C) indices and Hosmer-Lemeshow calibration were used to evaluate the performances of the models. RESULTS: Baseline mrT3 substage, extramural venous invasion (EMVI) grading, mucinous adenocarcinoma, mesorectal fascia involvement, elevated pretreatment carcinoembryonic antigen level, and neoadjuvant chemoradiotherapy (NCRT) were independent predictors of DFS. T3 substage, EMVI grading, and NCRT were also independent predictors of OS. The nomograms constructed permitted the individualized prediction of 3-year and 5-year DFS and 5-year OS with high discrimination (C-index range, 0.833-0.892) and good calibration in the TC and VC. CONCLUSIONS: We have identified baseline MRI characteristics that help independently predict survival outcomes in patients with mrT3 LARC. The survival models based on these characteristics allow for the individualized pretreatment risk stratification in patients with mrT3 LARC. KEY POINTS: • Baseline MRI characteristics can independently stratify risk and predict survival outcomes in patients with mrT3 LARC. • The nomograms built using selected baseline MRI characteristics facilitate the individualized pretreatment risk stratification and help with clinical decision-making in patients with mrT3 LARC. • MR-defined risk factors should, therefore, be carefully reported in the baseline MRI evaluation.


Assuntos
Neoplasias Retais , Quimiorradioterapia , Intervalo Livre de Doença , Humanos , Imageamento por Ressonância Magnética , Terapia Neoadjuvante , Prognóstico , Neoplasias Retais/diagnóstico por imagem , Neoplasias Retais/terapia , Estudos Retrospectivos , Fatores de Risco
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