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Background An extended cholecystectomy is generally recommended for patients with gallbladder carcinoma (GBC) at stage T2 or above, and involves cholecystectomy and a limited hepatic resection. It is important to differentiate tumor-adjacent and tumor-distant liver tissues before surgery for surgical strategies decision making. Diffusion-weighted imaging (DWI) can provide functional information by depicting the diffusion of water in biological tissue and can be quantitated by apparent diffusion coefficient (ADC). Our study aimed to evaluate feasibility of ADC and its standard deviation (ADCsd) to differentiate between tumor-adjacent and tumor-distant liver tissues in T3 staged resectable GBC. Methods A total of 41 consecutive patients with T3 staged resectable GBC were included in this retrospective study and divided into two sets with (n = 27) and without (n = 14) liver invasion. All patients underwent DWI at b-values of 0, 20, 50, 80, 100, 200, 400, 600, 800 and 1000 sec/mm2 with a 3.0-T magnetic resonance scanner before surgery. Mean ADC and ADCsd of tumor-adjacent and tumor-distant liver tissues were measured on DWI, and were compared by Mann-Whitney U tests. If there was a significant difference in any derived parameter, area under the receiver operating characteristic curve (AUC) was used to assess performance of this parameter for differentiating tumor-adjacent and tumor-distant liver tissues. Results In the set with liver invasion, mean ADC and ADCsd of tumor-adjacent liver tissue were lower than those of tumor-distant liver tissue when b = 0, 800 sec/mm2, and b = 0, 1000 sec/mm2, respectively (all P-values sd at all b values had no statistical differences between tumor-adjacent and tumor-distant liver tissues (all P-values > 0.05). To differentiate tumor-adjacent from tumor-distant liver tissues in the set with liver invasion, AUCs of ADC were 0.687 (b = 0, 800 sec/mm2) and 0.680 (b = 0, 1000 sec/mm2), respectively; and those of ADCsd were 0.673 (b = 0, 800 sec/mm2) and 0.731 (b = 0, 1000 sec/mm2), respectively. Conclusions In patients with liver invasion by T3 staged GBC, ADC and ADCsd could have potential value in differentiating tumor-adjacent from tumor-distant liver tissues.

Background It is essential to accurately determine resectability of thoracic esophageal squamous cell carcinoma (ESCC) for treatment decision making. Previous studies revealed that CT-derived gross tumour volume (GTV) associates well with T category of ESCC, nodal metastases and N stage, treatment failure rate, and disease survival rate. This study aimed to explore whether anatomical distribution-based GTV of non-distant metastatic thoracic ESCC measured with multidetector computed tomography (MDCT) could quantitatively determine the resectability. Methods 473 consecutive patients with biopsy-confirmed non-distant metastatic thoracic ESCC underwent contrast-enhanced CT were randomized into the training (TC, 376 patients) and validation (VC, 97 patients) cohorts. GTV was retrospectively measured on MDCT. Univariate and multivariate analyses were performed to identify risk factors of non-distant metastatic ESCC resectability in TC. Subsequently, Mann-Whitney U test was applied to compare GTV based on different anatomic distributions between patients of resectable and unresectable ESCCs. Receiver operating characteristic (ROC) analysis was to clarify if anatomical distribution-based GTV could help quantitatively determinate resectability. Unweighted Cohen’s Kappa tests in VC were to assess the performance of the previous models. Results Univariate analysis demonstrated that gender, anatomic distribution, cT stage, cN stage and GTV were related to resectability of non-distant metastatic ESCC (all P-values P 3, 22.89 cm3 and 22.58 cm3 to determine resectability with areas under the ROC curves of more than 0.9, respectively. Unweighted Cohen’s Kappa tests showed an excellent performance of the ROC models in the upper, middle and lower thoracic portions with Cohen k-values of 0.913, 0.879 and 0.871, respectively. Conclusions GTV and anatomic distribution of non-distant metastatic thoracic ESCC could be independent risk factors of resectability, and anatomical distribution-based GTV could well quantitatively determine resectability.

ObjectiveTo investigate relationship of tumor stage-based gross tumor volume (GTV) of esophageal squamous cell carcinoma (ESCC) measured on computed tomography (CT) with early recurrence (ER) after esophagectomy.Materials and MethodsTwo hundred and four consecutive patients with resectable ESCC including 159 patients enrolled in the training cohort (TC) and 45 patients in validation cohort (VC) underwent contrast-enhanced CT less than 2 weeks before esophagectomy. GTV was retrospectively measured by multiplying sums of all tumor areas by section thickness. For the TC, univariate and multivariate analyses were performed to determine factors associated with ER. Mann-Whitney U test was conducted to compare GTV in patients with and without ER. Receiver operating characteristic (ROC) analysis was performed to determine if tumor stage-based GTV could predict ER. For the VC, unweighted Cohen’s Kappa tests were used to evaluate the performances of the previous ROC predictive models.ResultsER occurred in 63 of 159 patients (39.6%) in the TC. According to the univariate analysis, histologic differentiation, cT stage, cN stage, and GTV were associated with ER after esophagectomy (all P-values < 0.05). Multivariate analysis revealed that cT stage and GTV were independent risk factors with hazard ratios of 3.382 [95% confidence interval (CI): 1.533–7.459] and 1.222 (95% CI: 1.125–1.327), respectively (all P-values < 0.05). Mann-Whitney U tests showed that GTV could help differentiate between ESCC with and without ER in stages cT1-4a, cT2, and cT3 (all P-values < 0.001), and the ROC analysis demonstrated the corresponding cutoffs of 13.31, 17.22, and 17.83 cm3 with areas under the curve of more than 0.8, respectively. In the VC, the Kappa tests validated that the ROC predictive models had good performances for differentiating between ESCC with and without ER in stages cT1-4a, cT2, and cT3 with Cohen k of 0.696 (95% CI, 0.498–0.894), 0.733 (95% CI, 0.386–1.080), and 0.862 (95% CI, 0.603–1.121), respectively.ConclusionGTV and cT stage can be independent risk factors of ER in ESCC after esophagectomy, and tumor stage-based GTV measured on CT can help predict ER.

To evaluate whether combinations of liver lobe and spleen volumes obtained on magnetic resonance imaging (MRI) could predict esophagogastric variceal bleeding (EVB) in hepatitis B-related cirrhotic patients. Ninety-six consecutive patients with hepatitis B-related cirrhosis underwent upper abdominal contrast-enhanced MRI within 1 week after initial hospitalization, and grouped based on outcomes of EVB during the 2 years' follow-up after being discharged. Total liver volume (TLV), spleen volume (SV) and 4 liver lobe volumes including right lobe volume (RV), left medial lobe volume (LMV), left lateral lobe volume (LLV), and caudate lobe volume (CV) were measured on MRI. Percentages of individual liver lobe volumes in TLV (including RV/TLV, LMV/TLV, LLV/TLV, and CV/TLV), ratios of SV to individual liver lobe volumes (including SV/RV, SV/LMV, SV/LLV, and SV/CV), and SV/TLV were statistically analyzed to predict EVB. Patients with EVB had lower RV than without EVB (P value = .001), whereas no differences in LMV, LLV, CV, and TLV were found (P values .05 for all). Among percentages of individual liver lobe volumes in TLV, RV/TLV was lower whereas LMV/TLV and LLV/TLV were greater in patients with EVB than without EVB (P values .05 for all). SV, ratios of SV to individual liver lobe volumes, and SV/TLV in patients with EVB were larger than without EVB (P values .05 for all). Among parameters with difference between patients with and without EVB, SV/RV could best predict EVB with an area under receiver operating characteristic curve of 0.84. SV/RV could best predict EVB in hepatitis B-related cirrhotic patients.

BACKGROUND: Prediction of lymph node status in esophageal squamous cell carcinoma (ESCC) is critical for clinical decision making. In clinical practice, computed tomography (CT) has been frequently used to assist in the preoperative staging of ESCC. Texture analysis can provide more information to reflect potential biological heterogeneity based on CT. A nomogram for the preoperative diagnosis of lymph node metastasis in patients with resectable ESCC has been previously developed. However, to the best of our knowledge, no reports focus on developing CT radiomics features to discriminate ESCC patients with regional lymph node metastasis (RLNM) and non-regional lymph node metastasis (NRLNM). We, therefore, aimed to develop CT radiomics models to predict lymph node metastasis (LNM) in advanced ESCC and to discriminate ESCC between RLNM and NRLNM. METHODS: This study enrolled 334 patients with pathologically confirmed advanced ESCC, including 152 patients without LNM and 182 patients with LNM, and 103 patients with RLNM and 79 patients NRLNM. Radiomics features were extracted from CT data for each patient. The least absolute shrinkage and selection operator (LASSO) model and independent samples t-tests or Mann-Whitney U tests were exploited for dimension reduction and selection of radiomics features. Optimal radiomics features were chosen using multivariable logistic regression analysis. The discriminating performance was assessed by area under the receiver operating characteristic curve (AUC) and accuracy. RESULTS: The radiomics features were developed based on multivariable logistic regression and were significantly associated with LNM status in both the training and validation cohorts (P

BackgroundEarly recurrence of oesophageal squamous cell carcinoma (SCC) is defined as recurrence after surgery within 1 year, and appears as local recurrence, distant recurrence, and lymph node positive and disseminated recurrence. Contrast-enhanced computed tomography (CECT) is recommended for diagnosis of primary tumor and initial staging of oesophageal SCC, but it cannot be used to predict early recurrence. It is reported that radiomics can help predict preoperative stages of oesophageal SCC, lymph node metastasis before operation, and 3-year overall survival of oesophageal SCC patients following chemoradiotherapy by extracting high-throughput quantitative features from CT images. This study aimed to develop models based on CT radiomics and clinical features of oesophageal SCC to predict early recurrence of locally advanced cancer.MethodsWe collected electronic medical records and image data of 197 patients with confirmed locally advanced oesophageal SCC. These patients were randomly allocated to 137 patients in the training cohort and 60 in the test cohort. 352 radiomics features were extracted by delineating region-of-interest (ROI) around the lesion on CECT images and clinical signature was generated by medical records. The radiomics model, clinical model, the combined model of radiomics and clinical features were developed by radiomics features and/or clinical characteristics. Predicting performance of the three models was assessed with area under receiver operating characteristic curve (AUC), accuracy and F-1 score.ResultsEleven radiomics features and/or six clinical signatures were selected to build prediction models related to recurrence of locally advanced oesophageal SCC after trimodal therapy. The AUC of integration of radiomics and clinical models was better than that of radiomics or clinical model for the training cohort (0.821 versus 0.754 or 0.679, respectively) and for the validation cohort (0.809 versus 0.646 or 0.658, respectively). Integrated model of radiomics and clinical features showed good performance in predicting early recurrence of locally advanced oesophageal SCC for both the training and validation cohorts (accuracy = 0.730 and 0.733, and F-1score = 0.730 and 0.778, respectively).ConclusionsThe integrated model of CECT radiomics and clinical features may be a potential imaging biomarker to predict early recurrence of locally advanced oesophageal SCC after trimodal therapy.

Liver injury is found in some patients with coronavirus disease-2019 (COVID-19). Both the clinical treatment efficacy and the patient's prognosis are affected by the severity of liver injury. In addition, in some cases, liver injury may occur in the absence of respiratory symptoms. To date, liver injury diagnosed based on laboratory findings and abdominal computed tomography (CT) has been reported in COVID-19 patients. The aim of this review was to summarize the mechanism of liver injury caused by COVID-19 and describe the CT features of COVID-19-induced liver damage.