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Objectives: To investigate the association between preoperative texture analysis from multidetector computed tomography (MDCT) and overall survival in patients with gastric cancer. Methods: Institutional review board approval and informed consent were obtained. Fifty-six patients with biopsy-proved gastric cancer were examined by MDCT and treated with surgery. Image features from texture analysis were quantified, with and without filters for fine to coarse textures. The association with survival time was assessed using Kaplan–Meier and Cox analysis. Results: The following parameters were significantly associated with a negative prognosis, according to different thresholds: energy [no filter] – Logarithm of relative risk (Log RR): 3.25; p = 0.046; entropy [no filter] (Log RR: 5.96; p = 0.002); entropy [filter 1.5] (Log RR: 3.54; p = 0.027); maximum Hounsfield unit value [filter 1.5] (Log RR: 3.44; p = 0.027); skewness [filter 2] (Log RR: 5.83; p = 0.004); root mean square [filter 1] (Log RR: - 2.66; p = 0.024) and mean absolute deviation [filter 2] (Log RR: - 4.22; p = 0.007). Conclusions: Texture analysis could increase the performance of a multivariate prognostic model for risk stratification in gastric cancer. Further evaluations are warranted to clarify the clinical role of texture analysis from MDCT. Key points: • Textural analysis from computed tomography can be applied in gastric cancer. • Preoperative non-invasive texture features are related to prognosis in gastric cancer. • Texture analysis could help to evaluate the aggressiveness of this tumour.

To investigate the role of the apparent diffusion coefficient (ADC) as a potential prognostic biomarker in the evaluation of the aggressiveness of oesophageal cancer.Between November 2009 and December 2013, 43 patients with evidence of oesophageal or oesophago-gastric junction cancer were referred to our institution and prospectively entered in our database. The final study population consisted of 23 patients (18 men; 5 women; mean age, 64.62 ± 10.91 years) who underwent diffusion-weighted Magnetic Resonance before surgical intervention. Specifically, 14 were directly treated with surgery and 9 were addressed to chemo/radiotherapy beforehand. Two radiologists independently measured mean tumour ADC and inter-observer agreement (Spearman's and intraclass correlation coefficient [ICC]) was assessed. In the univariate analysis, overall survival curves related to pathological ADC, pT, pN, tumour location and histotype were fitted using the Kaplan-Meier method. Survival curves were then compared using the log-rank test.Inter-observer reproducibility was very good (Spearman's rho = 0.95; ICC = 0.94). At a total median follow-up of 19 months (2-49 months), 4 patients had died. The median follow-up was 18.50 months (5-49 months) for the surgery-only group (1/4 events, 25 %) and 24 months (2-34 months) for the chemo/radiotherapy group (3/4 events, 75 %). Survival time at 48 months for the overall population was 59 % (±0.11), while for the surgery-only group and the chemo/radiotherapy group was 90 % (±0.09) and 61 % (±0.34), respectively. In the univariate analysis, ADC values below or equal to 1.4 × 10(-3) mm(2)/s were associated with a negative prognosis both in the total population (P = 0.016) and in the surgery-only group (P 0.001).Despite the biggest limitation of our study (i.e. the small study population), we were able to show that pathological ADC could be considered a prognostic factor for oesophageal cancer. DWI might be introduced into clinical practice as a promising and reliable technique in the diagnostic pathway of this tumour.

To assess changes in apparent diffusion coefficient (ΔADC) and volume (ΔV) after neoadjuvant treatment (NT), and tumour regression grade (TRG) in gastro-oesophageal cancers (GEC), and to discriminate responders from non-responders.Thirty-two patients with biopsy-proven locally-advanced GEC underwent diffusion weighted magnetic resonance imaging (DWI) pre- and post-NT. Lesion ADC, volume, ΔADC and ΔV were calculated. TRG 1-2-3 patients were classified as R; TRG 4-5 as non-responders. ΔADC-TRG and ΔV-TRG correlations, pre-NT and post-NT ADC, ΔADC and ΔV cut-off values for responders and non-responders were calculated. Two readers measured mean tumour ADCs and interobserver variability was calculated. (Spearman's and intraclass correlation coefficient [ICC]).The interobserver reproducibility was very good both for pre-NT (Spearman's rho = 0.8160; ICC = 0.8993) and post-NT (Spearman's rho = 0.8357; ICC = 0.8663). Responders showed lower pre-NT ADC (1.32 versus 1.63 × 10(-3) mm(2)/s; P = 0.002) and higher post-NT ADC (2.22 versus 1.51 × 10(-3) mm(2)/s; P = 0.001) than non-responders and ADC increased in responders (ΔADC, 85.45 versus -8.21 %; P = 0.00005). ΔADC inversely correlated with TRG (r = -0.71, P = 0.000004); no difference in ΔV between responders and non-responders (-50.92 % versus -14.12 %; P = 0.068) and no correlation ΔV-TRG (r = 0.02 P = 0.883) were observed.The ADC can be used to assess gastro-oesophageal tumour response to neoadjuvant treatment as a reliable expression of tumour regression.• DWI is now being used to assess many cancers. • Change in ADC measurements offer new information about oesophageal tumours. • ADC changes are more reliable than dimensional criteria in assessing neoadjuvant treatment. • Such ADC assessment could optimise management of locally advanced gastro-oesophageal cancers.

Liver thermal ablation is an alternative treatment for hepatocellular carcinoma (HCC) and secondary liver malignancies. Microwave ablation (MWA) produces large ablation zones (AZ) in short time; however, AZ prediction is based on preclinical ex vivo models, rising concerns about reproducibility and safety in humans. We aimed to investigate the effects produced by a new-generation MWA system on human liver in vivo with different approaches (percutaneous or intraoperative) and liver conditions (cirrhosis or previous chemotherapy treatment), in comparison with manufacturer-provided predictions based on ex vivo animal models. Complete tumor ablation (CA) and early clinical outcomes were also assessed. From October 2014, 60 consecutive patients (cirrhotic = 31; non-cirrhotic = 10; chemotherapy-treated = 19) with 81 liver nodules (HCC = 31; mets = 50) underwent MWA procedures (percutaneous = 30; laparotomic = 18; laparoscopic = 12), with a 2450 MHz/100 W generator with Thermosphere™ Technology (Emprint™, Medtronic). A contrast-enhanced CT or MR was performed after one month to assess CA and measure AZ. A linear correlation between AZ volumes and ablation times was observed in vivo, without differences from manufacturer-provided ex vivo predictions in all operative approaches and liver conditions. Other independent variables (sex, age, nodule location) showed no relationship when added to the model. Median (IQR) longitudinal and transverse roundness-indexes of the AZs were, respectively, 0.77(0.13) and 0.93(0.11). CA at 1 month was 93% for percutaneous and 100% for intraoperative procedures (p = 0.175). Thirty-day morbidity and mortality were 3% and 0%. MWA with Thermosphere™ Technology produces predictable AZs on human liver in vivo, according to manufacturer-provided ex vivo predictions. In our experience, this new-generation MWA system is effective and safe to treat liver malignancies in different operative and clinical settings.

Despite the considerable progress in understanding the molecular bases of acute myeloid leukemia (AML), new tools to link disease biology to the unpredictable patient clinical course are still needed. Herein, high-throughput metabolomics, combined with the other “-omics” disciplines, holds promise in identifying disease-specific and clinically relevant features. In this study, we took advantage of nuclear magnetic resonance (NMR) to trace AML-associated metabolic trajectory employing two complementary strategies. On the one hand, we performed a prospective observational clinical trial to identify metabolic changes associated with blast clearance during the first two cycles of intensive chemotherapy in nine adult patients. On the other hand, to reduce the intrinsic variability associated with human samples and AML genetic heterogeneity, we analyzed the metabolic changes in the plasma of immunocompromised mice upon engraftment of primary human AML blasts. Combining the two longitudinal approaches, we narrowed our screen to seven common metabolites, for which we observed a mirror-like trajectory in mice and humans, tracing AML progression and remission, respectively. We interpreted this set of metabolites as a dynamic fingerprint of AML evolution. Overall, these NMR-based metabolomic data, to be consolidated in larger cohorts and integrated in more comprehensive system biology approaches, hold promise for providing valuable and non-redundant information on the systemic effects of leukemia. Electronic supplementary material The online version of this article (doi:10.1186/s13045-016-0346-2) contains supplementary material, which is available to authorized users.

BACKGROUND: Currently, approximately 80% of melanoma patients undergoing sentinel node biopsy (SNB) have negative sentinel lymph nodes (SLNs), and no prediction system is reliable enough to be implemented in the clinical setting to reduce the number of SNB procedures. In this study, the predictive power of support vector machine (SVM)-based statistical analysis was tested. METHODS: The clinical records of 246 patients who underwent SNB at our institution were used for this analysis. The following clinicopathologic variables were considered: the patient's age and sex and the tumor's histological subtype, Breslow thickness, Clark level, ulceration, mitotic index, lymphocyte infiltration, regression, angiolymphatic invasion, microsatellitosis, and growth phase. The results of SVM-based prediction of SLN status were compared with those achieved with logistic regression. RESULTS: The SLN positivity rate was 22% (52 of 234). When the accuracy was > or = 80%, the negative predictive value, positive predictive value, specificity, and sensitivity were 98%, 54%, 94%, and 77% and 82%, 41%, 69%, and 93% by using SVM and logistic regression, respectively. Moreover, SVM and logistic regression were associated with a diagnostic error and an SNB percentage reduction of (1) 1% and 60% and (2) 15% and 73%, respectively. CONCLUSIONS: The results from this pilot study suggest that SVM-based prediction of SLN status might be evaluated as a prognostic method to avoid the SNB procedure in 60% of patients currently eligible, with a very low error rate. If validated in larger series, this strategy would lead to obvious advantages in terms of both patient quality of life and costs for the health care system.