McKittrick-Wheelock syndrome: a rare cause of acute renal failure and hypokalemia not to be overlooked.

McKittrick-Wheelock syndrome is a rare disorder in which a colorectal tumor (usually a villous adenoma) determines secretory mucous diarrhea, which in turn leads to prerenal acute renal failure, hyponatremia, hypokalemia and metabolic acidosis. Even though the outcome is usually favorable with complete recovery after surgery, the diagnosis is often delayed, making the patient susceptible to life-threatening complications, mainly severe acidosis and hypokalemia. We present two paradigmatic cases with extreme electrolytes imbalance and complete recovery following the appropriate treatment. The pathogenesis of this degenerative condition is discussed in detail.

Preliminary report on harmonization of features extraction process using the ComBat tool in the multi-center "Blue Sky Radiomics" study on stage III unresectable NSCLC.

BACKGROUND AND PURPOSE: In the retrospective-prospective multi-center "Blue Sky Radiomics" study (NCT04364776), we plan to test a pre-defined radiomic signature in a series of stage III unresectable NSCLC patients undergoing chemoradiotherapy and maintenance immunotherapy. As a necessary preliminary step, we explore the influence of different image-acquisition parameters on radiomic features' reproducibility and apply methods for harmonization. MATERIAL AND METHODS: We identified the primary lung tumor on two computed tomography (CT) series for each patient, acquired before and after chemoradiation with i.v. contrast medium and with different scanners. Tumor segmentation was performed by two oncological imaging specialists (thoracic radiologist and radio-oncologist) using the Oncentra Masterplan® software. We extracted 42 radiomic features from the specific ROIs (LIFEx). To assess the impact of different acquisition parameters on features extraction, we used the Combat tool with nonparametric adjustment and the longitudinal version (LongComBat). RESULTS: We defined 14 CT acquisition protocols for the harmonization process. Before harmonization, 76% of the features were significantly influenced by these protocols. After, all extracted features resulted in being independent of the acquisition parameters. In contrast, 5% of the LongComBat harmonized features still depended on acquisition protocols. CONCLUSIONS: We reduced the impact of different CT acquisition protocols on radiomic features extraction in a group of patients enrolled in a radiomic study on stage III NSCLC. The harmonization process appears essential for the quality of radiomic data and for their reproducibility. ClinicalTrials.gov Identifier: NCT04364776, First Posted:April 28, 2020, Actual Study Start Date: April 15, 2020, https://clinicaltrials.gov/ct2/show/NCT04364776 .

Radiomics features as predictive and prognostic biomarkers in NSCLC.

Introduction: Radiomics extracts a large amount of quantitative information from medical images using specific data characterization algorithms. This information, called radiomic features, can be combined with clinical data to build prediction models for prognostic evaluation and treatment selection.Areas covered: We outlined a series of studies investigating the correlation between radiomics features and outcome (prognostic) as well as response to therapy (predictive) in non-small cell lung cancer (NSCLC). We performed our analysis both in the setting of early and advanced stage of disease, with a focus on the different therapies and imaging modalities adopted.Expert opinion: The prognostic and predictive potential of the radiomic approach, combined with clinical models, could help decision-making process and guide toward the creation of an optimal and 'tailored' therapeutic strategy for lung cancer patients. However, due to the low reproducibility of most of the conducted studies and the lack of validated results, such a desirable scenario has not yet been translated to routine clinical practice.

Dose-related effects of metformin on acid-base balance and renal function in patients with diabetes who develop acute renal failure: a cross-sectional study.

AIMS: The role of metformin in the development of lactic acidosis (LA) in the setting of acute renal failure (ARF) is debated. Moreover, recent experiments suggested that metformin can also be nephrotoxic, but little clinical data exist about this topic. We sought to investigate these possible associations in a large cohort of patients with diabetes who developed ARF. METHODS: We analyzed data from patients with diabetes admitted to our emergency department between 2007 and 2011 with ARF and a previously normal renal function (n = 126). We compared acid-base balance and renal function of patients taking metformin (n = 74) with patients not taking it (n = 52). RESULTS: Patients taking metformin had decreased pH (7.31 ± 0.16 vs 7.39 ± 0.11, p = 0.008) and higher lactates (4.54 ± 4.30 vs 1.71 ± 1.14 mmol/L, p < 0.001). Both acidosis (pH < 7.35) and LA (lactates >5 mmol/L and pH < 7.35) were more frequently observed in this group (p = 0.0491 and p < 0.001, respectively). Multivariate analysis ruled out the role of some possible confounders, especially decreased renal function. The influence of metformin on pH and lactates grew significantly with higher doses of the drug (p = 0.259 and p = 0.092 for <1 g/day, p = 0.289 and p < 0.001 for 1-2 g/day, p = 0.009 and p < 0.001 for 2-3 g/day, for pH and lactates, respectively). Metformin influenced creatinine levels in a dose-related manner as well (p = 0.925 for <1 g/day, p = 0.033 for 1-2 g/day, p < 0.001 for 2-3 g/day). CONCLUSIONS: In patients with diabetes who were admitted to our emergency department with ARF, the use of metformin was associated in a dose-related fashion with both LA and worse renal function.