Surgery for Intraductal Papillary Mucinous Neoplasms of the Pancreas: Preoperative Factors Tipping the Scale of Decision-Making

Background: Decision-making in intraductal papillary mucinous neoplasms (IPMNs) of the pancreas depends on scaling the risk of malignancy with the surgical burden of a pancreatectomy. This study aimed to develop a preoperative, disease-specific tool to predict surgical morbidity for IPMNs. Methods: Based on preoperative variables of resected IPMNs at two high-volume institutions, classification tree analysis was applied to derive a predictive model identifying the risk factors for major morbidity (Clavien-Dindo ≥3) and postoperative pancreatic insufficiency. Results: Among 524 patients, 289 (55.2%) underwent pancreaticoduodenectomy (PD), 144 (27.5%) underwent distal pancreatectomy (DP), and 91 (17.4%) underwent total pancreatectomy (TP) for main-duct (18.7%), branch-duct (12.6%), or mixed-type (68.7%) IPMN. For 98 (18.7%) of the patients, major morbidity developed. The classification tree distinguished different probabilities of major complications based on the type of surgery (area under the surve [AUC] 0.70; 95% confidence interval [CI], 0.63-0.77). Among the DP patients, the presence of preoperative diabetes identified two risk classes with respective probabilities of 5% and 25% for the development of major morbidity, whereas among the PD/TP patients, three different classes with respective probabilities of 15%, 20%, and 36% were identified according to age and body mass index (BMI). Overall, history of diabetes, age, and cyst size segregated three different risk classes for new-onset/worsening diabetes. Conclusions: In presumed IPMNs, the disease-specific risk of major morbidity and pancreatic insufficiency can be determined in the preoperative setting and used to personalize the possible surgical indication. Age and overweight status in case of PD/TP and diabetes in case of DP tip the scale toward less aggressive clinical management in the absence of features suggestive for malignancy

Cell colony counter called CoCoNut

Clonogenic assays are powerful tools for testing cell reproductive death after biological damage caused by, for example, ionizing radiation. Traditionally, the methods require a cumbersome, slow and eye-straining manual counting of viable colonies under a microscope. To speed up the counting process and minimize those issues related to the subjective decisions of the scoring personnel, we developed a semi-automated, image-based cell colony counting setup, named CoCoNut (Colony Counter developed by the Nutech department at the Technical University of Denmark). It consists in an ImageJ macro and a photographic 3D-printed light-box, conceived and demonstrated to work together for Crystal Violet-stained colonies. Careful attention was given to the image acquisition process, which allows background removal (i.e. any unwanted element in the picture) in a minimally invasive manner. This is mainly achieved by optimal lighting conditions in the light-box and dividing the image of a flask that contains viable colonies by the picture of an empty flask. In this way, CoCoNut avoids using aggressive background removal filters that usually lead to suboptimal colony count recovery. The full method was tested with V79 and HeLa cell survival samples. Results were compared to other freely available tools. CoCoNut proved able to successfully distinguish between single and merged colonies and to identify colonies bordering on flask edges. CoCoNut software calibration is fast; it requires the adjustment of a single parameter that is the smallest colony area to be counted. The employment of a single parameter reduces the risk of subjectivity, providing a robust and user-friendly tool, whose results can be easily compared over time and among different bio-laboratories. The method is inexpensive and easy to obtain. Among its advantages, we highlight the possibility of combining the macro with a perfectly reproducible 3D-printed light-box. The CoCoNut software and the 3D-printer files are provided as supporting information (S1 CoCoNut Files).</div

Monitoring tumor growth rate to predict immune checkpoint inhibitors' treatment outcome in advanced NSCLC

Introduction: Radiological response assessment to immune checkpoint inhibitor is challenging due to atypical pattern of response and commonly used RECIST 1.1 criteria do not take into account the kinetics of tumor behavior. Our study aimed at evaluating the tumor growth rate (TGR) in addition to RECIST 1.1 criteria to assess the benefit of immune checkpoint inhibitors (ICIs). Methods: Tumor real volume was calculated with a dedicated computed tomography (CT) software that semi-automatically assess tumor volume. Target lesions were identified according to RECIST 1.1. For each patient, we had 3 measurement of tumor volume. CT-1 was performed 8-12 weeks before ICI start, the CT at baseline for ICI was CT0, while CT + 1 was the first assessment after ICI. We calculated the percentage increase in tumor volume before (TGR1) and after immunotherapy (TGR2). Finally, we compared TGR1 and TGR2. If no progressive disease (PD), the group was disease control (DC). If PD but TGR2 &lt; TGR1, it was called LvPD and if TGR2 &gt; TGR1, HvPD. Results: A total of 61 patients who received ICIs and 33 treated with chemotherapy (ChT) were included. In ICI group, 18 patients were HvPD, 22 LvPD, 21 DC. Median OS was 4.4 months (95% CI: 2.0-6.8, reference) for HvPD, 7.1 months (95% CI 5.4-8.8) for LvPD, p = 0.018, and 20.9 months (95% CI: 12.5-29.3) for DC, p &lt; 0.001. In ChT group, 7 were categorized as HvPD, 17 as LvPD and 9 as DC. No difference in OS was observed in the ChT group (p = 0.786) Conclusion: In the presence of PD, a decrease in TGR may result in a clinical benefit in patients treated with ICI but not with chemotherapy. Monitoring TGR changes after ICIs administration can help physician in deciding to treat beyond PD