Convolutional Neural Network Model for Intestinal Metaplasia Recognition in Gastric Corpus Using Endoscopic Image Patches.

Gastric cancer (GC) is a significant healthcare concern, and the identification of high-risk patients is crucial. Indeed, gastric precancerous conditions present significant diagnostic challenges, particularly early intestinal metaplasia (IM) detection. This study developed a deep learning system to assist in IM detection using image patches from gastric corpus examined using virtual chromoendoscopy in a Western country. Utilizing a retrospective dataset of endoscopic images from Sant'Andrea University Hospital of Rome, collected between January 2020 and December 2023, the system extracted 200 × 200 pixel patches, classifying them with a voting scheme. The specificity and sensitivity on the patch test set were 76% and 72%, respectively. The optimization of a learnable voting scheme on a validation set achieved a specificity of 70% and sensitivity of 100% for entire images. Despite data limitations and the absence of pre-trained models, the system shows promising results for preliminary screening in gastric precancerous condition diagnostics, providing an explainable and robust Artificial Intelligence approach.

Role of radiomics in predicting lung cancer spread through air spaces in a heterogeneous dataset.

Background: Spread through air spaces (STAS) has been reported as a negative prognostic factor in patients with lung cancer undergoing sublobar resection. Radiomics has been recently proposed to predict STAS using preoperative computed tomography (CT). However, limitations of previous studies included the strict selection of imaging acquisition protocols, leading to results hardly applicable to daily clinical practice. The aim of this study is to test a radiomics-based prediction model of STAS in a practice-based dataset. Methods: A training cohort of 99 consecutive patients (65 STAS+ and 34 STAS-) with resected lung adenocarcinoma (ADC) was retrospectively collected. Preoperative CT images were collected from different centers regardless model and scanner manufacture, acquisition and reconstruction protocol, contrast phase and pixel size. Radiomics features were selected according to separation power and P value stability within different preprocessing setups and bootstrapping resampling. A prospective cohort of 50 patients (33 STAS+ and 17 STAS-) was enrolled for the external validation. Results: Only the five features with the highest stability were considered for the prediction model building. Radiomics, radiological and mixed radiomics-radiological prediction models were created, showing an accuracy of 0.66±0.02 after internal validation and reaching an accuracy of 0.78 in the external validation. Conclusions: Radiomics-based prediction models of STAS may be useful to properly plan surgical treatment and avoid oncological ineffective sublobar resections. This study supports a possible application of radiomics-based models on data with high variance in acquisition, reconstruction and preprocessing, opening a new chance for the use of radiomics in the prediction of STAS. Trial Registration: ClinicalTrials.gov identifier: NCT04893200.

Multimodal evaluation of 19F-BPA internalization in pancreatic cancer cells for boron capture and proton therapy potential applications.

PURPOSE: One of the obstacles to the application of Boron Neutron Capture Therapy (BNCT) and Proton Boron Fusion Therapy (PBFT) concerns the measurement of borated carriers' biodistribution. The objective of the present study was to evaluate the in vitro internalization of the 19F-labelled p-boronophenylalanine (19F-BPA) in the human cancer pancreatic cell line (PANC-1) for the potential application of BNCT and PBFT in pancreatic cancer. The 19F-BPA carrier has the advantage that its bio-distribution may be monitored in vivo using 19F-Nuclear Magnetic Resonance (19F NMR). MATERIALS AND METHODS: The 19F-BPA internalization in PANC-1 cells was evaluated using three independent techniques on cellular samples left in contact with growing medium enriched with 13.6 mM 19F-BPA corresponding to a 11B concentration of 120 ppm: neutron autoradiography, which quantifies boron; liquid chromatography hyphenated to tandem mass spectrometry and UV-Diode Array Detection (UV-DAD), which quantifies 19F-BPA molecule; and 19F NMR spectroscopy, which detects fluorine nuclei. RESULTS: Our studies suggested that 19F-BPA is internalized by PANC-1 cells. The three methods provided consistent results of about 50% internalization fraction at 120 ppm of 11B. Small variations (less than 15%) in internalization fraction are mainly dependent on the proliferation state of the cells. CONCLUSIONS: The ability of 19F NMR spectroscopy to study 19F-BPA internalization was validated by well-established independent techniques. The multimodal approach we used suggests 19F-BPA as a promising BNCT/PBFT carrier for the treatment of pancreatic cancer. Since the quantification is performed at doses useful for BNCT/PBFT, 19F NMR can be envisaged to monitor 19F-BPA bio-distribution during the therapy.