Evaluation of radiological and clinical efficacy of ^{90}Y-DOTATATE} therapy in patients with progressive metastatic midgut neuroendocrine carcinomas

Background: To evaluate the radiological and clinical therapeutic effectiveness of ^{90}Y-octreotate [DOTATATE] inpatients with progressive somatostatin receptor-positive midgut neuroendocrine carcinomas (GEPNETs). Material/Methods: The study group: 34 patients, with histological proven extensive non-resectable and progressive midgut GEP-NETs. Radionuclide therapy (^{90}Y-DOTATATE) was given i.v. with a mean activity per administration 3,82 GBq. Initial clinical tumor responses were assessed 6-7 weeks after therapy completion and then once 3-monthly. The objective tumor response was classified according to the RECIST, initially between 4-6 months and then after each of the 6 months interval. Results: At 6 months after treatment completion, radiological tumor response was observed in 6 subjects with PR (19%), 25 presented SD (78%) and single had PD (3%). Overall clinical response to therapy at 6 months follow-up was observed in 23 patients (68%), SD in 5 patients (15%) and PD in 6 (18%). A year after therapy radiological tumour response was seen in 11 patients (44%), SD had 12 subjects (44%) and DP was noted in 2 patients. Two years after completed therapy PR was seen in 6 patients (33%), SD in additional 11 subjects (61%), single patient had PD. Clinical response to treatment in terms of PR and SD were noted in 22 patients (88%) after 1 year and in 14 patients (87%) after 2 years. Median PFS was 20 months, while the median OS was 23 months. In the 6 patients with clinical PD within initial 6 months the median PFS was 6 months and OS 11 months, while in those with SD or PR PFS was 22 months and OS 26 months (P<0.05). Conclusions: Therapy with ^{90}Y-DOTATATE} is effective in terms of clinical response, however the radiological response measured by the RECIST criteria underestimates benefits of this type of therapy in patients with progressive somatostatin receptor-positive midgut neuroendocrine carcinomas

A Comparative Study for 2D and 3D Computer-aided Diagnosis Methods for Solitary Pulmonary Nodules

Many computer-aided diagnosis (CAD) methods, including 2D and 3D approaches, have been proposed for solitary pulmonary nodules (SPNs). However, the detection and diagnosis of SPNs remain challenging in many clinical circumstances. One goal of this work is to investigate the relative diagnostic accuracy of 2D and 3D methods. An additional goal is to develop a two-stage approach that combines the simplicity of 2D and the accuracy of 3D methods. The experimental results show statistically significant differences between the diagnostic accuracy of 2D and 3D methods. The results also show that with a very minor drop in diagnostic performance the two-stage approach can significantly reduce the number of nodules needed to be processed by the 3D method, streamlining the computational demand

Towards automatic pulmonary nodule management in lung cancer screening with deep learning

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.Comment: Published on Scientific Report

Lung_PAYNet: a pyramidal attention based deep learning network for lung nodule segmentation

Accurate and reliable lung nodule segmentation in computed tomography (CT) images is required for early diagnosis of lung cancer. Some of the difficulties in detecting lung nodules include the various types and shapes of lung nodules, lung nodules near other lung structures, and similar visual aspects. This study proposes a new model named Lung_PAYNet, a pyramidal attention-based architecture, for improved lung nodule segmentation in low-dose CT images. In this architecture, the encoder and decoder are designed using an inverted residual block and swish activation function. It also employs a feature pyramid attention network between the encoder and decoder to extract exact dense features for pixel classification. The proposed architecture was compared to the existing UNet architecture, and the proposed methodology yielded significant results. The proposed model was comprehensively trained and validated using the LIDC-IDRI dataset available in the public domain. The experimental results revealed that the Lung_PAYNet delivered remarkable segmentation with a Dice similarity coefficient of 95.7%, mIOU of 91.75%, sensitivity of 92.57%, and precision of 96.75%

Tumor Volume as an Alternative Response Measurement for Imatinib Treated GIST Patients

Background: Assessment of tumor size changes is crucial in clinical trials and patient care. We compared imatinib-induced volume changes of liver metastases (LM) from gastro-intestinal stromal tumors (GIST) to RECIST and Choi criteria and their association with overall survival (OS). Methods: LM from 84 GIST p