Innovations in thoracic imaging:CT, radiomics, AI and x-ray velocimetry

In recent years, pulmonary imaging has seen enormous progress, with the introduction, validation and implementation of new hardware and software. There is a general trend from mere visual evaluation of radiological images to quantification of abnormalities and biomarkers, and assessment of 'non visual' markers that contribute to establishing diagnosis or prognosis. Important catalysts to these developments in thoracic imaging include new indications (like computed tomography [CT] lung cancer screening) and the COVID-19 pandemic. This review focuses on developments in CT, radiomics, artificial intelligence (AI) and x-ray velocimetry for imaging of the lungs. Recent developments in CT include the potential for ultra-low-dose CT imaging for lung nodules, and the advent of a new generation of CT systems based on photon-counting detector technology. Radiomics has demonstrated potential towards predictive and prognostic tasks particularly in lung cancer, previously not achievable by visual inspection by radiologists, exploiting high dimensional patterns (mostly texture related) on medical imaging data. Deep learning technology has revolutionized the field of AI and as a result, performance of AI algorithms is approaching human performance for an increasing number of specific tasks. X-ray velocimetry integrates x-ray (fluoroscopic) imaging with unique image processing to produce quantitative four dimensional measurement of lung tissue motion, and accurate calculations of lung ventilation

A Comparative Study of Automated Deep Learning Segmentation Models for Prostate MRI

Rodrigues, N. M., Silva, S., Vanneschi, L., & Papanikolaou, N. (2023). A Comparative Study of Automated Deep Learning Segmentation Models for Prostate MRI. Cancers, 15(5), 1-21. [1467]. https://doi.org/10.3390/cancers15051467 --- Funding: The research leading to these results has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952159 (ProCAncer-I). This work was partially supported by FCT, Portugal, through funding of the LASIGE Research Unit (UIDB/00408/2020 and UIDP/00408/2020), and under the project UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Nuno Rodrigues was supported by PhD Grant 2021/05322/BD.Prostate cancer is one of the most common forms of cancer globally, affecting roughly one in every eight men according to the American Cancer Society. Although the survival rate for prostate cancer is significantly high given the very high incidence rate, there is an urgent need to improve and develop new clinical aid systems to help detect and treat prostate cancer in a timely manner. In this retrospective study, our contributions are twofold: First, we perform a comparative unified study of different commonly used segmentation models for prostate gland and zone (peripheral and transition) segmentation. Second, we present and evaluate an additional research question regarding the effectiveness of using an object detector as a pre-processing step to aid in the segmentation process. We perform a thorough evaluation of the deep learning models on two public datasets, where one is used for cross-validation and the other as an external test set. Overall, the results reveal that the choice of model is relatively inconsequential, as the majority produce non-significantly different scores, apart from nnU-Net which consistently outperforms others, and that the models trained on data cropped by the object detector often generalize better, despite performing worse during cross-validation.publishersversionpublishe

Do temperature, relative humidity and interspecific competition alter the population size and the damage potential of stored-product insect pests? A hierarchical multilevel modeling approach

The premises of stored agricultural products and food consists of a complex ecosystem in which several pests can seriously affect the quality and quantity of the products. In this study we utilize a 4-level hierarchical linear multilevel model in order to assess the effect of temperature, relative humidity (RH) and interspecific competition on the population size and damage potential of the larger grain borer, Prostephanus truncatus (Horn) (Coleoptera: Bostrychidae) and the lesser grain borer, Rhyzopertha dominica (F.) (Coleoptera: Bostrychidae). As RH was increased, we observed higher percentage of live insects, while increased levels of temperature significantly decreased the percentage of live insects. The combination of R. dominica and P. truncatus lead to reduction of the percentages of live insects in comparison to single species treatments. However, P. truncatus is more damaging than R. dominica in maize, based on the proportion of damaged kernels which were infested by each insect species. We expect our results to have bearing in the management of these species

Challenges and Promises of Radiomics for Rectal Cancer

Moreira, J. M., Santiago, I., Santinha, J., Figueiredo, N., Marias, K., Figueiredo, M., ... Papanikolaou, N. (2019). Challenges and Promises of Radiomics for Rectal Cancer. Current Colorectal Cancer Reports, 15(6), 175-180. https://doi.org/10.1007/s11888-019-00446-yPurpose of Review: This literature review aims to gather the relevant works published on the topic of Radiomics in Rectal Cancer. Research on this topic has focused on finding predictors of rectal cancer staging and chemoradiation treatment response from medical images. The methods presented may, in principle, aid clinicians with the appropriate treatment planning options. Finding appropriate automatic tools to help in this task is very important, since rectal cancer has been considered one of the most challenging oncological pathologies in recent years. Recent Findings: Radiomics is a class of methods based on the extraction of mineable, high-dimensional data/features from the routine, standard-of-care medical imaging. This data is then fed to machine learning algorithms, with the goal of automatically obtaining predictions regarding disease stage and therapeutic response. Summary: The literature reviewed suggests that Radiomics will continue to be a part of the body of research in oncology in the upcoming years. However, and excluding very few studies, proper validation on the performance of the methods (mainly with external datasets) is still one of the main limitations of the field, which strongly limits their clinical applicability. Progress will only occur if the community opens itself to collaborate with different groups, as data availability and limited shareability continues to be the barrier for its development. Nowadays, Radiomics is used for nearly every type of cancer. In particular, for rectal cancer, the need for predicting treatment response will continue to demand and boost research in this field.authorsversionpublishe

(Quasi)-binomial vs. Gaussian models to evaluate thiamethoxam, pirimiphos-methyl, alpha-cypermethrin and deltamethrin on different types of storage bag materials against Ephestia kuehniella Zeller (Lepidoptera: Pyralidae) and Tribolium confusum Jacquelin du Val (Coleoptera: Tenebrionidae)

The Mediterranean flour moth, Ephestia kuehniella Zeller (Lepidoptera: Pyralidae) and the confused flour beetle, Tribolium confusum Jacquelin du Val (Coleoptera: Tenebrionidae) are worldwide spread and notorious organisms of numerous stored-products. Both species are dangerous for bagged commodities as penetrators and invaders. The aim of the current study was to examine the efficacy of thiamethoxam, pirimiphos-methyl, alpha-cypermethrin, and deltamethrin, against E. kuehniella and T. confusum larvae, on different types of storage bag materials, i.e., woven propylene, biaxially oriented polypropylene and kraft paper through a (quasi)-binomial modeling approach. The type of the tested storage bag material did not affect the mortality rates of both species when treated with the tested insecticides. Thiamethoxam and pirimiphos-methyl showed statistically significant higher mortality rates on E. kuehniella and T. confusum (beta coefficient = 0.141; p-value < 0.05) compared to alpha-cypermethrin and deltamethrin. In addition, T. confusum exhibited significantly higher mortality rate in comparison to E. kuehniella. Our results also showed that the tested doses and surface treatments had a significant effect on the mortality E. kuehniella and T. confusum larvae. Significantly higher mortality rates were recorded when larvae were exposed on bag materials having both surfaces treated or on the single treated surface than when they were exposed on the untreated surface. Our findings can be useful towards an effective management strategy against stored-product insect pests

Biological features and population growth of two Southeastern European Tribolium confusum Jacquelin du Val (Coleoptera: Tenebrionidae) strains

A study of the biological features and the potential population growth between two laboratory strains of the confused flour beetle, Tribolium confusum Jacquelin du Val (Coleoptera: Tenebrionidae) from Greece and Serbia is conducted on cracked barley and cracked white rice. The results show that, at a species level, T. confusum is able to complete development on cracked barley but not on cracked white rice. Therefore, cracked white rice proves to be an unsuitable commodity for T. confusum. Larval development on cracked barley is significantly shorter for the Serbian compared to the Greek strain (37.7 and 49.7 days, respectively), but pupal development does not differ between the two strains (6.2 days for both strains). Additionally, male longevity does not differ between the Greek and Serbian strains (144.4 and 151.4 days, respectively), while female longevity is significantly shorter for the Serbian (151.7 days) compared to the Greek strain (186.6 days). Fecundity does not differ between the two strains (11.3 and 17.7 eggs/female for the Greek and the Serbian strain, respectively), whilst survival is higher for the Serbian strain on both tested commodities. The values of the net reproductive rate, the intrinsic rate of increase and the finite rate of increase on cracked barley are significantly higher for the Serbian (7.27 females/female, 0.025 female/female/day and 1.026, respectively) compared to the Greek strain (2.91 females/female, 0.014 females/female/day and 1.014, respectively). It therefore is expected that different strains of T. confusum may exhibit variable phenology as well as potential population growth. Additionally, we expect our results to have bearing on the management of this species

Perfusion Magnetic Resonance as a Biomarker for Sorafenib-Treated Advanced Hepatocellular Carcinoma: A Pilot Study

Background: Sorafenib is the currently recommended therapy in patients with advanced hepatocellular carcinoma (HCC). Among the several biomarkers available for the evaluation of the therapeutic response and prognosis, there is perfusion magnetic resonance imaging (p-MRI) that, through measurement of the vascular permeability unit (ktrans), may retrieve useful information regarding the microvascular properties of focal liver lesions. The aim of this study was to evaluate the impact of sorafenib therapy in patients with advanced HCC using the p-MRI technique. Materials and Methods: In this retrospective study, 27 patients with the diagnosis of advanced HCC were included for palliative therapy using sorafenib. MRI of the liver was performed before the beginning of the oral therapy (T0), after 3 (T3), and after 6 months (T6). Dynamic acquisitions of the tumor (n = 50, during the first 2 min after contrast injection) were obtained in the coronal plane and were used to compute the parametric perfusion maps, acquiring the ktrans value using the extended Tofts pharmacokinetic model. Results: The value of ktrans obtained at T0 was significantly different from the value of ktrans obtained at T6 (p = 0.028). There were no significant differences between T0 and T3 (p = 0.115) or a correlation between ktrans at T0 and the size of the lesion (p = 0.376). The ktrans value at T0 in patients with progression-free survival (PFS) &#x3e; 6 months was not significantly different from the ktrans value in patients with PFS ≤6 months (p = 0.113). The ktrans value at T0 was not significantly different between patients who were previously submitted to chemoembolization and those who were not submitted (p = 0.587). Conclusion: In this pilot study, the ktrans value may serve as a biomarker of tumor response to antiangiogenic therapy, but only 6 months after its initiation. Clinical outcomes such as PFS were not predicted before the initiation of treatment

Breast cancer surgery with augmented reality

© 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).Introduction: Innovations in 3D spatial technology and augmented reality imaging driven by digital high-tech industrial science have accelerated experimental advances in breast cancer imaging and the development of medical procedures aimed to reduce invasiveness. Presentation of case: A 57-year-old post-menopausal woman presented with screen-detected left-sided breast cancer. After undergoing all staging and pre-operative studies the patient was proposed for conservative breast surgery with tumor localization. During surgery, an experimental digital and non-invasive intra-operative localization method with augmented reality was compared with the standard pre-operative localization with carbon tattooing (institutional protocol). The breast surgeon wearing an augmented reality headset (Hololens) was able to visualize the tumor location projection inside the patient's left breast in the usual supine position. Discussion: This work describes, to our knowledge, the first experimental test with a digital non-invasive method for intra-operative breast cancer localization using augmented reality to guide breast conservative surgery. In this case, a successful overlap of the previous standard pre-operative marks with carbon tattooing and tumor visualization inside the patient's breast with augmented reality was obtained. Conclusion: Breast cancer conservative guided surgery with augmented reality can pave the way for a digital non-invasive method for intra-operative tumor localization.info:eu-repo/semantics/publishedVersio

Corrigendum to “Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data” [Comput. Biol. Med. 17 (2024) 108216]

ProCAncer-I Consortium, Rodrigues, N. M., de Almeida, J. G., Castro Verde, A. S., Gaivão, A. M., Bilreiro, C., Santiago, I., Ip, J., Belião, S., Moreno, R., Matos, C., Vanneschi, L., Tsiknakis, M., Marias, K., Regge, D., Silva, S., & Papanikolaou, N. (2024). Corrigendum to “Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data” [Comput. Biol. Med. 17 (2024) 108216]. Computers in Biology and Medicine, 1. Article 108352. https://doi.org/10.1016/j.compbiomed.2024.108352The authors regret to inform that the affiliations of José Guilherme de Almeida and Ana Sofia Castro Verde are incorrect in the pdf. These authors are affiliated with institution aComputational Clinical Imaging Group Champalimaud Foundation Portugal. The authors would like to deeply apologise for any inconvenience caused.publishersversionpublishe

Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data

Rodrigues, N. M., Almeida, J. G. D., Verde, A. S. C., Gaivão, A. M., Bilreiro, C., Santiago, I., Ip, J., Belião, S., Moreno, R., Matos, C., Vanneschi, L., Tsiknakis, M., Marias, K., Regge, D., Silva, S., & Papanikolaou, N. (2024). Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data. Computers in Biology and Medicine, 171, 1-22. Article 108216. https://doi.org/10.1016/j.compbiomed.2024.108216 --- This work was partially supported by the Fundação para a Ciência e a Tecnologia, Portugal, through funding of the LASIGE Research Unit refs. UIDB/00408/2020 (https://doi.org/10.54499/UIDB/00408/2020), UIDP/00408/2020 (https://doi.org/10.54499/UIDP/00408/2020) and UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Nuno M. Rodrigues was supported by PhD Grant 2021/05322/BD. All authors except Nuno Rodrigues, Leonardo Vanneschi and Sara Silva, were supported by the European Union H2020: ProCAncer-I project (EU grant 952159)Despite being one of the most prevalent forms of cancer, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection and treatment. Computational methods can help make this detection process considerably faster and more robust. However, some modern machine-learning approaches require accurate segmentation of the prostate gland and the index lesion. Since performing manual segmentations is a very time-consuming task, and highly prone to inter-observer variability, there is a need to develop robust semi-automatic segmentation models. In this work, we leverage the large and highly diverse ProstateNet dataset, which includes 638 whole gland and 461 lesion segmentation masks, from 3 different scanner manufacturers provided by 14 institutions, in addition to other 3 independent public datasets, to train accurate and robust segmentation models for the whole prostate gland, zones and lesions. We show that models trained on large amounts of diverse data are better at generalizing to data from other institutions and obtained with other manufacturers, outperforming models trained on single-institution single-manufacturer datasets in all segmentation tasks. Furthermore, we show that lesion segmentation models trained on ProstateNet can be reliably used as lesion detection models.publishersversionpublishe