Highlights lecture EANM 2016: “Embracing molecular imaging and multi-modal imaging: a smart move for nuclear medicine towards personalized medicine”

Aboagye, Eric O.; Kraeber-Bodéré, Françoise

doi:10.1007/s00259-017-3704-6

Cited by 20 publications

(13 citation statements)

References 30 publications

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“…This leads to healthcare approaches that help improve patient comfort and reduce healthcare costs as part of a fully patient-centric, personalized medicine [40] (Figure 5). Hybrid imaging data, as a major constituent of this process, plays a central role in personalized medicine [41].…”

Section: Machine Learning For Medical Big Data Analysismentioning

confidence: 99%

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

et al. 2018

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Medical imaging has evolved from a pure visualization tool to representing a primary source of analytic approaches toward in vivo disease characterization. Hybrid imaging is an integral part of this approach, as it provides complementary visual and quantitative information in the form of morphological and functional insights into the living body. As such, non-invasive imaging modalities no longer provide images only, but data, as stated recently by pioneers in the field. Today, such information, together with other, non-imaging medical data creates highly heterogeneous data sets that underpin the concept of medical big data. While the exponential growth of medical big data challenges their processing, they inherently contain information that benefits a patient-centric personalized healthcare. Novel machine learning approaches combined with high-performance distributed cloud computing technologies help explore medical big data. Such exploration and subsequent generation of knowledge require a profound understanding of the technical challenges. These challenges increase in complexity when employing hybrid, aka dual-or even multi-modality image data as input to big data repositories. This paper provides a general insight into medical big data analysis in light of the use of hybrid imaging information. First, hybrid imaging is introduced (see further contributions to this special Research Topic), also in the context of medical big data, then the technological background of machine learning as well as state-of-the-art distributed cloud computing technologies are presented, followed by the discussion of data preservation and data sharing trends. Joint data exploration endeavors in the context of in vivo radiomics and hybrid imaging will be presented. Standardization challenges of imaging protocol, delineation, feature engineering, and machine learning evaluation will be detailed. Last, the paper will provide an outlook into the future role of hybrid imaging in view of personalized medicine, whereby a focus will be given to the derivation of prediction models as part of clinical decision support systems, to which machine learning approaches and hybrid imaging can be anchored.

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Section: Machine Learning For Medical Big Data Analysismentioning

confidence: 99%

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

et al. 2018

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“…In another study of ours, the hosts' metabolic characteristics underwent nuclear magnetic resonance (NMR) analysis to evaluate the drainage resistance phenomenon of KPLA, and five key metabolites were identified which might be potential targets for guiding novel therapeutics [14]. This indicates that the occurrence and progress of a liver abscess could be further explored using functional imaging methods that can reflect metabolic characteristics, such as magnetic resonance spectroscopy and molecular imaging [32].…”

Section: Discussionmentioning

confidence: 99%

The increased recurrence rate of liver abscess caused by extended-spectrum β-lactamase-producing Klebsiella pneumoniae

Wang

Ren

Chang

et al. 2020

Eur J Clin Microbiol Infect Dis

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Background The pathogenic bacterium Klebsiella pneumoniae (KP) is the major causative agent of pyogenic liver abscess (PLA). But reports about the prognosis of KP-caused PLA (KPLA) are rare. This study aimed to ascertain the recurrence rate of KPLA after initial treatment, and its contributing factors. Methods The medical records data were retrospectively analyzed of KPLA patients who were admitted to Shengjing Hospital of China Medical University from January 2012 to January 2018. According to whether or not there was recurrence of KPLA during follow-up, the patients were divided into a 'recurrence' and a 'non-recurrence' group. The clinical and CT characteristics of patients were compared between the two groups, and those factors related to KPLA recurrence were further analyzed. Results A total of 110 patients who had first-time episodes of KPLA were included into the study. The average follow-up time was 3.65±2.18 years. Twenty (18.18%) KPLA patients experienced recurrence. Those in the recurrence group had a significantly greater incidence of extended-spectrum βlactamase (ESBL) production compared to the non-recurrence group (30.0% vs 8.89%, P=0.018). Diabetes, biliary tract disease, and history of malignancy was not associated with recurrence (all P>0.05). No difference in the CT characteristics of KPLA (including abscess size, location, whether multilocular, gas production of KPLA, and thrombophlebitis) was found between the two groups. Multivariate regression analysis showed that ESBL production (OR, 6.3; 95% CI, 1.02-38.59; P=0.04) was an independent risk factor for the recurrence of KPLA. Conclusions KPLA has a high recurrence rate, and ESBL production is a risk factor for recurrent KPLA.

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“…Cystic renal cell carcinoma is a general term for cystic space-occupying lesions of the kidney, which is separated into four subtypes, i.e., the monolocular, multilocular, cystic necrosis, and cyst epithelial-derived type (12). However, some scholars only separate it into polycystic and unicapsular kidney cancers (13).…”

Section: Discussionmentioning

confidence: 99%

Diagnostic value and clinical significance of ultrasound combined with CT in cystic renal cell carcinoma

et al. 2019

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This study investigated the value and clinical significance of ultrasound combined with CT in the diagnosis of cystic renal cell carcinoma. A total of 85 patients with cystic renal cell carcinoma, who were admitted to the Oncology Department of Yantai Yuhuangding Hospital Affiliated to Qingdao University from December 2015 to April 2017, were selected as the study group, and 70 patients with benign renal cyst, who were examined in Yantai Yuhuangding Hospital Affiliated to Qingdao University, were selected as the benign group. The patients in the two groups were examined by ultrasound and CT. The diagnostic value of ultrasound, CT, and ultrasound combined with CT in cystic renal cell carcinoma was analyzed. The sensitivity of ultrasound combined with CT was significantly higher than that of CT and ultrasound (P<0.05). The specificity and diagnostic coincidence rate of ultrasound combined with CT were significantly higher than those of CT (P<0.05). For unicapsular kidney cancer, there was no significant difference among ultrasound, CT and ultrasound combined with CT in the diagnosis of septum and wall nodule (P>0.05). For polycystic kidney cancer, there was no significant difference among ultrasound, CT and ultrasound combined with CT in the diagnosis of the presence or absence of septum (P>0.05). Ultrasound was significantly better than CT in cyst wall confounding (P<0.05). Ultrasound combined with CT was significantly better than ultrasound in calcification and blood supply of tumors (P<0.05). In conclusion, the accuracy of ultrasound combined with CT is higher than that of ultrasound or that of CT in the diagnosis of cystic renal cell carcinoma, which can be beneficial in accurately carrying out clinical diagnosis, reduce the incidence of missed diagnosis and misdiagnosis caused by a single diagnosis and treatment. Ultrasound combined with CT is good for clinical screening and can guide clinical symptomatic treatment, which is worthy of generalizing in clinic.

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Highlights lecture EANM 2016: “Embracing molecular imaging and multi-modal imaging: a smart move for nuclear medicine towards personalized medicine”

Cited by 20 publications

References 30 publications

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

Personalizing Medicine Through Hybrid Imaging and Medical Big Data Analysis

The increased recurrence rate of liver abscess caused by extended-spectrum β-lactamase-producing Klebsiella pneumoniae

Diagnostic value and clinical significance of ultrasound combined with CT in cystic renal cell carcinoma

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