A subset of non-small cell lung cancer patients treated with pemetrexed show 18f-fluorothymidine ‘flare’ on positron emission tomography

Thymidylate synthase (TS) remains a major target for cancer therapy. TS inhibition elicits increases in DNA salvage pathway activity, detected as a transient compensatory “flare” in 3′-deoxy-3′-[18F]fluorothymidine positron emission tomography (18F-FLT PET). We determined the magnitude of the 18F-FLT flare in non-small cell lung cancer (NSCLC) patients treated with the antifolate pemetrexed in relation to clinical outcome. Method: Twenty-one patients with advanced/metastatic non-small cell lung cancer (NSCLC) scheduled to receive palliative pemetrexed ± platinum-based chemotherapy underwent 18F-FLT PET at baseline and 4 h after initiating single-agent pemetrexed. Plasma deoxyuridine (dUrd) levels and thymidine kinase 1 (TK1) activity were measured before each scan. Patients were then treated with the combination therapy. The 18F-FLT PET variables were compared to RECIST 1.1 and overall survival (OS). Results: Nineteen patients had evaluable PET scans at both time points. A total of 32% (6/19) of patients showed 18F-FLT flares (>20% change in SUVmax-wsum). At the lesion level, only one patient had an FLT flare in all the lesions above (test–retest borders). The remaining had varied uptake. An 18F-FLT flare occurred in all lesions in 1 patient, while another patient had an 18F-FLT reduction in all lesions; 17 patients showed varied lesion uptake. All patients showed global TS inhibition reflected in plasma dUrd levels (p < 0.001) and 18F-FLT flares of TS-responsive normal tissues including small bowel and bone marrow (p = 0.004 each). Notably, 83% (5/6) of patients who exhibited 18F-FLT flares were also RECIST responders with a median OS of 31 m, unlike patients who did not exhibit 18F-FLT flares (15 m). Baseline plasma TK1 was prognostic of survival but its activity remained unchanged following treatment. Conclusions: The better radiological response and longer survival observed in patients with an 18F-FLT flare suggest the efficacy of the tracer as an indicator of the early therapeutic response to pemetrexed in NSCLC

Transformer Lesion Tracker

Evaluating lesion progression and treatment response via longitudinal lesion tracking plays a critical role in clinical practice. Automated approaches for this task are motivated by prohibitive labor costs and time consumption when lesion matching is done manually. Previous methods typically lack the integration of local and global information. In this work, we propose a transformer-based approach, termed Transformer Lesion Tracker (TLT). Specifically, we design a Cross Attention-based Transformer (CAT) to capture and combine both global and local information to enhance feature extraction. We also develop a Registration-based Anatomical Attention Module (RAAM) to introduce anatomical information to CAT so that it can focus on useful feature knowledge. A Sparse Selection Strategy (SSS) is presented for selecting features and reducing memory footprint in Transformer training. In addition, we use a global regression to further improve model performance. We conduct experiments on a public dataset to show the superiority of our method and find that our model performance has improved the average Euclidean center error by at least 14.3% (6mm vs. 7mm) compared with the state-of-the-art (SOTA). Code is available at https://github.com/TangWen920812/TLT.Comment: Accepted MICCAI 202

Advancements and Breakthroughs in Ultrasound Imaging

Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

Oncologic and functional outcomes after robot-assisted radical hysterectomy for cervical cancer

Background: Cervical cancer is the fourth most common cancer in women worldwide, but the incidence has rapidly declined in developed countries after the introduction of structured screening programs. This disease is caused by persistent HPV infection commonly acquired in adolescence, thereby affecting young women. In countries with established screening programs, early detection has resulted in favorable prognosis. Surgical treatment is the main treatment for early-stage disease and radical hysterectomy (RH) cures more than 90% of those afflicted. However, this treatment is associated with considerable morbidity and impaired quality of life (QoL). In 2005, robot-assisted laparoscopic radical hysterectomy (RRH), was introduced and subsequently implemented in Sweden. The perceived benefits of minimally invasive surgery (MIS), and of RRH in particular, have not been confirmed. It is therefore imperative to assess the efficacy and safety of this surgical technique, as well as short- and long-term adverse effects, particularly since long-term survival is expected. Aims: The overall aim was to investigate the oncologic safety of RRH. Secondary aims included assessment of surgical outcomes, health care costs and impact on QoL, bladder, bowel, sexual and lymphatic function after RRH. Methods: To assess the oncologic and surgical outcomes, two population-based studies were performed (Studies I and II). Study I included 304 women who underwent RH stage IA1-IIA during 2006-2015 at Karolinska University Hospital (KUH). Surgical and oncologic outcomes, as well as the costs of RRH and open radical hysterectomy (ORH) were compared. Study II, a nationwide cohort study, assessed overall and disease-free survival after RRH and ORH in 864 women with stage IA1-IB1 disease. The functional impact of RRH was investigated in two prospective clinical studies (Studies III and IV) with one-year follow-up. In Study III, 26 women undergoing RRH filled in a questionnaire regarding psychological well‐being and sexual, bowel, bladder, and lymphatic function. In addition, postoperative ovarian function was measured by change in sex hormones. In Study IV, 27 patient-reported outcomes after RRH were assessed using two validated questionnaires concerning bladder function and its impact on QoL. Outcomes were determined objectively by urodynamics and quantification of ablated autonomic nerves. Results: In the regional study (Study I), RRH was associated with an increased risk of recurrence (HR 2.13; 95% CI, 1.06-4.26). The postoperative complication rates (37%) and costs were similar, but the hospital stay was shorter than following ORH. The nationwide study (Study II) showed no statistical difference between RRH and ORH with respect to 5-year OS (HR 1.00; 95% CI, 0.50-2.01) and DFS (HR 1.08; 95% CI, 0.66-1.78). Study III demonstrated that RRH had a minor effect on sexual function, as well as bowel function. However, bladder impairment and lymphedema remained the main dysfunctions associated with RRH for cervical cancer (Studies III and IV). No correlation between the number of autonomous nerves ablated and functional outcomes was observed. In general, postoperative urinary symptoms diminished over time, but persisted in a substantial proportion of the women and may impair QoL. Conclusions: RRH appears to be safe once surgical proficiency is achieved. Prospective trials are needed to ensure the safety of RRH for cervical cancer. RRH was associated with less perioperative morbidity, and health care costs were similar to those of ORH. RRH seems to have only minor effects on sexual function, though bladder dysfunction remains a significant sequele. The cause of functional impairment after RRH is multifactorial and cannot be explained by nerve ablation alone

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors

Investigation of intra-tumour heterogeneity to identify texture features to characterise and quantify neoplastic lesions on imaging

The aim of this work was to further our knowledge of using imaging data to discover image derived biomarkers and other information about the imaged tumour. Using scans obtained from multiple centres to discover and validate the models has advanced earlier research and provided a platform for further larger centre prospective studies. This work consists of two major studies which are describe separately: STUDY 1: NSCLC Purpose The aim of this multi-center study was to discover and validate radiomics classifiers as image-derived biomarkers for risk stratification of non-small-cell lung cancer (NSCLC). Patients and methods Pre-therapy PET scans from 358 Stage I–III NSCLC patients scheduled for radical radiotherapy/chemoradiotherapy acquired between October 2008 and December 2013 were included in this seven-institution study. Using a semiautomatic threshold method to segment the primary tumors, radiomics predictive classifiers were derived from a training set of 133 scans using TexLAB v2. Least absolute shrinkage and selection operator (LASSO) regression analysis allowed data dimension reduction and radiomics feature vector (FV) discovery. Multivariable analysis was performed to establish the relationship between FV, stage and overall survival (OS). Performance of the optimal FV was tested in an independent validation set of 204 patients, and a further independent set of 21 (TESTI) patients. Results Of 358 patients, 249 died within the follow-up period [median 22 (range 0–85) months]. From each primary tumor, 665 three-dimensional radiomics features from each of seven gray levels were extracted. The most predictive feature vector discovered (FVX) was independent of known prognostic factors, such as stage and tumor volume, and of interest to multi-center studies, invariant to the type of PET/CT manufacturer. Using the median cut-off, FVX predicted a 14-month survival difference in the validation cohort (N = 204, p = 0.00465; HR = 1.61, 95% CI 1.16–2.24). In the TESTI cohort, a smaller cohort that presented with unusually poor survival of stage I cancers, FVX correctly indicated a lack of survival difference (N = 21, p = 0.501). In contrast to the radiomics classifier, clinically routine PET variables including SUVmax, SUVmean and SUVpeak lacked any prognostic information. Conclusion PET-based radiomics classifiers derived from routine pre-treatment imaging possess intrinsic prognostic information for risk stratification of NSCLC patients to radiotherapy/chemo-radiotherapy. STUDY 2: Ovarian Cancer Purpose The 5-year survival of epithelial ovarian cancer is approximately 35-40%, prompting the need to develop additional methods such as biomarkers for personalised treatment. Patient and Methods 657 texture features were extracted from the CT scans of 364 untreated EOC patients. A 4-texture feature ‘Radiomic Prognostic Vector (RPV)’ was developed using machine learning methods on the training set. Results The RPV was able to identify the 5% of patients with the worst prognosis, significantly improving established prognostic methods and was further validated in two independent, multi-centre cohorts. In addition, the genetic, transcriptomic and proteomic analysis from two independent datasets demonstrated that stromal and DNA damage response pathways are activated in RPV-stratified tumours. Conclusion RPV could be used to guide personalised therapy of EOC. Overall, the two large datasets of different imaging modalities have increased our knowledge of texture analysis, improving the models currently available and provided us with more areas with which to implement these tools in the clinical setting.Open Acces