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1.
Eur Respir J ; 2020 Jul 02.
Artigo em Inglês | MEDLINE | ID: mdl-32616597

RESUMO

BACKGROUND: The outbreak of the coronavirus disease 2019 (COVID-19) has globally strained medical resources and caused significant mortality. OBJECTIVE: To develop and validate machine-learning model based on clinical features for severity risk assessment and triage for COVID-19 patients at hospital admission. METHOD: 725 patients were used to train and validate the model including a retrospective cohort of 299 hospitalised COVID-19 patients at Wuhan, China, from December 23, 2019, to February 13, 2020, and five cohorts with 426 patients from eight centers in China, Italy, and Belgium, from February 20, 2020, to March 21, 2020. The main outcome was the onset of severe or critical illness during hospitalisation. Model performances were quantified using the area under the receiver operating characteristic curve (AUC) and metrics derived from the confusion-matrix. RESULTS: The median age was 50.0 years and 137 (45.8%) were men in the retrospective cohort. The median age was 62.0 years and 236 (55.4%) were men in five cohorts. The model was prospectively validated on five cohorts yielding AUCs ranging from 0.84 to 0.89, with accuracies ranging from 74.4% to 87.5%, sensitivities ranging from 75.0% to 96.9%, and specificities ranging from 57.5% to 88.0%, all of which performed better than the pneumonia severity index. The cut-off values of the low, medium, and high-risk probabilities were 0.21 and 0.80. The online-calculators can be found at www.covid19risk.ai. CONCLUSION: The machine-learning model, nomogram, and online-calculator might be useful to access the onset of severe and critical illness among COVID-19 patients and triage at hospital admission.

2.
BMC Cancer ; 20(1): 557, 2020 Jun 15.
Artigo em Inglês | MEDLINE | ID: mdl-32539805

RESUMO

BACKGROUND: About 50% of non-small cell lung cancer (NSCLC) patients have metastatic disease at initial diagnosis, which limits their treatment options and, consequently, the 5-year survival rate (15%). Immune checkpoint inhibitors (ICI), either alone or in combination with chemotherapy, have become standard of care (SOC) for most good performance status patients. However, most patients will not obtain long-term benefit and new treatment strategies are therefore needed. We previously demonstrated clinical safety of the tumour-selective immunocytokine L19-IL2, consisting of the anti-ED-B scFv L19 antibody coupled to IL2, combined with stereotactic ablative radiotherapy (SABR). METHODS: This investigator-initiated, multicentric, randomised controlled open-label phase II clinical trial will test the hypothesis that the combination of SABR and L19-IL2 increases progression free survival (PFS) in patients with limited metastatic NSCLC. One hundred twenty-six patients will be stratified according to their metastatic load (oligo-metastatic: ≤5 or poly-metastatic: 6 to 10) and randomised to the experimental-arm (E-arm) or the control-arm (C-arm). The C-arm will receive SOC, according to the local protocol. E-arm oligo-metastatic patients will receive SABR to all lesions followed by L19-IL2 therapy; radiotherapy for poly-metastatic patients consists of irradiation of one (symptomatic) to a maximum of 5 lesions (including ICI in both arms if this is the SOC). The accrual period will be 2.5-years, starting after the first centre is initiated and active. Primary endpoint is PFS at 1.5-years based on blinded radiological review, and secondary endpoints are overall survival, toxicity, quality of life and abscopal response. Associative biomarker studies, immune monitoring, CT-based radiomics, stool collection, iRECIST and tumour growth rate will be performed. DISCUSSION: The combination of SABR with or without ICI and the immunocytokine L19-IL2 will be tested as 1st, 2nd or 3rd line treatment in stage IV NSCLC patients in 14 centres located in 6 countries. This bimodal and trimodal treatment approach is based on the direct cytotoxic effect of radiotherapy, the tumour selective immunocytokine L19-IL2, the abscopal effect observed distant from the irradiated metastatic site(s) and the memory effect. The first results are expected end 2023. TRIAL REGISTRATION: ImmunoSABR Protocol Code: NL67629.068.18; EudraCT: 2018-002583-11; Clinicaltrials.gov: NCT03705403; ISRCTN ID: ISRCTN49817477; Date of registration: 03-April-2019.

3.
Mol Oncol ; 14(7): 1555-1568, 2020 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-32386436

RESUMO

The extracellular matrix protein fibronectin contains a domain that is rarely found in healthy adults and is almost exclusively expressed by newly formed blood vessels in tumours, particularly in solid tumours, different types of lymphoma and some leukaemias. This domain, called the extra domain B (ED-B), thus has broad therapeutic potential. The antibody L19 has been developed to specifically target ED-B and has shown therapeutic potential when combined with cytokines, such as IL-2. In this review article, we discuss the preclinical research and clinical trials that highlight the potential of ED-B targeting for the imaging and treatment of various types of cancer. ED-B-centred studies also highlight how proper patient stratification is of utmost importance for the successful implementation of novel antibody-based targeted therapies.

4.
PLoS One ; 15(5): e0232639, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-32442178

RESUMO

INTRODUCTION: In this study, we investigate the role of radiomics for prediction of overall survival (OS), locoregional recurrence (LRR) and distant metastases (DM) in stage III and IV HNSCC patients treated by chemoradiotherapy. We hypothesize that radiomic analysis of (peri-)tumoral tissue may detect invasion of surrounding tissues indicating a higher chance of locoregional recurrence and distant metastasis. METHODS: Two comprehensive data sources were used: the Dutch Cancer Society Database (Alp 7072, DESIGN) and "Big Data To Decide" (BD2Decide). The gross tumor volumes (GTV) were delineated on contrast-enhanced CT. Radiomic features were extracted using the RadiomiX Discovery Toolbox (OncoRadiomics, Liege, Belgium). Clinical patient features such as age, gender, performance status etc. were collected. Two machine learning methods were chosen for their ability to handle censored data: Cox proportional hazards regression and random survival forest (RSF). Multivariable clinical and radiomic Cox/ RSF models were generated based on significance in univariable cox regression/ RSF analyses on the held out data in the training dataset. Features were selected according to a decreasing hazard ratio for Cox and relative importance for RSF. RESULTS: A total of 444 patients with radiotherapy planning CT-scans were included in this study: 301 head and neck squamous cell carcinoma (HNSCC) patients in the training cohort (DESIGN) and 143 patients in the validation cohort (BD2DECIDE). We found that the highest performing model was a clinical model that was able to predict distant metastasis in oropharyngeal cancer cases with an external validation C-index of 0.74 and 0.65 with the RSF and Cox models respectively. Peritumoral radiomics based prediction models performed poorly in the external validation, with C-index values ranging from 0.32 to 0.61 utilizing both feature selection and model generation methods. CONCLUSION: Our results suggest that radiomic features from the peritumoral regions are not useful for the prediction of time to OS, LR and DM.

5.
Molecules ; 25(10)2020 May 18.
Artigo em Inglês | MEDLINE | ID: mdl-32443462

RESUMO

Hypoxia, a common feature of solid tumours' microenvironment, is associated with an aggressive phenotype and is known to cause resistance to anticancer chemo- and radiotherapies. Tumour-associated carbonic anhydrases isoform IX (hCA IX), which is upregulated under hypoxia in many malignancies participating to the microenvironment acidosis, represents a valuable target for drug strategy against advanced solid tumours. To overcome cancer cell resistance and improve the efficacy of therapeutics, the use of bio-reducible prodrugs also known as Hypoxia-activated prodrugs (HAPs), represents an interesting strategy to be applied to target hCA IX isozyme through the design of selective carbonic anhydrase IX inhibitors (CAIs). Here, we report the design, synthesis and biological evaluations including CA inhibition assays, toxicity assays on zebrafish and viability assays on human cell lines (HT29 and HCT116) of new HAP-CAIs, harboring different bio-reducible moieties in nitroaromatic series and a benzenesulfonamide warhead to target hCA IX. The CA inhibition assays of this compound series showed a slight selectivity against hCA IX versus the cytosolic off-target hCA II and hCA I isozymes. Toxicity and viability assays have highlighted that the compound bearing the 2-nitroimidazole moiety possesses the lowest toxicity (LC50 of 1400 µM) and shows interesting results on viability assays.

6.
Semin Radiat Oncol ; 30(2): 187-193, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-32381298

RESUMO

There is now strong clinical and preclinical evidence that lymphocytes, for example, CD8+ T cells, are key effectors of immunotherapy and that irradiation of large blood vessels, the heart, and lymphoid organs (including nodes, spleen, bones containing bone marrow, and thymus in children) causes transient or persistent lymphopenia. Furthermore, there is extensive clinical evidence, across multiple cancer sites and treatment modalities, that lymphopenia correlates strongly with decreased overall survival. At the moment, we lack quantitative evidence to establish the relationship between dose-volume and dose-rate to critical normal structures and lymphopenia. Therefore, we propose that data should be systematically recorded to characterise a possible quantitative relationship. This might enable us to improve the efficacy of radiotherapy and develop strategies to predict and prevent treatment-related lymphopenia. In anticipation of more quantitative data, we recommend the application of the principle of As Low As Reasonably Achievable to lymphocyte-rich regions for radiotherapy treatment planning to reduce the radiation doses to these structures, thus moving toward "Lymphocyte-Sparing Radiotherapy."

7.
Sci Rep ; 10(1): 4542, 2020 Mar 11.
Artigo em Inglês | MEDLINE | ID: mdl-32161279

RESUMO

A major challenge in radiomics is assembling data from multiple centers. Sharing data between hospitals is restricted by legal and ethical regulations. Distributed learning is a technique, enabling training models on multicenter data without data leaving the hospitals ("privacy-preserving" distributed learning). This study tested feasibility of distributed learning of radiomics data for prediction of two year overall survival and HPV status in head and neck cancer (HNC) patients. Pretreatment CT images were collected from 1174 HNC patients in 6 different cohorts. 981 radiomic features were extracted using Z-Rad software implementation. Hierarchical clustering was performed to preselect features. Classification was done using logistic regression. In the validation dataset, the receiver operating characteristics (ROC) were compared between the models trained in the centralized and distributed manner. No difference in ROC was observed with respect to feature selection. The logistic regression coefficients were identical between the methods (absolute difference <10-7). In comparison of the full workflow (feature selection and classification), no significant difference in ROC was found between centralized and distributed models for both studied endpoints (DeLong p > 0.05). In conclusion, both feature selection and classification are feasible in a distributed manner using radiomics data, which opens new possibility for training more reliable radiomics models.

8.
JCO Clin Cancer Inform ; 4: 184-200, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-32134684

RESUMO

Big data for health care is one of the potential solutions to deal with the numerous challenges of health care, such as rising cost, aging population, precision medicine, universal health coverage, and the increase of noncommunicable diseases. However, data centralization for big data raises privacy and regulatory concerns.Covered topics include (1) an introduction to privacy of patient data and distributed learning as a potential solution to preserving these data, a description of the legal context for patient data research, and a definition of machine/deep learning concepts; (2) a presentation of the adopted review protocol; (3) a presentation of the search results; and (4) a discussion of the findings, limitations of the review, and future perspectives.Distributed learning from federated databases makes data centralization unnecessary. Distributed algorithms iteratively analyze separate databases, essentially sharing research questions and answers between databases instead of sharing the data. In other words, one can learn from separate and isolated datasets without patient data ever leaving the individual clinical institutes.Distributed learning promises great potential to facilitate big data for medical application, in particular for international consortiums. Our purpose is to review the major implementations of distributed learning in health care.

9.
Radiology ; 295(2): 328-338, 2020 May.
Artigo em Inglês | MEDLINE | ID: mdl-32154773

RESUMO

Background Radiomic features may quantify characteristics present in medical imaging. However, the lack of standardized definitions and validated reference values have hampered clinical use. Purpose To standardize a set of 174 radiomic features. Materials and Methods Radiomic features were assessed in three phases. In phase I, 487 features were derived from the basic set of 174 features. Twenty-five research teams with unique radiomics software implementations computed feature values directly from a digital phantom, without any additional image processing. In phase II, 15 teams computed values for 1347 derived features using a CT image of a patient with lung cancer and predefined image processing configurations. In both phases, consensus among the teams on the validity of tentative reference values was measured through the frequency of the modal value and classified as follows: less than three matches, weak; three to five matches, moderate; six to nine matches, strong; 10 or more matches, very strong. In the final phase (phase III), a public data set of multimodality images (CT, fluorine 18 fluorodeoxyglucose PET, and T1-weighted MRI) from 51 patients with soft-tissue sarcoma was used to prospectively assess reproducibility of standardized features. Results Consensus on reference values was initially weak for 232 of 302 features (76.8%) at phase I and 703 of 1075 features (65.4%) at phase II. At the final iteration, weak consensus remained for only two of 487 features (0.4%) at phase I and 19 of 1347 features (1.4%) at phase II. Strong or better consensus was achieved for 463 of 487 features (95.1%) at phase I and 1220 of 1347 features (90.6%) at phase II. Overall, 169 of 174 features were standardized in the first two phases. In the final validation phase (phase III), most of the 169 standardized features could be excellently reproduced (166 with CT; 164 with PET; and 164 with MRI). Conclusion A set of 169 radiomics features was standardized, which enabled verification and calibration of different radiomics software. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Kuhl and Truhn in this issue.

10.
Eur Radiol ; 30(5): 2680-2691, 2020 May.
Artigo em Inglês | MEDLINE | ID: mdl-32006165

RESUMO

OBJECTIVES: Develop a CT-based radiomics model and combine it with frozen section (FS) and clinical data to distinguish invasive adenocarcinomas (IA) from preinvasive lesions/minimally invasive adenocarcinomas (PM). METHODS: This multicenter study cohort of 623 lung adenocarcinomas was split into training (n = 331), testing (n = 143), and external validation dataset (n = 149). Random forest models were built using selected radiomics features, results from FS, lesion volume, clinical and semantic features, and combinations thereof. The area under the receiver operator characteristic curves (AUC) was used to evaluate model performances. The diagnosis accuracy, calibration, and decision curves of models were tested. RESULTS: The radiomics-based model shows good predictive performance and diagnostic accuracy for distinguishing IA from PM, with AUCs of 0.89, 0.89, and 0.88, in the training, testing, and validation datasets, respectively, and with corresponding accuracies of 0.82, 0.79, and 0.85. Adding lesion volume and FS significantly increases the performance of the model with AUCs of 0.96, 0.97, and 0.96, and with accuracies of 0.91, 0.94, and 0.93 in the three datasets. There is no significant difference in AUC between the FS model enriched with radiomics and volume against an FS model enriched with volume alone, while the former has higher accuracy. The model combining all available information shows minor non-significant improvements in AUC and accuracy compared with an FS model enriched with radiomics and volume. CONCLUSIONS: Radiomics signatures are potential biomarkers for the risk of IA, especially in combination with FS, and could help guide surgical strategy for pulmonary nodules patients. KEY POINTS: • A CT-based radiomics model may be a valuable tool for preoperative prediction of invasive adenocarcinoma for patients with pulmonary nodules. • Radiomics combined with frozen sections could help in guiding surgery strategy for patients with pulmonary nodules.

12.
Br J Radiol ; 93(1108): 20190948, 2020 Apr.
Artigo em Inglês | MEDLINE | ID: mdl-32101448

RESUMO

Historically, medical imaging has been a qualitative or semi-quantitative modality. It is difficult to quantify what can be seen in an image, and to turn it into valuable predictive outcomes. As a result of advances in both computational hardware and machine learning algorithms, computers are making great strides in obtaining quantitative information from imaging and correlating it with outcomes. Radiomics, in its two forms "handcrafted and deep," is an emerging field that translates medical images into quantitative data to yield biological information and enable radiologic phenotypic profiling for diagnosis, theragnosis, decision support, and monitoring. Handcrafted radiomics is a multistage process in which features based on shape, pixel intensities, and texture are extracted from radiographs. Within this review, we describe the steps: starting with quantitative imaging data, how it can be extracted, how to correlate it with clinical and biological outcomes, resulting in models that can be used to make predictions, such as survival, or for detection and classification used in diagnostics. The application of deep learning, the second arm of radiomics, and its place in the radiomics workflow is discussed, along with its advantages and disadvantages. To better illustrate the technologies being used, we provide real-world clinical applications of radiomics in oncology, showcasing research on the applications of radiomics, as well as covering its limitations and its future direction.


Assuntos
Aprendizado Profundo/tendências , Diagnóstico por Imagem/tendências , Processamento de Imagem Assistida por Computador/tendências , Tecnologia Radiológica/tendências , Neoplasias Encefálicas/diagnóstico por imagem , Neoplasias da Mama/diagnóstico por imagem , Diagnóstico por Imagem/métodos , Feminino , Previsões , Humanos , Processamento de Imagem Assistida por Computador/métodos , Neoplasias Pulmonares/diagnóstico por imagem , Masculino , Radiografia/métodos , Tecnologia Radiológica/métodos , Fluxo de Trabalho
13.
Acta Orthop ; 91(2): 215-220, 2020 04.
Artigo em Inglês | MEDLINE | ID: mdl-31928116

RESUMO

Artificial intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI, particularly deep learning, has recently made substantial strides in perception tasks allowing machines to better represent and interpret complex data. Deep learning is a subset of AI represented by the combination of artificial neuron layers. In the last years, deep learning has gained great momentum. In the field of orthopaedics and traumatology, some studies have been done using deep learning to detect fractures in radiographs. Deep learning studies to detect and classify fractures on computed tomography (CT) scans are even more limited. In this narrative review, we provide a brief overview of deep learning technology: we (1) describe the ways in which deep learning until now has been applied to fracture detection on radiographs and CT examinations; (2) discuss what value deep learning offers to this field; and finally (3) comment on future directions of this technology.


Assuntos
Aprendizado Profundo , Fraturas Ósseas/diagnóstico por imagem , Humanos , Interpretação de Imagem Radiográfica Assistida por Computador/métodos , Radiografia , Tomografia Computadorizada por Raios X
14.
Respiration ; 99(2): 99-107, 2020.
Artigo em Inglês | MEDLINE | ID: mdl-31991420

RESUMO

Medical imaging plays a key role in evaluating and monitoring lung diseases such as chronic obstructive pulmonary disease (COPD) and lung cancer. The application of artificial intelligence in medical imaging has transformed medical images into mineable data, by extracting and correlating quantitative imaging features with patients' outcomes and tumor phenotype - a process termed radiomics. While this process has already been widely researched in lung oncology, the evaluation of COPD in this fashion remains in its infancy. Here we outline the main applications of radiomics in lung cancer and briefly review the workflow from image acquisition to the evaluation of model performance. Finally, we discuss the current assessments of COPD and the potential application of radiomics in COPD.

15.
Radiother Oncol ; 144: 189-200, 2020 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-31911366

RESUMO

BACKGROUND AND PURPOSE: Access to healthcare data is indispensable for scientific progress and innovation. Sharing healthcare data is time-consuming and notoriously difficult due to privacy and regulatory concerns. The Personal Health Train (PHT) provides a privacy-by-design infrastructure connecting FAIR (Findable, Accessible, Interoperable, Reusable) data sources and allows distributed data analysis and machine learning. Patient data never leaves a healthcare institute. MATERIALS AND METHODS: Lung cancer patient-specific databases (tumor staging and post-treatment survival information) of oncology departments were translated according to a FAIR data model and stored locally in a graph database. Software was installed locally to enable deployment of distributed machine learning algorithms via a central server. Algorithms (MATLAB, code and documentation publicly available) are patient privacy-preserving as only summary statistics and regression coefficients are exchanged with the central server. A logistic regression model to predict post-treatment two-year survival was trained and evaluated by receiver operating characteristic curves (ROC), root mean square prediction error (RMSE) and calibration plots. RESULTS: In 4 months, we connected databases with 23 203 patient cases across 8 healthcare institutes in 5 countries (Amsterdam, Cardiff, Maastricht, Manchester, Nijmegen, Rome, Rotterdam, Shanghai) using the PHT. Summary statistics were computed across databases. A distributed logistic regression model predicting post-treatment two-year survival was trained on 14 810 patients treated between 1978 and 2011 and validated on 8 393 patients treated between 2012 and 2015. CONCLUSION: The PHT infrastructure demonstrably overcomes patient privacy barriers to healthcare data sharing and enables fast data analyses across multiple institutes from different countries with different regulatory regimens. This infrastructure promotes global evidence-based medicine while prioritizing patient privacy.

16.
J Enzyme Inhib Med Chem ; 35(1): 109-117, 2020 Dec.
Artigo em Inglês | MEDLINE | ID: mdl-31687859

RESUMO

With the aim to obtain novel compounds possessing both strong affinity against human carbonic anhydrases and low toxicity, we synthesised novel thiourea and sulphonamide derivatives 3, 4 and 10, and studied their in vitro inhibitory properties against human CA I, CA II and CA IX. We also evaluated the toxicity of these compounds using zebrafish larvae. Among the three compounds, derivative 4 showed efficient inhibition against hCA II (KI = 58.6 nM). Compound 10 showed moderate inhibition against hCA II (KI = 199.2 nM) and hCA IX (KI = 147.3 nM), whereas it inhibited hCA I less weakly at micromolar concentrations (KI = 6428.4 nM). All other inhibition constants for these compounds were in the submicromolar range. The toxicity evaluation studies showed no adverse effects on the zebrafish larvae. Our study suggests that these compounds are suitable for further preclinical characterisation as potential inhibitors of hCA I, II and IX.


Assuntos
Anidrase Carbônica II/antagonistas & inibidores , Anidrase Carbônica IV/antagonistas & inibidores , Anidrase Carbônica I/antagonistas & inibidores , Inibidores da Anidrase Carbônica/farmacologia , Nitroimidazóis/farmacologia , Animais , Anidrase Carbônica I/metabolismo , Anidrase Carbônica II/metabolismo , Anidrase Carbônica IV/metabolismo , Inibidores da Anidrase Carbônica/síntese química , Inibidores da Anidrase Carbônica/química , Relação Dose-Resposta a Droga , Humanos , Larva/efeitos dos fármacos , Estrutura Molecular , Nitroimidazóis/síntese química , Nitroimidazóis/química , Relação Estrutura-Atividade , Peixe-Zebra
17.
Math Biosci Eng ; 16(6): 6257-6273, 2019 07 04.
Artigo em Inglês | MEDLINE | ID: mdl-31698561

RESUMO

Tumour hypoxia has been associated with increased resistance to various cancer treatments, particularly radiation therapy. Conversely, tumour hypoxia is a validated and ideal target for guided cancer drug delivery. For this reason, hypoxia-activated prodrugs (HAPs) have been developed, which remain inactive in the body until in the presence of tissue hypoxia, allowing for an activation tendency in hypoxic regions. We present here an experimentally motivated mathematical model predicting the effectiveness of HAPs in a variety of clinical settings. We first examined HAP effectiveness as a function of the amount of tumour hypoxia and showed that the drugs have a larger impact on tumours with high levels of hypoxia. We then combined HAP treatment with radiation to examine the effects of combination therapies. Our results showed radiation-HAP combination therapies to be more effective against highly hypoxic tumours. The analysis of combination therapies was extended to consider schedule sequencing of the combination treatments. These results suggested that administering HAPs before radiation was most effective in reducing total cell number. Finally, a sensitivity analysis of the drug-related parameters was done to examine the effect of drug diffusivity and enzyme abundance on the overall effectiveness of the drug. Altogether, the results highlight the importance of the knowledge of tumour hypoxia levels before administration of HAPs in order to ensure positive results.

18.
Semin Nucl Med ; 49(5): 438-449, 2019 09.
Artigo em Inglês | MEDLINE | ID: mdl-31470936

RESUMO

Radiomics - the high-throughput computation of quantitative image features extracted from medical imaging modalities- can be used to aid clinical decision support systems in order to build diagnostic, prognostic, and predictive models, which could ultimately improve personalized management based on individual characteristics. Various tools for radiomic features extraction are available, and the field gained a substantial scientific momentum for standardization and validation. Radiomics analysis of molecular imaging is expected to provide more comprehensive description of tissues than that of currently used parameters. We here review the workflow of radiomics, the challenges the field currently faces, and its potential for inclusion in clinical decision support systems to maximize disease characterization, and to improve clinical decision-making. We also present guidelines for standardization and implementation of radiomics in order to facilitate its transition to clinical use.


Assuntos
Sistemas de Apoio a Decisões Clínicas , Processamento de Imagem Assistida por Computador/métodos , Medicina Nuclear/métodos , Humanos , Processamento de Imagem Assistida por Computador/normas , Imagem Molecular , Medicina Nuclear/normas , Padrões de Referência
19.
Radiother Oncol ; 141: 247-255, 2019 12.
Artigo em Inglês | MEDLINE | ID: mdl-31431383

RESUMO

BACKGROUND AND PURPOSE: Esophageal cancer incidence is increasing and is rarely curable. Hypoxic tumor areas cause resistance to conventional therapies, making them susceptible for treatment with hypoxia-activated prodrugs (HAPs). We investigated in vivo whether the HAP evofosfamide (TH-302) could increase the therapeutic ratio by sensitizing esophageal carcinomas to radiotherapy without increasing normal tissue toxicity. MATERIALS AND METHODS: To assess therapeutic efficacy, growth of xenografted esophageal squamous cell (OE21) or adeno (OE19) carcinomas was monitored after treatment with TH-302 (50 mg/kg, QD5) and irradiation (sham or 10 Gy). Short- and long-term toxicity was assessed in a gut mucosa and lung fibrosis irradiation model, sensitive to acute and late radiation injury respectively. Mice were injected with TH-302 (50 mg/kg, QD5) and the abdominal area (sham, 8 or 10 Gy) or the upper part of the right lung (sham, 20 Gy) was irradiated. Damage to normal tissues was assessed 84 hours later by histology and blood plasma citrulline levels (gut) and for up to 1 year by non-invasive micro CT imaging (lung). RESULTS: The combination treatment of TH-302 with radiotherapy resulted in significant tumor growth delay in OE19 (P = 0.02) and OE21 (P = 0.03) carcinomas, compared to radiotherapy only. Irradiation resulted in a dose-dependent decrease of crypt survival (P < 0.001), mucosal surface area (P < 0.01) and citrulline levels (P < 0.001) in both tumor and non-tumor bearing animals. On the long-term, irradiation increased CT density in the lung, indicating fibrosis, over time. TH-302 did not influence the radiation-induced short-term and long-term toxicity, confirmed by histological evaluation. CONCLUSION: The combination of TH-302 and radiotherapy might be a promising approach to improve the therapeutic index for esophageal cancer patients.

20.
BMC Med Inform Decis Mak ; 19(1): 130, 2019 07 11.
Artigo em Inglês | MEDLINE | ID: mdl-31296199

RESUMO

BACKGROUND: Patient decision aids (PDAs) can support the treatment decision making process and empower patients to take a proactive role in their treatment pathway while using a shared decision-making (SDM) approach making participatory medicine possible. The aim of this study was to develop a PDA for prostate cancer that is accurate and user-friendly. METHODS: We followed a user-centered design process consisting of five rounds of semi-structured interviews and usability surveys with topics such as informational/decisional needs of users and requirements for PDAs. Our user-base consisted of 8 urologists, 4 radiation oncologists, 2 oncology nurses, 8 general practitioners, 19 former prostate cancer patients, 4 usability experts and 11 healthy volunteers. RESULTS: Informational needs for patients centered on three key factors: treatment experience, post-treatment quality of life, and the impact of side effects. Patients and clinicians valued a PDA that presents balanced information on these factors through simple understandable language and visual aids. Usability questionnaires revealed that patients were more satisfied overall with the PDA than clinicians; however, both groups had concerns that the PDA might lengthen consultation times (42 and 41%, respectively). The PDA is accessible on http://beslissamen.nl/ . CONCLUSIONS: User-centered design provided valuable insights into PDA requirements but challenges in integrating diverse perspectives as clinicians focus on clinical outcomes while patients also consider quality of life. Nevertheless, it is crucial to involve a broad base of clinical users in order to better understand the decision-making process and to develop a PDA that is accurate, usable, and acceptable.


Assuntos
Tomada de Decisão Compartilhada , Técnicas de Apoio para a Decisão , Participação do Paciente , Neoplasias da Próstata/terapia , Adulto , Feminino , Humanos , Masculino , Enfermeiras e Enfermeiros , Enfermagem Oncológica , Educação de Pacientes como Assunto , Médicos , Urologia
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