12 research outputs found
Developing and validating of Ramathibodi Appendicitis Score (RAMA-AS) for diagnosis of appendicitis in suspected appendicitis patients
Integrated Environmental Assessment of streams in the Sinos River basin in the state of Rio Grande do Sul, Brazil
The necessity of management in a lake of the Atlantic Forest biodiversity hotspot: nitrogen levels connected to a persistent bloom of Cylindrospermopsis raciborskii
Feasibility of preoperative chemotherapy for locally advanced, operable colon cancer: The pilot phase of a randomised controlled trial
Summary:
Background Preoperative (neoadjuvant) chemotherapy and radiotherapy are more eff ective than similar postoperative
treatment for oesophageal, gastric, and rectal cancers, perhaps because of more eff ective micrometastasis eradication
and reduced risk of incomplete excision and tumour cell shedding during surgery. The FOxTROT trial aims to
investigate the feasibility, safety, and effi cacy of preoperative chemotherapy for colon cancer.
Methods In the pilot stage of this randomised controlled trial, 150 patients with radiologically staged locally advanced
(T3 with ≥5 mm invasion beyond the muscularis propria or T4) tumours from 35 UK centres were randomly
assigned (2:1) to preoperative (three cycles of OxMdG [oxaliplatin 85 mg/m², l-folinic acid 175 mg, fl uorouracil
400 mg/m² bolus, then 2400 mg/m² by 46 h infusion] repeated at 2-weekly intervals followed by surgery and a
further nine cycles of OxMdG) or standard postoperative chemotherapy (12 cycles of OxMdG). Patients with KRAS
wild-type tumours were randomly assigned (1:1) to receive panitumumab (6 mg/kg; every 2 weeks with the fi rst
6 weeks of chemotherapy) or not. Treatment allocation was through a central randomisation service using a
minimised randomisation procedure including age, radiological T and N stage, site of tumour, and presence of
defunctioning colostomy as stratifi cation variables. Primary outcome measures of the pilot phase were feasibility,
safety, and tolerance of preoperative therapy, and accuracy of radiological staging. Analysis was by intention to treat.
This trial is registered, number ISRCTN 87163246.
Findings 96% (95 of 99) of patients started and 89% (85 of 95) completed preoperative chemotherapy with grade 3–4
gastrointestinal toxicity in 7% (seven of 94) of patients. All 99 tumours in the preoperative group were resected, with
no signifi cant diff erences in postoperative morbidity between the preoperative and control groups: 14% (14 of 99)
versus 12% (six of 51) had complications prolonging hospital stay (p=0·81). 98% (50 of 51) of postoperative
chemotherapy patients had T3 or more advanced tumours confi rmed at post-resection pathology compared with 91%
(90 of 99) of patients following preoperative chemotherapy (p=0·10). Preoperative therapy resulted in signifi cant
downstaging of TNM5 compared with the postoperative group (p=0·04), including two pathological complete
responses, apical node involvement (1% [one of 98] vs 20% [ten of 50], p<0·0001), resection margin involvement (4%
[ four of 99] vs 20% [ten of 50], p=0·002), and blinded centrally scored tumour regression grading: 31% (29 of 94) vs 2%
(one of 46) moderate or greater regression (p=0·0001).
Interpretation Preoperative chemotherapy for radiologically staged, locally advanced operable primary colon cancer is
feasible with acceptable toxicity and perioperative morbidity. Proceeding to the phase 3 trial, to establish whether the
encouraging pathological responses seen with preoperative therapy translates into improved long-term oncological
outcome, is appropriate
A relevance feedback method based on genetic programming for classification of remote sensing images
Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)This paper presents an interactive technique for remote sensing image classification. In our proposal, users are able to interact with the classification system, indicating regions of interest (and those which are not). This feedback information is employed by a genetic programming approach to learning user preferences and combining image region descriptors that encode spectral and texture properties. Experiments demonstrate that the proposed method is effective for image classification tasks and outperforms the traditional MaxVer method. (C) 2010 Elsevier Inc. All rights reserved.18113SI26712684Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Fundação de Amparo à Pesquisa do Estado de Minas Gerais (FAPEMIG)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)CNPq [MCT/CNPq 573871/2008-6]CNPq [MCT/CNPq/CT-INFO 550874/2007-0