Caracterização do infiltrado inflamatório e avaliação dos marcadores de prognóstico Ki-67, p53, receptor de estrógeno e progesterona no tumor mamário maligno de cadelas

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Development of a CAD system for automatic classification of microcalcifications based on FPGA

This paper presents a Computer Aided Diagnosis (CAD) system that automatically classifies microcalcifications detected on digital mammograms into one of the five types proposed by Michele Le Gal, a classification scheme that allows radiologists to determine whether a breast tumor is malignant or not without the need for surgeries. The developed system uses a combination of wavelets and Artificial Neural Networks (ANN) and is executed on an Altera DE2-115 Development Kit, a kit containing a Field-Programmable Gate Array (FPGA) that allows the system to be smaller, cheaper and more energy efficient. Results have shown that the system was able to correctly classify 96.67% of test samples, which can be used as a second opinion by radiologists in breast cancer early diagnosis. (C) 2013 The Authors. Published by Elsevier B.V.

Classificação morfológica e imunostoquímica em microarranjo de tecido (TMA) de linfomas não-Hodgkin em cães conforme os critérios da Histological Classification od Hematopoietic Tumors of Domestic Animals (WHO)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Evaluation of argyrophilic nucleolar organizer regions in oral tumor progression

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

A comparison of CA125, HE4, risk ovarian malignancy algorithm (ROMA), and risk malignancy index (RMI) for the classification of ovarian masses

OBJECTIVE: Differentiation between benign and malignant ovarian neoplasms is essential for creating a system for patient referrals. Therefore, the contributions of the tumor markers CA125 and human epididymis protein 4 (HE4) as well as the risk ovarian malignancy algorithm (ROMA) and risk malignancy index (RMI) values were considered individually and in combination to evaluate their utility for establishing this type of patient referral system. METHODS: Patients who had been diagnosed with ovarian masses through imaging analyses (n = 128) were assessed for their expression of the tumor markers CA125 and HE4. The ROMA and RMI values were also determined. The sensitivity and specificity of each parameter were calculated using receiver operating characteristic curves according to the area under the curve (AUC) for each method. RESULTS: The sensitivities associated with the ability of CA125, HE4, ROMA, or RMI to distinguish between malignant versus benign ovarian masses were 70.4%, 79.6%, 74.1%, and 63%, respectively. Among carcinomas, the sensitivities of CA125, HE4, ROMA (pre-and post-menopausal), and RMI were 93.5%, 87.1%, 80%, 95.2%, and 87.1%, respectively. The most accurate numerical values were obtained with RMI, although the four parameters were shown to be statistically equivalent. CONCLUSION: There were no differences in accuracy between CA125, HE4, ROMA, and RMI for differentiating between types of ovarian masses. RMI had the lowest sensitivity but was the most numerically accurate method. HE4 demonstrated the best overall sensitivity for the evaluation of malignant ovarian tumors and the differential diagnosis of endometriosis. All of the parameters demonstrated increased sensitivity when tumors with low malignancy potential were considered low-risk, which may be used as an acceptable assessment method for referring patients to reference centers.

Frequent ATRX, CIC, FUBP1 and IDH1 mutations refine the classification of malignant gliomas

Mutations in the critical chromatin modifier ATRX and mutations in CIC and FUBP1, which are potent regulators of cell growth, have been discovered in specific subtypes of gliomas, the most common type of primary malignant brain tumors. However, the frequency of these mutations in many subtypes of gliomas, and their association with clinical features of the patients, is poorly understood. Here we analyzed these loci in 363 brain tumors. ATRX is frequently mutated in grade II-III astrocytomas (71%), oligoastrocytomas (68%), and secondary glioblastomas (57%), and ATRX mutations are associated with IDH1 mutations and with an alternative lengthening of telomeres phenotype. CIC and FUBP1 mutations occurred frequently in oligodendrogliomas (46% and 24%, respectively) but rarely in astrocytomas or oligoastrocytomas (<10%). This analysis allowed us to define two highly recurrent genetic signatures in gliomas: IDH1/ATRX (I-A) and IDH1/CIC/FUBP1 (I-CF). Patients with I-CF gliomas had a significantly longer median overall survival (96 months) than patients with I-A gliomas (51 months) and patients with gliomas that did not harbor either signature (13 months). The genetic signatures distinguished clinically distinct groups of oligoastrocytoma patients, which usually present a diagnostic challenge, and were associated with differences in clinical outcome even among individual tumor types. In addition to providing new clues about the genetic alterations underlying gliomas, the results have immediate clinical implications, providing a tripartite genetic signature that can serve as a useful adjunct to conventional glioma classification that may aid in prognosis, treatment selection, and therapeutic trial design.

Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Cancer classification using the Immunoscore: a worldwide task force

Prediction of clinical outcome in cancer is usually achieved by histopathological evaluation of tissue samples obtained during surgical resection of the primary tumor. Traditional tumor staging (AJCC/UICC-TNM classification) summarizes data on tumor burden (T), presence of cancer cells in draining and regional lymph nodes (N) and evidence for metastases (M). However, it is now recognized that clinical outcome can significantly vary among patients within the same stage. The current classification provides limited prognostic information, and does not predict response to therapy. Recent literature has alluded to the importance of the host immune system in controlling tumor progression. Thus, evidence supports the notion to include immunological biomarkers, implemented as a tool for the prediction of prognosis and response to therapy. Accumulating data, collected from large cohorts of human cancers, has demonstrated the impact of immune-classification, which has a prognostic value that may add to the significance of the AJCC/UICC TNM-classification. It is therefore imperative to begin to incorporate the 'Immunoscore' into traditional classification, thus providing an essential prognostic and potentially predictive tool. Introduction of this parameter as a biomarker to classify cancers, as part of routine diagnostic and prognostic assessment of tumors, will facilitate clinical decision-making including rational stratification of patient treatment. Equally, the inherent complexity of quantitative immunohistochemistry, in conjunction with protocol variation across laboratories, analysis of different immune cell types, inconsistent region selection criteria, and variable ways to quantify immune infiltration, all underline the urgent requirement to reach assay harmonization. In an effort to promote the Immunoscore in routine clinical settings, an international task force was initiated. This review represents a follow-up of the announcement of this initiative, and of the J Transl Med. editorial from January 2012. Immunophenotyping of tumors may provide crucial novel prognostic information. The results of this international validation may result in the implementation of the Immunoscore as a new component for the classification of cancer, designated TNM-I (TNM-Immune).

Determination of histopathological tumor grade in neuroepithelial brain tumors by using spectral pattern analysis of in vivo spectroscopic data

OBJECT: In this study, 1H magnetic resonance (MR) spectroscopy was prospectively tested as a reliable method for presurgical grading of neuroepithelial brain tumors. METHODS: Using a database of tumor spectra obtained in patients with histologically confirmed diagnoses, 94 consecutive untreated patients were studied using single-voxel 1H spectroscopy (point-resolved spectroscopy; TE 135 msec, TE 135 msec, TR 1500 msec). A total of 90 tumor spectra obtained in patients with diagnostic 1H MR spectroscopy examinations were analyzed using commercially available software (MRUI/VARPRO) and classified using linear discriminant analysis as World Health Organization (WHO) Grade I/II, WHO Grade III, or WHO Grade IV lesions. In all cases, the classification results were matched with histopathological diagnoses that were made according to the WHO classification criteria after serial stereotactic biopsy procedures or open surgery. Histopathological studies revealed 30 Grade I/II tumors, 29 Grade III tumors, and 31 Grade IV tumors. The reliability of the histological diagnoses was validated considering a minimum postsurgical follow-up period of 12 months (range 12-37 months). Classifications based on spectroscopic data yielded 31 tumors in Grade I/II, 32 in Grade III, and 27 in Grade IV. Incorrect classifications included two Grade II tumors, one of which was identified as Grade III and one as Grade IV; two Grade III tumors identified as Grade II; two Grade III lesions identified as Grade IV; and six Grade IV tumors identified as Grade III. Furthermore, one glioblastoma (WHO Grade IV) was classified as WHO Grade I/II. This represents an overall success rate of 86%, and a 95% success rate in differentiating low-grade from high-grade tumors. CONCLUSIONS: The authors conclude that in vivo 1H MR spectroscopy is a reliable technique for grading neuroepithelial brain tumors.

Survival in surgically treated, nodal positive prostate cancer patients is predicted by histopathological characteristics of the primary tumor and its lymph node metastases

BACKGROUND: Histopathological risk factors for survival stratification of surgically treated nodal positive prostate cancer patients are poorly defined as reflected by only one category for nodal metastases. METHODS: We evaluated biochemical recurrence-free survival (RFS), disease-specific survival (DSS), and overall survival (OS) in 102 nodal positive, hormone treatment-naïve prostate cancer patients (median age: 65 years, range: 45-75 years; median follow-up 7.7 years, range: 1.0-15.9 years) who underwent radical prostatectomy and standardized extended lymphadenectomy. RESULTS: A significant stratification was possible, with the Gleason score of the primary and virtually all nodal parameters favoring patients with better differentiated primaries and metastases, lower nodal tumor burden, and without extranodal extension of metastases. In multivariate analyses, diameter of the largest metastasis (< or =10 mm vs. >10 mm) was the strongest independent predictor for RFS (P < 0.001), DSS (P < 0.001), and OS (P < 0.001) with a more than quadrupled relative risk of cancer related deaths for patients with larger metastases (Hazard ratio: 4.2, Confidence interval: 2.0-8.9; 5-year RFS/DSS/OS: 18%/57%/54%). The highest 5-year survival rates were seen in patients with micrometastases only (RFS/DSS/OS: 47%/94%/94%). CONCLUSION: The TNM classification's current allocation of only one category for nodal metastases in prostate cancers is unsatisfactory since subgroups with significantly different prognoses can be identified. The diameter of the patient's largest metastasis (< or =10 mm vs. >10 mm) should be used for substaging because of its independent prognostic value. The substage "micrometastasis only" is also useful in nodal positive prostate cancer since it designates the subgroup with the most favorable outcome.

Prognostic implications of the seventh edition of the international union against cancer classification for patients with gastric cancer: the Western experience of patients treated in a single-center European institution

PURPOSE Validity of the seventh edition of the American Joint Committee on Cancer/International Union Against Cancer (AJCC/UICC) staging systems for gastric cancer has been evaluated in several studies, mostly in Asian patient populations. Only few data are available on the prognostic implications of the new classification system on a Western population. Therefore, we investigated its prognostic ability based on a German patient cohort. PATIENTS AND METHODS Data from a single-center cohort of 1,767 consecutive patients surgically treated for gastric cancer were classified according to the seventh edition and were compared using the previous TNM/UICC classification. Kaplan-Meier analyses were performed for all TNM stages and UICC stages in a comparative manner. Additional survival receiver operating characteristic analyses and bootstrap-based goodness-of-fit comparisons via Bayesian information criterion (BIC) were performed to assess and compare prognostic performance of the competing classification systems. RESULTS We identified the UICC pT/pN stages according to the seventh edition of the AJCC/UICC guidelines as well as resection status, age, Lauren histotype, lymph-node ratio, and tumor grade as independent prognostic factors in gastric cancer, which is consistent with data from previous Asian studies. Overall survival rates according to the new edition were significantly different for each individual's pT, pN, and UICC stage. However, BIC analysis revealed that, owing to higher complexity, the new staging system might not significantly alter predictability for overall survival compared with the old system within the analyzed cohort from a statistical point of view. CONCLUSION The seventh edition of the AJCC/UICC classification was found to be valid with distinctive prognosis for each stage. However, the AJCC/UICC classification has become more complex without improving predictability for overall survival in a Western population. Therefore, simplification with better predictability of overall survival of patients with gastric cancer should be considered when revising the seventh edition.

Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation

In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

Interactive Segmentation of MR Images from Brain Tumor Patients

Medical doctors often do not trust the result of fully automatic segmentations because they have no possibility to make corrections if necessary. On the other hand, manual corrections can introduce a user bias. In this work, we propose to integrate the possibility for quick manual corrections into a fully automatic segmentation method for brain tumor images. This allows for necessary corrections while maintaining a high objectiveness. The underlying idea is similar to the well-known Grab-Cut algorithm, but here we combine decision forest classification with conditional random field regularization for interactive segmentation of 3D medical images. The approach has been evaluated by two different users on the BraTS2012 dataset. Accuracy and robustness improved compared to a fully automatic method and our interactive approach was ranked among the top performing methods. Time for computation including manual interaction was less than 10 minutes per patient, which makes it attractive for clinical use.

The tumor border configuration of colorectal cancer as a histomorphological prognostic indicator.

Histomorphological features of colorectal cancers (CRC) represent valuable prognostic indicators for clinical decision making. The invasive margin is a central feature for prognostication shaped by the complex processes governing tumor-host interaction. Assessment of the tumor border can be performed on standard paraffin sections and shows promise for integration into the diagnostic routine of gastrointestinal pathology. In aggressive CRC, an extensive dissection of host tissue is seen with loss of a clear tumor-host interface. This pattern, termed "infiltrative tumor border configuration" has been consistently associated with poor survival outcome and early disease recurrence of CRC-patients. In addition, infiltrative tumor growth is frequently associated with presence of adverse clinicopathological features and molecular alterations related to aggressive tumor behavior including BRAFV600 mutation. In contrast, a well-demarcated "pushing" tumor border is seen frequently in CRC-cases with low risk for nodal and distant metastasis. A pushing border is a feature frequently associated with mismatch-repair deficiency and can be used to identify patients for molecular testing. Consequently, assessment of the tumor border configuration as an additional prognostic factor is recommended by the AJCC/UICC to aid the TNM-classification. To promote the assessment of the tumor border configuration in standard practice, consensus criteria on the defining features and method of assessment need to be developed further and tested for inter-observer reproducibility. The development of a standardized quantitative scoring system may lay the basis for verification of the prognostic associations of the tumor growth pattern in multivariate analyses and clinical trials. This article provides a comprehensive review of the diagnostic features, clinicopathological associations, and molecular alterations associated with the tumor border configuration in early stage and advanced CRC.

Impact of miR-21, miR-126 and miR-221 as prognostic factors of clear cell renal cell carcinoma with tumor thrombus of the inferior vena cava

Clear cell renal cell carcinoma (ccRCC) characterized by a tumor thrombus (TT) extending into the inferior vena cava (IVC) generally indicates poor prognosis. Nevertheless, the risk for tumor recurrence after nephrectomy and thrombectomy varies. An applicable and accurate prediction system to select ccRCC patients with TT of the IVC (ccRCC/TT) at high risk after nephrectomy is urgently needed, but has not been established up to now. To our knowledge, a possible role of microRNAs (miRs) for the development of ccRCC/TT or their impact as prognostic markers in ccRCC/TT has not been explored yet. Therefore, we analyzed the expression of the previously described onco-miRs miR-200c, miR-210, miR-126, miR-221, let-7b, miR-21, miR-143 and miR-141 in a study collective of 74 ccRCC patients. Using the expression profiles of these eight miRs we developed classification systems that accurately differentiate ccRCC from non-cancerous renal tissue and ccRCC/TT from tumors without TT. In the subgroup of 37 ccRCC/TT cases we found that miR-21, miR-126, and miR-221 predicted cancer related death (CRD) accurately and independently from other clinico-pathological features. Furthermore, a combined risk score based on the expression of miR-21, miR-126 and miR-221 was developed and showed high sensitivity and specificity to predict cancer specific survival (CSS) in ccRCC/TT. Using the combined risk score we were able to classify ccRCC/TT patients correctly into high and low risk cases. The risk stratification by the combined risk score (CRS) will benefit from further cohort validation and might have potential for clinical application as a molecular prediction system to identify high- risk ccRCC/TT patients.