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Introduction: A Computer-Assisted Detection (CAD) System for classification into malignant-benign classes using CT images is proposed. Methods: Two methods that use the fractal dimension (FD) as a measure of the lung nodule contour irregularities (Box counting and Power spectrum) were implemented. The LIDC-IDRI database was used for this study. Of these, 100 slices belonging to 100 patients were analyzed with both methods. Results: The performance between both methods was similar with an accuracy higher than 90%. Little overlap was obtained between FD ranges for the different malignancy grades with both methods, being slightly better in Power spectrum. Box counting had one more false positive than Power spectrum. Discussion: Both methods are able to establish a boundary between the high and low malignancy degree. To further validate these results and enhance the performance of the CAD system, additional studies will be necessary.

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Lung cancer is the most lethal malignant neoplasm worldwide, with an annual estimated rate of 1.8 million deaths. Computed tomography has been widely used to diagnose and detect lung cancer, but its diagnosis remains an intricate and challenging work, even for experienced radiologists. Computer-aided diagnosis tools and radiomics tools have provided support to the radiologist's decision, acting as a second opinion. The main focus of these tools has been to analyze the intranodular zone; nevertheless, recent works indicate that the interaction between the nodule and its surroundings (perinodular zone) could be relevant to the diagnosis process. However, only a few works have investigated the importance of specific attributes of the perinodular zone and have shown how important they are in the classification of lung nodules. In this context, the purpose of this work is to evaluate the impact of using the perinodular zone on the characterization of lung lesions. Motivated by reproducible research, we used a large public dataset of solid lung nodule images and extracted fine-tuned radiomic attributes from the perinodular and intranodular zones. Our best-evaluated model obtained an average AUC of 0.916, an accuracy of 84.26%, a sensitivity of 84.45%, and specificity of 83.84%. The combination of attributes from the perinodular and intranodular zones in the image characterization resulted in an improvement in all the metrics analyzed when compared to intranodular-only characterization. Therefore, our results highlighted the importance of using the perinodular zone in the solid pulmonary nodules classification process.

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OBJETIVO: Determinar el rendimiento diagnóstico del PET/CT en el estudio de nódulo pulmonar (NP) utilizando SUVmax. MÉTODO: Se revisó la base de datos de PET/CT, seleccionando aquellos solicitados para estudio de NP sólido. Se incluyeron sólo aquellos NP confirmados como malignos o benignos. Se excluyó NP subsólidos, masas pulmonares (> 3 cm), y pacientes con metástasis conocidas. Se midió SUVmax de las lesiones, determinando mejores valores de corte para malignidad y benignidad. RESULTADOS: De los 140 NP estudiados, el 60% (84/140) fueron confirmados como malignos y el 40% como benignos (100% y 59,6% de confirmación histológica, respectivamente). Un SUVmax ≤ 1,0 mostró sensibilidad 98,8%, valor predictivo negativo (VPN) 96,2%, y Likelihood ratio negativo (LR -) 0,027. Un SUVmax ≤ 2,5 no fue capaz de asegurar razonablemente benignidad con VPN 69,4%, y LR - 0,295. Valores de SUV > 2,5 y 5,0 se asociaron a malignidad en 83% y 93% de los casos, respectivamente (LR+ 3,333 y 8,889). CONCLUSIÓN: El PET/CT presenta alto rendimiento diagnóstico en estimar la naturaleza de un NP Un valor de SUVmax ≤ 1 es altamente predictivo de benignidad, y un valor de SUVmax ≥ 2,5 de malignidad. Valores entre 1,0 y 2,5 no permiten caracterizar eficientemente los NP.

AIM: To establish the diagnostic accuracy of PET/CT in study of solid lung nodule (LN) using SUVmax index. METHOD: We revised PET/CT data base, selecting those scans asked to evaluate a solid LN. Only confirmed malign o benign LN were included. Subsolid LN, lung masses (> 3 cm), and known or suspected lung metastases were excluded. SUVmax was measured in each LN, and best cutoff for malignant and benign lesion was calculated. RESULTS: Of the whole group of 140 LN, 60% were confirmed as malignant, and 40% as benign (100% and 59,6% of histological confirmation, respectively). SUVmax ≤ 1,0 showed sensibility of 98,8%, negative predictive value (NPV) of 96,2%, and negative likelihood ratio (LR —) of 0,027. SUVmax ≤ 2,5 was not able to guarantee reasonably benign nature of LN, showing NPV of 69,4% and LR - of 0,295. SUVmax > 2,5 and > 5,0 was associated to malign lesion in 83% and 93% of cases, respectively (LR + of 3,333 and 8,889). CONCLUSION: PET/CT shows high accuracy estimating the nature of solid LN. SUVmax ≤ 1,0 is highly predictive of benignity, and SUVmax ≥ 2,5 is highly predictive of malignancy. SUVmax values between 1,0 and 2,5 were not able to characterize efficiently LN.

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ABSTRACT The objective of this work is to identify the malignant lung nodules accurately and early with less false positives. 'Nodule' is the 3mm to 30mm diameter size tissue clusters present inside the lung parenchyma region. Segmenting such a small nodules from consecutive CT scan slices are a challenging task. In our work Auto-seed clustering based segmentation technique is used to segment all the possible nodule candidates. Efficient shape and texture features (2D and 3D) were computed to eliminate the false nodule candidates. The change in centroid position of nodule candidates from consecutive slices was used as a measure to remove the vessels. The two-stage classifier is used in this work to classify the malignant and benign nodules. First stage rule-based classifier producing 100 % sensitivity, but with high false positive of 12.5 per patient scan. The BPN based ANN classifier is used as the second-stage classifier which reduces a false positive to 2.26 per patient scan with a reasonable sensitivity of 88.8%. The Rate of Nodule Growth (RNG) was computed in our work to measure the nodules growth between the two scans of the same patient taken at different time interval. Finally, the nodule growth predictive measure was modeled through the features such as compactness (CO), mass deficit (MD), mass excess (ME) and isotropic factor(IF). The developed model results show that the nodules which have low CO, low IF, high MD and high ME values might have the potential to grow in future.

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Fonsecaea spp. are melanized fungi which cause most cases of chromoblastomycosis. The taxonomy of this genus has been revised, now encompassing four species, with different pathogenic potential: F. pedrosoi, F. nubica, F. pugnacius, and F. monophora. The latter two species present wider clinical spectrum and have been associated with cases of visceral infection, most often affecting the brain. To our knowledge, this is the first report of proven case of F. monophora respiratory tract infection. A Brazilian 57-year-old-female patient underwent kidney transplantation on January 12, 2013. On the fourth postoperative month, the patient presented with fever, productive cough, and pleuritic pain in the right hemithorax. A thoracic CT scan showed a subpleural 2.2-cm nodular lesion in the right lung lower lobe, with other smaller nodules (0.5-0.7 cm) scattered in both lungs. Bronchoscopy revealed a grayish plaque on the right bronchus which was biopsied. Microscopic examination demonstrated invasion of bronchial mucosa by pigmented hyphae. Culture from the bronchial biopsy and bronchoalveolar lavage samples yielded a melanized mold, which was eventually identified as F. monophora. She started treatment with voriconazole (400 mg q.12h on the first day, followed by 200 mg q.12h). After 4 weeks of therapy, voriconazole dose was escalated to 200 mg q.8h and associated with amphotericin B (deoxycolate 1 mg/kg/day) because of a suspected dissemination to the brain. The patient eventually died of sepsis 8 weeks after the start of antifungal therapy. In conclusion, F. monophora may cause respiratory tract infection in solid organ transplant recipients.

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Abstract Introduction Lung cancer remains the leading cause of cancer mortality worldwide, with one of the lowest survival rates after diagnosis. Therefore, early detection greatly increases the chances of improving patient survival. Methods This study proposes a method for diagnosis of lung nodules in benign and malignant tumors based on image processing and pattern recognition techniques. Taxonomic indexes and phylogenetic trees were used as texture descriptors, and a Support Vector Machine was used for classification. Results The proposed method shows promising results for accurate diagnosis of benign and malignant lung tumors, achieving an accuracy of 88.44%, sensitivity of 84.22%, specificity of 90.06% and area under the ROC curve of 0.8714. Conclusion The results demonstrate the promising performance of texture extraction techniques by means of taxonomic indexes combined with phylogenetic trees. The proposed method achieves results comparable to those previously published.