Clinicopathological and targeted exome gene features of a patient with metastatic acinic cell carcinoma of the parotid gland harboring an ARID2 nonsense mutation and CDKN2A/B deletion

We describe the presentation, treatment, clinical outcome, and targeted genome analysis of a metastatic salivary acinic cell carcinoma (AciCC). A 71-year-old male presented with a 3 cm right tail of a parotid lesion, first detected as a nodule by the patient seven months earlier. He had a right total parotidectomy with cranial nerve VII resection, right facial nerve resection and grafting, resection of the right conchal cartilage, and right modified radical neck dissection. The primary tumor revealed AciCC with two distinct areas: a well-differentiated component with glandular architecture and a dedifferentiated component with infiltrative growth pattern associated with prominent stromal response, necrosis, perineural invasion, and cellular pleomorphism. Tumor staging was pT4 N0 MX. Immunohistochemistry staining showed pankeratin (+), CD56 (−), and a Ki67 proliferation index of 15%. Upon microscopic inspection, 49 local lymph nodes resected during parotidectomy were negative for cancer cells. Targeted sequencing of the primary tumor revealed deletions of CDKN2A and CDKN2B, a nonsense mutation in ARID2, and single missense mutations of unknown significance in nine other genes. Despite postoperative localized radiation treatment, follow-up whole body PET/CT scan showed lung, soft tissue, bone, and liver metastases. The patient expired 9 months after resection of the primary tumor

Detection of pulmonary nodules by computer-aided diagnosis in multidetector computed tomography: preliminary study of 24 cases

OBJECTIVES: To evaluate the performance of a computer program designed to facilitate the detection of pulmonary nodules using multidetector computed tomography (MDCT) scans of the chest. METHODS: We evaluated 24 consecutive MDCT scans of the chest at the Fleury Diagnostic Imaging Center during the period from October 7 to October 19 of 2006, using a 64-channel CT scanner. The study comprised 12 females and 12 males, ranging from 35 to 77 years of age (mean, 57.9 years). Double reading and a computer-aided diagnosis (CAD) system were used in order to perform two independent analyses of the data. The nodules found using both methods were recorded, and the data were compared. RESULTS: The total sensitivity of CAD for the detection of nodules was 16.5%, increasing to 55% when nodules 1 cm. More than 99% of true nodules detected by CAD were registered in the image double reading process. CONCLUSIONS: In this preliminary 24-case study, the sensitivity of computer program tested was not significantly greater than that of the double-reading process that is routinely performed in this facility.OBJETIVOS: Avaliar o desempenho de um programa para auxílio na detecção de nódulos pulmonares em tomografia computadorizada com múltiplos detectores (TCMD). MÉTODOS: Foram avaliadas 24 tomografias computadorizadas de tórax consecutivas realizadas no Centro de Medicina Diagnóstica Fleury no período de 07/10/2006 a 19/10/2006 usando um tomógrafo helicoidal multidetectores de 64 canais. O estudo compreendeu 12 pacientes do sexo feminino e 12 do sexo masculino, com idades variando entre 35 e 77 anos, idade média de 57,9. As imagens foram analisadas independentemente pelo método da dupla leitura e pelo programa diagnóstico auxiliado por computador (DAC). Os nódulos encontrados nos diferentes processos foram registrados e os dados comparados. RESULTADOS: A sensibilidade total da detecção de nódulos pelo DAC nesse trabalho foi de 16,5%, 55% excluindo os nódulos medindo 1 cm. Menos de 1% dos nódulos verdadeiros destacados pelo DAC não haviam sido registrados no processo de dupla leitura. CONCLUSÕES: Neste trabalho preliminar de 24 casos, o programa testado não conseguiu superar de forma significativa a sensibilidade da dupla leitura realizada de rotina neste serviço.Universidade Federal de São Paulo (UNIFESP) Departamento de Diagnóstico por ImagemCentro de Medicina Diagnóstica FleuryUniversidade Federal de São Paulo (UNIFESP)UNIFESP, Depto. de Diagnóstico por ImagemUNIFESPSciEL

Cancer diagnosis using deep learning: A bibliographic review

In this paper, we first describe the basics of the field of cancer diagnosis, which includes steps of cancer diagnosis followed by the typical classification methods used by doctors, providing a historical idea of cancer classification techniques to the readers. These methods include Asymmetry, Border, Color and Diameter (ABCD) method, seven-point detection method, Menzies method, and pattern analysis. They are used regularly by doctors for cancer diagnosis, although they are not considered very efficient for obtaining better performance. Moreover, considering all types of audience, the basic evaluation criteria are also discussed. The criteria include the receiver operating characteristic curve (ROC curve), Area under the ROC curve (AUC), F1 score, accuracy, specificity, sensitivity, precision, dice-coefficient, average accuracy, and Jaccard index. Previously used methods are considered inefficient, asking for better and smarter methods for cancer diagnosis. Artificial intelligence and cancer diagnosis are gaining attention as a way to define better diagnostic tools. In particular, deep neural networks can be successfully used for intelligent image analysis. The basic framework of how this machine learning works on medical imaging is provided in this study, i.e., pre-processing, image segmentation and post-processing. The second part of this manuscript describes the different deep learning techniques, such as convolutional neural networks (CNNs), generative adversarial models (GANs), deep autoencoders (DANs), restricted Boltzmann’s machine (RBM), stacked autoencoders (SAE), convolutional autoencoders (CAE), recurrent neural networks (RNNs), long short-term memory (LTSM), multi-scale convolutional neural network (M-CNN), multi-instance learning convolutional neural network (MIL-CNN). For each technique, we provide Python codes, to allow interested readers to experiment with the cited algorithms on their own diagnostic problems. The third part of this manuscript compiles the successfully applied deep learning models for different types of cancers. Considering the length of the manuscript, we restrict ourselves to the discussion of breast cancer, lung cancer, brain cancer, and skin cancer. The purpose of this bibliographic review is to provide researchers opting to work in implementing deep learning and artificial neural networks for cancer diagnosis a knowledge from scratch of the state-of-the-art achievements

Lung Nodule Detectability of Artificial Intelligence-assisted CT Image Reading in Lung Cancer Screening

BACKGROUND: Artificial intelligence (AI)-based automatic lung nodule detection system improves the detection rate of nodules. It is important to evaluate the clinical value of AI system by comparing AI-assisted nodule detection with actu-al radiology reports. OBJECTIVE: To compare the detection rate of lung nodules between the actual radiology reports and AI-assisted reading in lung cancer CT screening. METHODS: Participants in chest CT screening from November to December 2019 were retrospectively included. In the real-world radiologist observation, 14 residents and 15 radiologists participated to finalize radiology reports. In AI-assisted reading, one resident and one radiologist reevaluated all subjects with the assistance of an AI system to lo-cate and measure the detected lung nodules. A reading panel determined the type and number of detected lung nodules between these two methods. RESULTS: In 860 participants (57±7 years), the reading panel confirmed 250 patients with >1 solid nodule, while radiolo-gists observed 131, lower than 247 by AI-assisted reading (p1 non-solid nodule, whereas radiologist observation identified 28, lower than 110 by AI-assisted reading (p<0.001). The accuracy and sensitivity of radiologist observation for solid nodules were 86.2% and 52.4%, lower than 99.1% and 98.8% by AI-assisted reading, respectively. These metrics were 90.4% and 25.2% for non-solid nodules, lower than 98.8% and 99.1% by AI-assisted reading, respectively. CONCLUSION: Comparing with the actual radiology reports, AI-assisted reading greatly improves the accuracy and sensi-tivity of nodule detection in chest CT, which benefits lung nodule detection, especially for non-solid nodules

Automatic 3D pulmonary nodule detection in CT images: a survey

This work presents a systematic review of techniques for the 3D automatic detection of pulmonary nodules in computerized-tomography (CT) images. Its main goals are to analyze the latest technology being used for the development of computational diagnostic tools to assist in the acquisition, storage and, mainly, processing and analysis of the biomedical data. Also, this work identifies the progress made, so far, evaluates the challenges to be overcome and provides an analysis of future prospects. As far as the authors know, this is the first time that a review is devoted exclusively to automated 3D techniques for the detection of pulmonary nodules from lung CT images, which makes this work of noteworthy value. The research covered the published works in the Web of Science, PubMed, Science Direct and IEEEXplore up to December 2014. Each work found that referred to automated 3D segmentation of the lungs was individually analyzed to identify its objective, methodology and results. Based on the analysis of the selected works, several studies were seen to be useful for the construction of medical diagnostic aid tools. However, there are certain aspects that still require attention such as increasing algorithm sensitivity, reducing the number of false positives, improving and optimizing the algorithm detection of different kinds of nodules with different sizes and shapes and, finally, the ability to integrate with the Electronic Medical Record Systems and Picture Archiving and Communication Systems. Based on this analysis, we can say that further research is needed to develop current techniques and that new algorithms are needed to overcome the identified drawbacks

The Reference Image Database to Evaluate Response to Therapy in Lung Cancer (RIDER) Project: A Resource for the Development of Change‐Analysis Software

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/110105/1/cptclpt2008161.pd

Papillary carcinoma arising in struma ovarii versus ovarian metastasis from primary thyroid carcinoma: a case report and review of the literature

We present a case of a postmenopausal woman diagnosed with an ovarian mass containing thyroid follicles and foci of papillary thyroid carcinoma during pathological examination. This patient referred having had a metachronous thyroid malignancy 10 years before. The differential diagnosis between a thyroid malignancy arising from a struma ovarii and a metastatic ovarian tumor originating from thyroid-cancer is challenging. Struma ovarii should be considered when thyroid components are the predominant element or when thyroid malignant tissue is identified within an ovarian lesion. Thyroid carcinoma arising from a struma ovarii is reported to occur in a minority of cases. Of these, papillary carcinoma is the most frequent subtype encountered. Regarding primary thyroid carcinomas, papillary carcinomas have a lower metastatic potential when compared to follicular carcinomas, and most of the metastases occur in the cervical lymph nodes. Ovarian metastases are exceedingly rare and generally associated with widespread disease. However, they must be considered in the presence of previous history of malignant thyroid carcinoma. The authors review the main clinical, imaging and therapeutic aspects of both these entities and present the most likely diagnosis