scholarly journals Dilemma of the differential diagnosis of hilar cholangiocarcinoma and benign diseases:a single-center retrospective study

2020 ◽  
Author(s):  
Liwei Pang ◽  
Shangzhi Hu ◽  
Wanlin Dai ◽  
Shuodong Wu ◽  
Jing Kong
Keyword(s):  
Differential Diagnosis ◽  
Hilar Cholangiocarcinoma ◽  
Characteristic Curve ◽  
Age At Onset ◽  
Clinical Manifestations ◽  
Serum Levels ◽  
Institutional Review ◽  
Malignant Group ◽  
Informed Consent Document ◽  
Benign Group

Abstract Hilar cholangiocarcinoma, which lacks specific clinical manifestations, remains very difficult to distinguish from benign disease. This distinction is further complicated by the complex hilar anatomy. We conducted the present study to evaluate the differential diagnosis of these conditions. Sixty-five patients underwent resection surgery for suspected hilar cholangiocarcinoma between January 2011 and October 2018. Institutional Review Board of Shengjing hospital agreed this study and all particpants sign an informed consent document prior to participation in a research study. Following a postoperative pathology analysis, all patients were divided into 2 groups: malignant group (54 patients with HCCA) and benign group (11 cases with benign lesions). Compared to the benign group, the malignant group had a significantly higher median age and serum CA19-9, CEA, ALT, BILT, and BILD levels (P <0.05). By contrast, the groups did not differ significantly in terms of the sex distribution, clinical manifestations, serum levels of AST and ALKP, and imaging findings. In a receiver operating characteristic curve analysis, we identified a CA19-9 cut-off point of 233.15 U/ml for the differential diagnosis and CEA cut-off point of 2.98 ng/ml for the differential diagnosis. The differential diagnosis of HCCA and benign hilar lesions remains difficult. However, we found that patients with HCCA tended to have an older age at onset and higher serum levels of CA19-9, CEA, BILT, ALT, and BILD. Furthermore, patients with a serum CA19-9 level >233.15 U/ml and CEA level >2.98 ng/ml are more likely to have malignant disease.

scholarly journals Dilemma of the differential diagnosis of hilar cholangiocarcinoma and benign diseases:a single-center retrospective study

2019 ◽  
Author(s):  
Liwei Pang
Keyword(s):  
Differential Diagnosis ◽  
Hilar Cholangiocarcinoma ◽  
Characteristic Curve ◽  
Age At Onset ◽  
Laboratory Data ◽  
Clinical Manifestations ◽  
Serum Levels ◽  
Imaging Findings ◽  
Malignant Group ◽  
Benign Group

Abstract Objective Hilar cholangiocarcinoma, which lacks specific clinical manifestations, remains very difficult to distinguish from benign disease. This distinction is further complicated by the complex hilar anatomy. We conducted the present study to evaluate the differential diagnosis of these conditions. Methods Sixty-five patients underwent resection surgery for suspected hilar cholangiocarcinoma between January 2011 and October 2018. Following a postoperative pathology analysis, all patients were divided into 2 groups: malignant group (54 patients with HCCA) and benign group (11 cases with benign lesions). The patients' clinical data, including general demographics (sex, age), clinical manifestations (jaundice, abdominal discomfort, fever, weight loss), laboratory data [alanine aminotransferase (ALT), aspartate aminotransferase (AST), alkaline phosphatase (ALKP), total bilirubin (BILT), indirect bilirubin (BILD), carbohydrate antigen (CA) 19-9, carcinoembryonic antigen (CEA)], and imaging findings, were included in a retrospective analysis. Results Compared to the benign group, the malignant group had a significantly higher median age and serum CA19-9, CEA, ALT, BILT, and BILD levels (P <0.05). By contrast, the groups did not differ significantly in terms of the sex distribution, clinical manifestations, serum levels of AST and ALKP, and imaging findings. In a receiver operating characteristic curve analysis, we identified a CA19-1 cut-off point of 233.15 U/ml for the differential diagnosis, with a sensitivity of 56% and specificity of 91%. Furthermore, we identified a CEA cut-off point of 2.98 ng/ml for the differential diagnosis, with a sensitivity of 61% and specificity of 90%. Conclusion The differential diagnosis of HCCA and benign hilar lesions remains difficult. However, we found that patients with HCCA tended to have an older age at onset and higher serum levels of CA19-9, CEA, BILT, ALT, and BILD. Furthermore, patients with a serum CA19-9 level >233.15 U/ml and CEA level >2.98 ng/ml are more likely to have malignant disease.

scholarly journals Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

BMC Cancer ◽  
2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Sihua Niu ◽  
Jianhua Huang ◽  
Jia Li ◽  
Xueling Liu ◽  
Dan Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Breast Imaging ◽  
Texture Features ◽  
Matrix Analysis ◽  
Width Ratio ◽  
Malignant Group ◽  
Malignant Lesions ◽  
Occurrence Matrix ◽  
Benign Group

Abstract Background The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions. Methods A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed. Results Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P = 0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions. Conclusions Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.

scholarly journals Differential diagnosis of small bowel occlusions

2009 ◽  
Vol 3 (2) ◽  
pp. 81-87
Author(s):  
Paolo Ghiringhelli
Keyword(s):  
Colorectal Cancer ◽  
Differential Diagnosis ◽  
Lynch Syndrome ◽  
Hereditary Nonpolyposis Colorectal Cancer ◽  
Autosomal Dominant ◽  
Age At Onset ◽  
Clinical Manifestations ◽  
Early Age ◽  
Increased Risk ◽  
Hereditary Nonpolyposis

Hereditary nonpolyposis colorectal cancer (HNPCC), also known as Lynch syndrome, is a common autosomal dominant syndrome characterized by early age at onset, and microsatellite instability (MSI). Patients with Lynch syndrome have a markedly increased risk of colorectal cancer. We report a case of a 28-year-old male with Lynch syndrome; the case allows to describe clinical manifestations and diagnostic criteria of this syndrome, and to underline the importance of genetics in the diagnosis of this disease.

Exploring serum metabolic markers for the discrimination of ccRCC from renal angiomyolipoma by metabolomics

2020 ◽  
Vol 14 (8) ◽  
pp. 675-682
Author(s):  
Mingfeng Xiang ◽  
Feng Du ◽  
Jing Dai ◽  
Ling Chen ◽  
Ruijin Geng ◽  
...  
Keyword(s):  
Differential Diagnosis ◽  
Uric Acid ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Serum Levels ◽  
Resonance Spectroscopy ◽  
Renal Angiomyolipoma ◽  
Serum Samples ◽  
Metabolic Markers ◽  
Cell Renal Carcinoma

Aim: The discrimination of renal cell carcinoma from renal angiomyolipoma (RAML) is crucial for the effective treatment of each. Materials & methods: Serum samples were analyzed by nuclear magnetic resonance spectroscopy-based metabolomics and a number of metabolites were further quantified by HPLC–UV. Results: Clear-cell renal carcinoma (ccRCC) was characterized by drastic disruptions in energy, amino acids, creatinine and uric acid metabolic pathways. A logistic model for the differential diagnosis of RAML from ccRCC was established using the combination of serum levels of uric acid, the ratio of uric acid to hypoxanthine and the ratio of hypoxanthine to creatinine as variables with area under the curve of the receiver operating characteristic curve value of 0.907. Conclusion: Alterations in serum purine metabolites may be used as potential metabolic markers for the differential diagnosis of ccRCC and RAML.

scholarly journals Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

2020 ◽  
Author(s):  
Sihua Niu ◽  
Jianhua Huang ◽  
Jia Li ◽  
Xueling Liu ◽  
Dan Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Breast Imaging ◽  
Texture Features ◽  
Matrix Analysis ◽  
Width Ratio ◽  
Malignant Group ◽  
Malignant Lesions ◽  
Occurrence Matrix ◽  
Benign Group

Abstract Background: The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions.Methods: A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed.Results: Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P=0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions.Conclusions: Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.

scholarly journals Application of Ultrasound Artificial Intelligence in the Differential Diagnosis Between Benign and Malignant Breast Lesions of BI-RADS 4A

2020 ◽  
Author(s):  
SIHUA Niu ◽  
Jianhua Huang ◽  
Jia Li ◽  
Xueling Liu ◽  
Dan Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Breast Imaging ◽  
Matrix Analysis ◽  
Width Ratio ◽  
Grey Level ◽  
Edge Roughness ◽  
Malignant Group ◽  
Malignant Lesions ◽  
Benign Group

Abstract Background: The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is entirely based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS category. We analysed the morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and the ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions.Methods: A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the morphological and texture features of the lesions, such as circularity, depth-to-width ratio, number of spicules, edge roughness, edge fuzziness, margin lobules, energy, entropy, mean grey level, grey level variance, grey level similarity, internal calcification and angle between the long axis of the lesion and skin（ALS） of the ROI, were calculated using grey level gradient co-occurrence matrix analysis. The differences between benign and malignant lesions of BI-RADS 4A were analysed.Results: There were significant differences between the benign group and malignant group in margin lobules, entropy, internal calcification and ALS (P=0.013, 0.045, 0.045, 0.002, respectively). The malignant group had more margin lobules and lower entropy than the benign group, and the benign group had more internal calcification and a larger ALS than the malignant group. There were no significant differences in circularity, depth-to-width ratio, number of spicules, edge roughness, edge fuzziness, energy, mean of grey level, grey level variance, and grey level similarity between benign and malignant lesions.Conclusion: For benign and malignant lesions of BI-RADS 4A, margin lobules and internal echo uniformity are the critical points of differentiation. Some of the characteristics of atypical benign and malignant lesions are blurry or even inverted, which may lead to a deviation of the characteristics of benign and malignant lesions.

Peripheral Lymphadenopathies in Childhood: Experience at a Pediatric Oncology Department in Turkey

2020 ◽  
pp. 66-73
Author(s):  
Tuğçe Kalın Güngör ◽  
Handan Uğur Dinçaslan ◽  
Emel Cabi Ünal ◽  
Nurdan Taçyıldız ◽  
Leman Gülsan Yavuz
Keyword(s):  
Differential Diagnosis ◽  
Clinical Laboratory ◽  
Laboratory Findings ◽  
Abnormal Chest ◽  
Malignant Group ◽  
Chest X Ray ◽  
Systemic Symptoms ◽  
Reactive Hyperplasia ◽  
Physical Findings ◽  
Benign Group

Introduction: Palpable lymph nodes are very common physical examination findings in childhood, and sometimes it can be challenging to say if it is benign or malignant. Objectives: This retrospective study evaluated 157 children admitted to an oncology department because of lymphadenopathy and aimed to determine the clinical, laboratory, and epidemiologic data valuable for differential diagnosis. Materials and Methods: One hundred fifty-two cases were analyzed, which were defined as either malignant or benign by the etiology. The benign cases were also defined to three groups as ‘viral lymphadenopathy’, ‘bacterial lymphadenopathy’, and ‘other reactive lymphadenopathy’. Results: A specific cause for lymphadenopathy was documented in 61 (40,1%) cases. Of 152 cases, benign causes were detected in 133 (87,5%), and malignant causes were detected in 19 (12,5%) cases. The most frequent cause in the benign group was reactive hyperplasia (59,8%) and in the malignant group was lymphoma (7,3%). A biopsy was performed from 19 of the cases for diagnosis. Malign causes were detected in 12 (58%), and benign causes were detected in the remaining 7 (42%). In terms of differential diagnosis, some symptoms, physical findings, and laboratory tests showed meaningful differences between the case groups Conclusions: The following findings were determined as being important to alert physicians about the probability of a malignant disorder: location of lymphadenopathy, number of associated systemic symptoms, size of lymph node, abnormal laboratory findings, abnormal chest X-ray.

scholarly journals Application of ultrasound artificial intelligence in the differential diagnosis between benign and malignant breast lesions of BI-RADS 4A

2020 ◽  
Author(s):  
Sihua Niu ◽  
Jianhua Huang ◽  
Jia Li ◽  
Xueling Liu ◽  
Dan Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Breast Imaging ◽  
Texture Features ◽  
Matrix Analysis ◽  
Width Ratio ◽  
Malignant Group ◽  
Malignant Lesions ◽  
Occurrence Matrix ◽  
Benign Group

Abstract Background: The classification of Breast Imaging Reporting and Data System 4A (BI-RADS 4A) lesions is mostly based on the personal experience of doctors and lacks specific and clear classification standards. The development of artificial intelligence (AI) provides a new method for BI-RADS categorisation. We analysed the ultrasonic morphological and texture characteristics of BI-RADS 4A benign and malignant lesions using AI, and these ultrasonic characteristics of BI-RADS 4A benign and malignant lesions were compared to examine the value of AI in the differential diagnosis of BI-RADS 4A benign and malignant lesions.Methods: A total of 206 lesions of BI-RADS 4A examined using ultrasonography were analysed retrospectively, including 174 benign lesions and 32 malignant lesions. All of the lesions were contoured manually, and the ultrasonic morphological and texture features of the lesions, such as circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, margin lobulation, energy, entropy, grey mean, internal calcification and angle between the long axis of the lesion and skin, were calculated using grey level gradient co-occurrence matrix analysis. Differences between benign and malignant lesions of BI-RADS 4A were analysed.Results: Significant differences in margin lobulation, entropy, internal calcification and ALS were noted between the benign group and malignant group (P=0.013, 0.045, 0.045, and 0.002, respectively). The malignant group had more margin lobulations and lower entropy compared with the benign group, and the benign group had more internal calcifications and a greater angle between the long axis of the lesion and skin compared with the malignant group. No significant differences in circularity, height-to-width ratio, margin spicules, margin coarseness, margin indistinctness, energy, and grey mean were noted between benign and malignant lesions.Conclusions: Compared with the naked eye, AI can reveal more subtle differences between benign and malignant BI-RADS 4A lesions. These results remind us carefully observation of the margin and the internal echo is of great significance. With the help of morphological and texture information provided by AI, doctors can make a more accurate judgment on such atypical benign and malignant lesions.

A glance on dystonias, how to recognize and handle them

2019 ◽  
pp. 22-29
Author(s):  
F. N. Mercan ◽  
E. Bayram ◽  
M. C. Akbostanci
Keyword(s):  
Differential Diagnosis ◽  
Movement Disorder ◽  
Age Of Onset ◽  
Clinical Manifestations ◽  
Body Part ◽  
Classification Model ◽  
Treatment Choices ◽  
Muscle Groups

Dystonia refers to an involuntary, repetitive, sustained, painful and twisting movements of the affected body part. This movement disorder was first described in 1911 by Hermain Oppenheim, and many studies have been conducted to understand the mechanism, the diagnosis and the treatment of dystonia ever since. However, there are still many unexplained aspects of this phenomenon. Dystonia is diagnosed by clinical manifestations, and various classifications are recommended for the diagnosis and the treatment. Anatomic classification, which is based on the muscle groups involved, is the most helpful classification model to plan the course of the treatment. Dystonias can also be classified based on the age of onset and the cause. These dystonic syndromes can be present without an identified etiology or they can be clinical manifestations of a neurodegenerative or neurometabolic disease. In this review we summarized the differential diagnosis, definition, classifications, possible mechanisms and treatment choices of dystonia.

scholarly journals Reassessing the Role of the Active TGF-β1 as a Biomarker in Systemic Sclerosis: Association of Serum Levels with Clinical Manifestations

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Andréa Tavares Dantas ◽  
Sayonara Maria Calado Gonçalves ◽  
Anderson Rodrigues de Almeida ◽  
Rafaela Silva Guimarães Gonçalves ◽  
Maria Clara Pinheiro Duarte Sampaio ◽  
...  
Keyword(s):  
Systemic Sclerosis ◽  
Clinical Manifestations ◽  
Serum Levels ◽  
Vascular Involvement ◽  
Digital Ulcers ◽  
Serum Samples ◽  
Peripheral Blood Mononuclear ◽  
Pbmc Culture ◽  
Significant Difference ◽  
Skin Biopsies

Objective. To determine active TGF-β1 (aTGF-β1) levels in serum, skin, and peripheral blood mononuclear cell (PBMC) culture supernatants and to understand their associations with clinical parameters in systemic sclerosis (SSc) patients.Methods. We evaluated serum samples from 56 SSc patients and 24 healthy controls (HC). In 20 SSc patients, we quantified spontaneous or anti-CD3/CD28 stimulated production of aTGF-β1 by PBMC. The aTGF-β1 levels were measured by ELISA. Skin biopsies were obtained from 13 SSc patients and six HC, and TGFB1 expression was analyzed by RT-PCR.Results. TGF-β1 serum levels were significantly higher in SSc patients than in HC (p< 0.0001). Patients with increased TGF-β1 serum levels were more likely to have diffuse subset (p= 0.02), digital ulcers (p= 0.02), lung fibrosis (p< 0.0001), positive antitopoisomerase I (p= 0.03), and higher modified Rodnan score (p= 0.046). Most of our culture supernatant samples had undetectable levels of TGF-β1. No significant difference in TGFB1 expression was observed in the SSc skin compared with HC skin.Conclusion. Raised active TGF-β1 serum levels and their association with clinical manifestations in scleroderma patients suggest that this cytokine could be a marker of fibrotic and vascular involvement in SSc.

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