A perforated sigmoid colon cancer within an inguinal hernia is extremely rare. This unexpected finding is usually discovered during surgery and causes an unavoidable septic evolution. Here, we describe the case of an 84-year-old man who presented with fever, abdominal distension, and a painful, enlarged, left scrotum. A CT showed a left, incarcerated, inguinal hernia containing a perforated sigmoid adenocarcinoma (which was confirmed by histopathology). The possibility of an irreducible inguinal hernia in association with perforated sigmoid colon cancer should be considered in the array of diagnoses. A pre-operative CT scan would be helpful in facilitating an accurate diagnosis.
Adenocarcinoma/complications/*radiography/surgery ; Aged, 80 and over ; Colon, Sigmoid/radiography/surgery ; Diagnosis, Differential ; Fatal Outcome ; Fever/etiology ; Hernia, Inguinal/complications/*radiography/surgery ; Humans ; Intestinal Perforation/complications/*radiography/surgery ; Male ; Pain/etiology ; Shock, Septic/complications ; Sigmoid Neoplasms/complications/*radiography/surgery ; Tomography, X-Ray Computed

The radiological appearance of diffuse discrete pulmonary nodules associated with cryptogenic organizing pneumonia (COP) has been rarely described. We describe a case of COP in 49-year-old woman with acute myeloid leukemia who developed diffuse pulmonary nodules during the second course of induction chemotherapy. The clinical status of the patient and imaging findings suggested the presence of a pulmonary metastasis or infectious disease. A video-assisted thoracoscopic lung biopsy resulted in the unexpected diagnosis of COP as an isolated entity. Steroid therapy led to dramatic improvement of the clinical symptoms and the pulmonary lesions.
Cryptogenic Organizing Pneumonia/complications/*radiography ; Diagnosis, Differential ; Female ; Humans ; Leukemia, Myeloid, Acute/*complications/pathology ; Lung/*radiography ; Lung Neoplasms/radiography/secondary ; Middle Aged ; Multiple Pulmonary Nodules/complications/*radiography

OBJECTIVE: We wanted to assess the usefulness of four-dimensional (4D) ultrasonography (US), i.e., real-time three-dimensional US, as an adjunct for performing various US-guided interventional procedures in superficial lesions. MATERIALS AND METHODS: Thirty-three patients were referred for US-guided interventional procedures for superficial lesions, including core biopsy in 19, fine-needle aspiration in eight, therapeutic drug injection in four and needle puncture in two. The procedures were performed under 4D US guidance. We reviewed the pathologic/cytologic results of the core biopsies or needle aspirations, and also the outcomes of drug injection or needle puncture. RESULTS: For all the patients who underwent 4D US-guided core biopsy, the specimens were adequate for making the pathological diagnosis, and specimens were successfully obtained for those patients who underwent 4D US-guided aspiration. The patients treated with 4D US-guided therapeutic drug injection or needle puncture had a good response. No major procedure-related complications occurred. The procedural times were similar to those procedural times with using two-dimensional US. CONCLUSION: Combining the two dimensional and 4D US techniques aids the physician when performing US-guided interventional procedures for the superficial lesions.
Ultrasonography, Interventional/*methods ; Punctures/*methods ; Neoplasms/pathology ; Middle Aged ; Male ; Injections/*methods ; *Imaging, Three-Dimensional ; Humans ; Female ; Aged, 80 and over ; Aged ; Adult ; Adolescent

Background: The demand for aesthetic procedures continues to grow globally, particularly in East Asian countries. The popularity of specific aesthetic procedures varies, however, depending on the particular East Asian geographical region being studied. This study aimed to evaluate the experiences of and attitudes toward aesthetic procedures in five East Asian countries/regions, including China, Japan, South Korea, Hong Kong, and Taiwan. Methods: To recruit participants, an online questionnaire was designed and distributed on social media networks between May 2015 and March 2016. The statistical analysis was conducted using SPSS software, version 22.0. Results: A total of 3,088 people responded (approximately 600 in each country/region). Of these, 940 participants (47.8%) responded that they had experienced at least one aesthetic procedure in the past. Taiwan had the highest number of participants who had experienced at least one procedure (264/940, 41%), with primarily non-surgical experiences. Only in South Korea did surgical cosmetic experiences exceed non-surgical cosmetic experiences (55.9% vs. 44.1%). The popularity of particular procedures and the motivation for undergoing aesthetic procedures varied by country. Conclusions The popularity of aesthetic procedures continues to evolve. Similar trends were observed across the East Asian regions; however, each country had its unique demands and preferences. The information provided by this study can help aesthetic plastic surgeons further understand the patients in their corresponding region, customize their practice, and develop the requisite skills.

PURPOSE: To compare the clinical and computed tomography (CT) appearances of liver abscesses caused by non-Klebsiella pneumoniae bacterial pathogens in elderly and nonelderly patients. MATERIALS AND METHODS: Eighty patients with confirmed non-Klebsiella pneumoniae liver abscesses (non-KPLAs) were enrolled and divided into two age groups: elderly (age > or =65 years, n=42) and nonelderly (age <65 years, n=38). Diagnosis of non-KPLA was established by pus and/or blood culture. We compared clinical presentations, outcomes, and CT characteristics of the two groups, and performed multivariate analysis for significant variables and receiver-operating-characteristic analysis to determine the cutoff value of abscess diameter for predicting non-KPLA. RESULTS: Elderly patients with non-KPLA were associated with a longer hospital stay (p<0.01). Regarding etiology, biliary sources had a strong association in the elderly group (p<0.01), and chronic liver diseases were related to the nonelderly group (p<0.01). Non-KPLAs (52.5%) tended to show a large, multiloculated appearance in the elderly group and were associated with bile duct dilatation (p<0.01), compared with the nonelderly group. The abscess diameter (cutoff value, 5.2 cm; area under the curve, 0.78) between the two groups was predicted. In multivariate analysis, underlying biliary tract disease [odds ratio (OR), 3.58, p<0.05], abscess diameter (OR, 2.40, p<0.05), and multiloculated abscess (OR, 1.19, p<0.01) independently predicted elderly patients with non-KPLA. CONCLUSION: In the elderly patients with non-KPLA, a large, multiloculated abscess with a diameter greater than 5.2 cm was the predominant imaging feature.
Adult ; Aged ; Aged, 80 and over ; Bacterial Infections/*complications/*radiography ; Female ; Humans ; Klebsiella Infections/microbiology ; Klebsiella pneumoniae ; Length of Stay ; Liver Abscess/complications/microbiology/*radiography ; Logistic Models ; Male ; Microscopy ; Middle Aged ; Multivariate Analysis ; ROC Curve ; Retrospective Studies ; Tomography, X-Ray Computed/*methods

PURPOSE: To compare the clinical and computed tomography (CT) appearances of liver abscesses caused by non-Klebsiella pneumoniae bacterial pathogens in elderly and nonelderly patients. MATERIALS AND METHODS: Eighty patients with confirmed non-Klebsiella pneumoniae liver abscesses (non-KPLAs) were enrolled and divided into two age groups: elderly (age > or =65 years, n=42) and nonelderly (age <65 years, n=38). Diagnosis of non-KPLA was established by pus and/or blood culture. We compared clinical presentations, outcomes, and CT characteristics of the two groups, and performed multivariate analysis for significant variables and receiver-operating-characteristic analysis to determine the cutoff value of abscess diameter for predicting non-KPLA. RESULTS: Elderly patients with non-KPLA were associated with a longer hospital stay (p<0.01). Regarding etiology, biliary sources had a strong association in the elderly group (p<0.01), and chronic liver diseases were related to the nonelderly group (p<0.01). Non-KPLAs (52.5%) tended to show a large, multiloculated appearance in the elderly group and were associated with bile duct dilatation (p<0.01), compared with the nonelderly group. The abscess diameter (cutoff value, 5.2 cm; area under the curve, 0.78) between the two groups was predicted. In multivariate analysis, underlying biliary tract disease [odds ratio (OR), 3.58, p<0.05], abscess diameter (OR, 2.40, p<0.05), and multiloculated abscess (OR, 1.19, p<0.01) independently predicted elderly patients with non-KPLA. CONCLUSION: In the elderly patients with non-KPLA, a large, multiloculated abscess with a diameter greater than 5.2 cm was the predominant imaging feature.
Adult ; Aged ; Aged, 80 and over ; Bacterial Infections/*complications/*radiography ; Female ; Humans ; Klebsiella Infections/microbiology ; Klebsiella pneumoniae ; Length of Stay ; Liver Abscess/complications/microbiology/*radiography ; Logistic Models ; Male ; Microscopy ; Middle Aged ; Multivariate Analysis ; ROC Curve ; Retrospective Studies ; Tomography, X-Ray Computed/*methods

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.

Purpose: Biomarkers predicting clinically significant prostate cancer (sPC) before biopsy are currently lacking. This study aimed to develop a non-invasive urine test to predict sPC in at-risk men using urinary metabolomic profiles. Materials and Methods: Urine samples from 934 at-risk subjects and 268 treatment-naïve PC patients were subjected to liquid chromatography/mass spectrophotometry (LC-MS)-based metabolomics profiling using both C18 and hydrophilic interaction liquid chromatography (HILIC) column analyses. Four models were constructed (training cohort [n=647]) and validated (validation cohort [n=344]) for different purposes. Model I differentiates PC from benign cases. Models II, III, and a Gleason score model (model GS) predict sPC that is defined as National Comprehensive Cancer Network (NCCN)-categorized favorable-intermediate risk group or higher (Model II), unfavorable-intermediate risk group or higher (Model III), and GS ≥7 PC (model GS), respectively. The metabolomic panels and predicting models were constructed using logistic regression and Akaike information criterion. Results: The best metabolomic panels from the HILIC column include 25, 27, 28 and 26 metabolites in Models I, II, III, and GS, respectively, with area under the curve (AUC) values ranging between 0.82 and 0.91 in the training cohort and between 0.77 and 0.86 in the validation cohort. The combination of the metabolomic panels and five baseline clinical factors that include serum prostate-specific antigen, age, family history of PC, previously negative biopsy, and abnormal digital rectal examination results significantly increased AUCs (range 0.88–0.91). At 90% sensitivity (validation cohort), 33%, 34%, 41%, and 36% of unnecessary biopsies were avoided in Models I, II, III, and GS, respectively. The above results were successfully validated using LC-MS with the C18 column. Conclusions Urinary metabolomic profiles with baseline clinical factors may accurately predict sPC in men with elevated risk before biopsy.