The role of radiomics in the preoperative diagnosis of renal cell carcinoma with sarcomatoid dedifferentiation (sRCC).

621 Background: sRCC is an aggressive renal malignancy, with poor survival and limited response to therapy. Preoperative identification of sRCC would be helpful for counselling patients, and clinical trial enrollment. This study aims at assessing the potential of radiomics to discriminate clear cell sRCC from non-sarcomatoid clear cell RCC (nsRCC). Methods: The study included 49 sRCC and 41 nsRCC patients treated with surgery between 2007-2016, who had contrast-enhanced CT available. An experienced radiologist delineated the entire tumor using 3D Slicer ( http://www.slicer.org ). The extracted 3D region of interest was imported in our in-house radiomic pipeline. A total of 310 features (10 histogram-based and 300 second-order features) were calculated. Second-order radiomic features were calculated using the Grey Level Cooccurrence Matrix (GLCM) and 20 Haralick features were obtained from the GLCM. To account for directionality, the mean, variance and range of the features across different directions were calculated. Finally, different number of gray levels were also considered in the analysis (N = 8, 16, 32, 64, 256). Core features were obtained using a feature selection based on Least Absolute Shrinkage and Selection Operator (LASSO). Selected features were used to build a classification model for prediction of sRCC versus nsRCC (XGboost). To evaluate the robustness of the estimates, Leave One Out Cross-Validation (LOOCV) was conducted on the patient set. Results: Overall, median tumor size was 10.0 cm and most patients had pT3a (68%). There was no significant difference of age, gender, race, tumor size and stages between sRCC and nsRCC cohorts. The prediction of sRCC using LOOCV was significant with p-value < 0.0001. Area under the curve, sensitivity, and specificity for identification of sRCC were 96.8%, 92.6% and 93.8% respectively. Conclusions: This study demonstrates that CT radiomic features can accurately discriminate between sRCC and nsRCC. The proposed tool has the potential to advance clinical management strategies. In addition to being noninvasive, this methodology can be applied to scans obtained during routine clinical care. Further external validation is warranted.

Download Full-text

Meta-analysis of upfront VEGF targeted therapy prior to nephrectomy in metastatic clear cell renal cancer.

Journal of Clinical Oncology ◽

10.1200/jco.2017.35.6_suppl.514 ◽

2017 ◽

Vol 35 (6_suppl) ◽

pp. 514-514

Author(s):

Akhila Ganeshi Wimalasingham ◽

Alfonso Gomez De Liano Lista ◽

Roderick de Bruijn ◽

John B. A. G. Haanen ◽

Bernadett Szabados ◽

...

Keyword(s):

Renal Cancer ◽

Clear Cell ◽

Meta Analysis ◽

Primary Tumour ◽

Performance Status ◽

Response To Therapy ◽

Intermediate Risk ◽

Poor Risk ◽

Significant Difference ◽

Cell Renal Cancer

514 Background: The safety and efficacy of upfront VEGF targeted, before nephrectomy in metastatic clear cell renal cancer (mCCRC) has not been robustly evaluated. Methods: In this study we performed a meta-analysis of 3 studies (NCT) with an almost identical design and included a single institution experience (which adopted this approach as a standard). Patients with newly diagnosed mCCRC had 12-18 weeks of sunitinib or pazopanib therapy prior to planned cytoreductive nephrectomy (CN). Results: 224 patients were included in this analysis (54% had sunitinib and 46% pazopanib). Overall, 73% had MSKCC intermediate risk and 23% poor risk disease. 20% of patients had an ECOG performance status of 0.84% of patients obtained stable disease or a response to therapy (by RECIST) before surgery. The median reduction of size of the primary tumour was 14%. 60% of patients had CN. The commonest reason for not performing CN was progression of disease. Progression free survival (PFS) and overall survival (OS) was 6.2 (95% CI 5.7-6.7) and 13 (95% CI: 10.2-15.7) respectively. Patients with MSKCC poor risk disease had a poor outcome irrespective of CN (OS = 7.5 months 95% CI 5.8-9.2). A comparison of sunitinib and pazopanib showed no significant difference in median PFS 7.1 (95% CI, 6.0-9.2) and 6.0 (95% CI: 5.1-6.8) or surgical complications (p<0.05). Conclusions: Outcomes with this approach are in line with expected survival for this population. Results with sunitinib and pazopanib were similar. This approach is attractive for patients with MSKCC intermediate risk disease.

Download Full-text

A radiomics-based model for predicting prognosis of locally advanced gastric cancer in the preoperative setting

Scientific Reports ◽

10.1038/s41598-021-81408-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jaeseung Shin ◽

Joon Seok Lim ◽

Yong-Min Huh ◽

Jie-Hyun Kim ◽

Woo Jin Hyung ◽

...

Keyword(s):

Gastric Cancer ◽

Advanced Gastric Cancer ◽

Cox Regression ◽

Characteristic Curve ◽

External Validation ◽

Locally Advanced ◽

Clinical Model ◽

Locally Advanced Gastric Cancer ◽

Contrast Enhanced Ct ◽

Preoperative Ct

AbstractThis study aims to evaluate the performance of a radiomic signature-based model for predicting recurrence-free survival (RFS) of locally advanced gastric cancer (LAGC) using preoperative contrast-enhanced CT. This retrospective study included a training cohort (349 patients) and an external validation cohort (61 patients) who underwent curative resection for LAGC in 2010 without neoadjuvant therapies. Available preoperative clinical factors, including conventional CT staging and endoscopic data, and 438 radiomic features from the preoperative CT were obtained. To predict RFS, a radiomic model was developed using penalized Cox regression with the least absolute shrinkage and selection operator with ten-fold cross-validation. Internal and external validations were performed using a bootstrapping method. With the final 410 patients (58.2 ± 13.0 years-old; 268 female), the radiomic model consisted of seven selected features. In both of the internal and the external validation, the integrated area under the receiver operating characteristic curve values of both the radiomic model (0.714, P < 0.001 [internal validation]; 0.652, P = 0.010 [external validation]) and the merged model (0.719, P < 0.001; 0.651, P = 0.014) were significantly higher than those of the clinical model (0.616; 0.594). The radiomics-based model on preoperative CT images may improve RFS prediction and high-risk stratification in the preoperative setting of LAGC.

Download Full-text

Testing telediagnostic thyroid ultrasound in Peru: a new horizon in expanding access to imaging in rural and underserved areas

Journal of Endocrinological Investigation ◽

10.1007/s40618-021-01584-7 ◽

2021 ◽

Author(s):

T. J. Marini ◽

S. L. Weiss ◽

A. Gupta ◽

Y. T. Zhao ◽

T. M. Baran ◽

...

Keyword(s):

Thyroid Nodule ◽

Standard Of Care ◽

The United States ◽

Epidemiological Studies ◽

Thyroid Ultrasound ◽

Target Region ◽

Experienced Radiologist ◽

Significant Difference ◽

Size Evaluation

Abstract Purpose Thyroid ultrasound is a key tool in the evaluation of the thyroid, but billions of people around the world lack access to ultrasound imaging. In this study, we tested an asynchronous telediagnostic ultrasound system operated by individuals without prior ultrasound training which may be used to effectively evaluate the thyroid and improve access to imaging worldwide. Methods The telediagnostic system in this study utilizes volume sweep imaging (VSI), an imaging technique in which the operator scans the target region with simple sweeps of the ultrasound probe based on external body landmarks. Sweeps are recorded and saved as video clips for later interpretation by an expert. Two operators without prior ultrasound experience underwent 8 h of training on the thyroid VSI protocol and the operation of the telemedicine platform. After training, the operators scanned patients at a health center in Lima. Telediagnostic examinations were sent to the United States for remote interpretation. Standard of care thyroid ultrasound was performed by an experienced radiologist at the time of VSI examination to serve as a reference standard. Results Novice operators scanned 121 subjects with the thyroid VSI protocol. Of these exams, 88% were rated of excellent image quality showing complete or near complete thyroid visualization. There was 98.3% agreement on thyroid nodule presence between VSI teleultrasound and standard of care ultrasound (Cohen’s kappa 0.91, P < 0.0001). VSI measured the thyroid size, on average, within 5 mm compared to standard of care. Readers of VSI were also able to effectively characterize thyroid nodules, and there was no significant difference in measurement of thyroid nodule size (P = 0.74) between VSI and standard of care. Conclusion Thyroid VSI telediagnostic ultrasound demonstrated both excellent visualization of the thyroid gland and agreement with standard of care thyroid ultrasound for nodules and thyroid size evaluation. This system could be deployed for evaluation of palpable thyroid abnormalities, nodule follow-up, and epidemiological studies to promote global health and improve the availability of diagnostic imaging in underserved communities.

Download Full-text

Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections

Scientific Reports ◽

10.1038/s41598-020-78129-0 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Young-Gon Kim ◽

Sungchul Kim ◽

Cristina Eunbee Cho ◽

In Hye Song ◽

Hee Jin Lee ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Transfer Learning ◽

Frozen Section ◽

Medical Center ◽

External Validation ◽

Model Performance ◽

Classification Model ◽

Training Dataset

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.

Download Full-text

Adrenal incidentalomas: imaging challenges—role of MDCT scan versus MRI in evaluating adrenal incidentalomas

Egyptian Journal of Radiology and Nuclear Medicine ◽

10.1186/s43055-021-00437-w ◽

2021 ◽

Vol 52 (1) ◽

Author(s):

Monica Maher Amin Nawar ◽

Sameh Abdel Aziz Zaky Hanna ◽

Shereen Sadik El-Sawy ◽

Sally Yehia Shokralla

Keyword(s):

Significant Risk ◽

Adrenal Incidentaloma ◽

Adrenal Incidentalomas ◽

Specific Patient ◽

Contrast Enhanced Ct ◽

Contrast Enhanced ◽

Enhanced Ct ◽

Significant Difference ◽

Adrenal Masses

Abstract Background The term adrenal incidentaloma (AI), by definition, is an adrenal mass that is unexpectedly detected through an imaging procedure performed for reasons unrelated to adrenal dysfunction or suspected dysfunction. Despite their frequent appearance, the challenge remains in recognizing and treating the small percentage of AI that poses a significant risk, either because of their hormonal activity or because of their malignant histology. The aim of this study is to study the role of MRI, specifically chemical shift imaging (CSI), against various MDCT scans (non-enhanced, enhanced, and delayed) in the characterization of incidentally discovered adrenal masses to offer a way for the patients to avoid unnecessary time and money-wasting imaging modalities used to reach a diagnosis of their incidentally discovered adrenal lesions. We examined a total number of 20 patients with total of 22 adrenal lesions. The mean age was 51.1 ± 15.27. Results In our study, we found that among CT parameters, APW and RPW showed the highest sensitivity and specificity for detection of lipid-rich adenomas. CSI has also proven to be the best MR technique. However, there is no statistically significant difference in the diagnostic capability of CSI versus the CT washout technique. Both modalities could be conducted, according to specific patient preferences and/or limitations, with comparable highly accurate outcomes. Conclusion This study demonstrates that a similar diagnostic outcome is obtained from contrast-enhanced CT (CECT) and MRI with CSI of adrenal lesions.

Download Full-text

Modification and Validation of the Phosphate Removal Model: A Multicenter Study

Kidney & Blood Pressure Research ◽

10.1159/000511375 ◽

2021 ◽

pp. 1-10

Author(s):

Weichen Zhang ◽

Qiuna Du ◽

Jing Xiao ◽

Zhaori Bi ◽

Chen Yu ◽

...

Keyword(s):

Polynomial Regression ◽

External Validation ◽

Original Model ◽

Phosphate Concentration ◽

Phosphate Removal ◽

Internal Validation ◽

Modified Model ◽

New Model ◽

Significant Difference ◽

Removal Model

Background: Our research group has previously reported a noninvasive model that estimates phosphate removal within a 4-h hemodialysis (HD) treatment. The aim of this study was to modify the original model and validate the accuracy of the new model of phosphate removal for HD and hemodiafiltration (HDF) treatment. Methods: A total of 109 HD patients from 3 HD centers were enrolled. The actual phosphate removal amount was calculated using the area under the dialysate phosphate concentration time curve. Model modification was executed using second-order multivariable polynomial regression analysis to obtain a new parameter for dialyzer phosphate clearance. Bias, precision, and accuracy were measured in the internal and external validation to determine the performance of the modified model. Results: Mean age of the enrolled patients was 63 ± 12 years, and 67 (61.5%) were male. Phosphate removal was 19.06 ± 8.12 mmol and 17.38 ± 6.75 mmol in 4-h HD and HDF treatments, respectively, with no significant difference. The modified phosphate removal model was expressed as Tpo4 = 80.3 × C45 − 0.024 × age + 0.07 × weight + β × clearance − 8.14 (β = 6.231 × 10−3 × clearance − 1.886 × 10−5 × clearance2 – 0.467), where C45 was the phosphate concentration in the spent dialysate measured at the 45th minute of HD and clearance was the phosphate clearance of the dialyzer. Internal validation indicated that the new model was superior to the original model with a significantly smaller bias and higher accuracy. External validation showed that R2, bias, and accuracy were not significantly different than those of internal validation. Conclusions: A new model was generated to quantify phosphate removal by 4-h HD and HDF with a dialyzer surface area of 1.3–1.8 m2. This modified model would contribute to the evaluation of phosphate balance and individualized therapy of hyperphosphatemia.

Download Full-text

Radiomics analysis for predicting pembrolizumab response in patients with advanced rare cancers

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-001752 ◽

2021 ◽

Vol 9 (4) ◽

pp. e001752

Author(s):

Rivka R Colen ◽

Christian Rolfo ◽

Murat Ak ◽

Mira Ayoub ◽

Sara Ahmed ◽

...

Keyword(s):

Partial Response ◽

Stable Disease ◽

Progressive Disease ◽

Tumor Response ◽

Complete Response ◽

Classification Model ◽

P Value ◽

Rare Cancer ◽

Rare Cancers ◽

Contrast Enhanced Ct

BackgroundWe present a radiomics-based model for predicting response to pembrolizumab in patients with advanced rare cancers.MethodsThe study included 57 patients with advanced rare cancers who were enrolled in our phase II clinical trial of pembrolizumab. Tumor response was evaluated using Response Evaluation Criteria in Solid Tumors (RECIST) 1.1 and immune-related RECIST (irRECIST). Patients were categorized as 20 “controlled disease” (stable disease, partial response, or complete response) or 37 progressive disease). We used 3D-slicer to segment target lesions on standard-of-care, pretreatment contrast enhanced CT scans. We extracted 610 features (10 histogram-based features and 600 second-order texture features) from each volume of interest. Least absolute shrinkage and selection operator logistic regression was used to detect the most discriminatory features. Selected features were used to create a classification model, using XGBoost, for the prediction of tumor response to pembrolizumab. Leave-one-out cross-validation was performed to assess model performance.FindingsThe 10 most relevant radiomics features were selected; XGBoost-based classification successfully differentiated between controlled disease (complete response, partial response, stable disease) and progressive disease with high accuracy, sensitivity, and specificity in patients assessed by RECIST (94.7%, 97.3%, and 90%, respectively; p<0.001) and in patients assessed by irRECIST (94.7%, 93.9%, and 95.8%, respectively; p<0.001). Additionally, the common features of the RECIST and irRECIST groups also highly predicted pembrolizumab response with accuracy, sensitivity, specificity, and p value of 94.7%, 97%, 90%, p<0.001% and 96%, 96%, 95%, p<0.001, respectively.ConclusionOur radiomics-based signature identified imaging differences that predicted pembrolizumab response in patients with advanced rare cancer.InterpretationOur radiomics-based signature identified imaging differences that predicted pembrolizumab response in patients with advanced rare cancer.

Download Full-text

Reanalysis and External Validation of a Decision Tree Model for Detecting Unrecognized Diabetes in Rural Chinese Individuals

International Journal of Endocrinology ◽

10.1155/2017/3894870 ◽

2017 ◽

Vol 2017 ◽

pp. 1-6 ◽

Cited By ~ 2

Author(s):

Zhong Xin ◽

Lin Hua ◽

Xu-Hong Wang ◽

Dong Zhao ◽

Cai-Guo Yu ◽

...

Keyword(s):

Decision Tree ◽

Predictive Value ◽

Early Stage ◽

Current Model ◽

External Validation ◽

Area Under The Curve ◽

Decision Tree Model ◽

Tree Model ◽

Chinese Adult ◽

Significant Difference

We reanalyzed previous data to develop a more simplified decision tree model as a screening tool for unrecognized diabetes, using basic information in Beijing community health records. Then, the model was validated in another rural town. Only three non-laboratory-based risk factors (age, BMI, and presence of hypertension) with fewer branches were used in the new model. The sensitivity, specificity, positive predictive value, negative predictive value, and area under the curve (AUC) for detecting diabetes were calculated. The AUC values in internal and external validation groups were 0.708 and 0.629, respectively. Subjects with high risk of diabetes had significantly higher HOMA-IR, but no significant difference in HOMA-B was observed. This simple tool will help general practitioners and residents assess the risk of diabetes quickly and easily. This study also validates the strong associations of insulin resistance and early stage of diabetes, suggesting that more attention should be paid to the current model in rural Chinese adult populations.

Download Full-text

An ensemble model for predicting therapeutic response to unfractionated heparin therapy (Preprint)

10.2196/preprints.34533 ◽

2021 ◽

Author(s):

Ahmad Abdel-Hafez ◽

Ian A. Scott ◽

Nazanin Falconer ◽

Stephen Canaris ◽

Oscar Bonilla ◽

...

Keyword(s):

Unfractionated Heparin ◽

Therapeutic Response ◽

Characteristic Curve ◽

External Validation ◽

Heparin Therapy ◽

Classification Model ◽

Target Range ◽

Inpatient Setting ◽

Episodes Of Care ◽

Multi Classification

BACKGROUND Unfractionated heparin (UFH), is an anticoagulant drug considered a high-risk medication in that an excessive dose can cause bleeding, while an insufficient dose can lead to a recurrent embolic event. Following initiation of intravenous (IV) UFH, the therapeutic response is monitored using a measure of blood clotting time known as the activated partial thromboplastin time (aPTT). Clinicians iteratively adjust the dose of UFH to a target aPTT range, with the usual therapeutic target range between 60 to 100 seconds. OBJECTIVE The aim of this study was to develop and validate a ML algorithm to predict, aPTT within 12 hours after a specified bolus and maintenance dose of UFH. METHODS This was a retrospective cohort study of 3273 episodes of care from January 2017 to August 2020 using data collected from electronic health records (EHR) of five hospitals in Queensland, Australia. Data from four hospitals were used to build and test ensemble models using cross validation, while the data from the fifth hospital was used for external validation. Modelling was performed using H2O Driverless AI® an automated ML tool, and 17 different experiments were conducted in an iterative process to optimise model accuracy. RESULTS In predicting aPTT, the best performing experiment produced an ensemble with 4x LightGBM models with a root mean square error (RMSE) of 31.35. This dataset was re-purposed as a multi-classification task (sub-therapeutic, therapeutic, and supra-therapeutic aPTT result) and achieved a 59.9% accuracy and area under the receiver operating characteristic curve (AUC) of 0.735. External validation yielded similar results: RMSE of 30.52 +/- 1.29 for the prediction model, and accuracy of 56.8% +/- 3.15 and AUC of 0.724 for the multi-classification model. CONCLUSIONS According to our knowledge, this is the first study of ML applied to IV UFH dosing that has been developed and externally validated in a multisite adult general medical inpatient setting. We present the processes of data collection, preparation, and feature engineering for purposes of replication.

Download Full-text

Study the Prevalence of Depression in Patients Attending Comprehensive Care Centre, Islamabad, Pakistan

Research and Analysis Journal ◽

10.18535/raj/v4i09.01 ◽

2021 ◽

Vol 4 (9) ◽

pp. 1-6

Author(s):

Mehak Nimra ◽

Sobia Yousaf ◽

Huma Naz ◽

Hira Nain ◽

Tahreem Shahid ◽

...

Keyword(s):

Alcohol Use ◽

Clinical Care ◽

Comprehensive Care ◽

P Value ◽

People Living With Hiv ◽

Care Centre ◽

Neuropsychiatric Complications ◽

Significant Difference ◽

Prevalence Of Depression ◽

Age Category

Abstract: Depression is one of the most common neuropsychiatric complications of HIV disease, and this leads to worse HIV-related health outcomes. With 350 million people affected worldwide, rates of depression are roughly two times greater in people living with HIV than in the general population. Objective: Determine prevalence of depression in patients attending Comprehensive Care Centre Shifa international Hospital, Islamabad Design: Descriptive cross-sectional quantitative study. Settings: Shifa international Hospital, Islamabad Comprehensive Care Centre, Methods: This data is from a bigger study ‘prevalence of alcohol use disorders and depression in patients attending Comprehensive Care Centre (CCC). The study population consisted of PLWHA attending the CCC. Two hundred and seventy-two (N=272) participants from CCC attendants were recruited. All consenting male and female aged 18-65 years were interviewed using the researcher’s designed questioner to collect their socio-demographic characteristics. Fully completed questionnaires were entered into excel sheets and analyzed using the Statistical Package for Social Sciences (SPSS) Version 20. Results: The overall prevalence of depression was 23.8%, with mild depression at 9.7%, moderate depression at 10.4% and severe depression accounting for 3.7%, respectively. Depression was associated with alcohol use (p=0.024). A significant difference between depression and age where depression levels worsens as age advances; respondents in age category of 18-21 years had less or no depression compared to those in the age category of 33 years and above. We found an association between depression and employment. Those laid-off work (1/3), and the retired (15%) had more depression compared to the employed (11%) or self-employed 6%, with a P value of 0.55 (borderline). On multivariate analysis severity of depression (OR=5.5, 95% CI of OR [2.1 –14.3], p<0.0001) was associated with male gender (OR=10, 95% CI of OR [3.6 –28.3], p<0.0001). Conclusion: The study findings indicate a high prevalence of depressive symptoms in patients attending the CCC. There is need to set-up appropriate interventions and strategies to reduce the prevalence of mental health disorders into routine HIV clinical care and support.

Download Full-text