•CT-based radiomics with machine learning classifier is able to accurately predict primary refractory Diffuse Large B Cell Lymphomas (DLBCL).•The radiomics model exhibits a better discrimination for ...refractory DLBCL identification compared to available standard clinical criteria.
Biomarkers which can identify Diffuse Large B-Cell Lymphoma (DLBCL) likely to be refractory to first-line therapy are essential for selecting this population prior to therapy initiation to offer alternate therapeutic options that can improve prognosis. We tested the ability of a CT-based radiomics approach with machine learning to predict Primary Treatment Failure (PTF)-DLBCL from initial imaging evaluation. Twenty-six refractory patients were matched to 26 non-refractory patients, yielding 180 lymph nodes for analysis. Manual 3D delineation of the total node volume was performed by two independent readers to test the reproducibility. Then, 1218 hand-crafted radiomic features were extracted. The Random Forests machine learning approach was used as a classifier for constructing the prediction models. Seventy percent of the nodes were randomly assigned to a training set and the remaining 30% were assigned to an independent test set. The final model was tested on the dataset from the 2 readers, showing a mean accuracy, sensitivity and specificity of 73%, 62% and 82%, respectively, for distinguishing between refractory and non-refractory patients. The area under the receiver operating characteristic curve (AUC) was 0.83 and 0.79 for the two readers. We conclude that machine learning CT-based radiomics analysis is able to identify a priori PTF-DLBCL with a good accuracy.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Introduction
Approximately 15% of diffuse large B-cell lymphomas (DLBCL) do not respond to R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine and prednisone) or equivalent regimen. These ...primary refractory cases (prDLBCL) have a particularly poor survival. There are currently no reliable biomarkers to a priori identify prDLBCL patients and include them in clinical trials, while avoiding needless toxicity from predictably ineffective therapy. In this study, we evaluated the potential for radiomic analysis with machine learning for predicting prDLBCL.
Method
This study included adult patients with prDLBCL from a single institution from 2009 to 2018, who had first-line treatment with an R-CHOP like regimen, had never received systemic treatment for indolent lymphoma, and who had a CT scan at the time of diagnosis. Refractory (R) patients were defined by progression of disease (PD) after completion of at least one cycle, or failure to achieve a complete response (CR) after at least 4 cycles, as per Lugano criteria (Cheson, JCO 2014). Non-refractory (NR) patients were matched 1:1 on sex and R-IPI for the comparison group. Enlarged lymph nodes (≥1.5 cm in greatest diameter) were eligible for evaluation. The 6 largest nodes were selected at each node site (abdomen, chest, axilla and neck) and for each node category (refractory node (RN), partial response (PR) and CR, as per Lugano criteria).
3D Slicer software was used for the delineation of the region of interest (ROI) either for subsequent 2D analysis (largest axial section) or 3D analysis (total node volume). Each node was manually contoured by two independent readers and also was reviewed by an experienced senior oncologic radiologist. A total of 788 and 1218 features were extracted from 2D and 3D regions of interest, respectively, using Pyradiomics open source software.
Two independent machine learning approaches, Random Forests (RF) and Support Vector Machine (SVM), were tested for constructing the prediction models. 70% of cases were randomly assigned to the training set and 30% to the independent testing set. In the node model (NM) each independent node's response to treatment was predicted. In the patient model (PM), groups of nodes per site (abdomen, chest, axilla and neck) were used to predict the overall patient response.
Results
A total of 26 refractory patients were identified with a total 149 nodes (RN=55, PR=20, CR=74) and matched to 26 NR patients for comparison, with a total of 105 CR nodes. Seventeen nodes with significant artifact were excluded from the analysis (7 from NR patients and 10 from R patients).
RF had consistently superior performance compared to SVM and was used for constructing the final prediction models. Furthermore, 2D radiomic analysis had superior performance compared to 3D radiomic analysis. In the independent testing (prediction) set, the mean accuracy between the 2 readers for this model for distinguishing a R from NR patient was 80% (mean sensitivity and specificity, 73% and 88%, respectively). This model was able to predict a R patient (positive predictive value (PPV)) in 100% and 71% of the case, respectively for readers 1 and 2. The area under the ROC curve (AUC) was 0.96 and 0.81 for reader 1 and 2, respectively (Figure 1A).
For performance of the radiomic model for distinguishing individual refractory from responsive nodes, the independent testing set had a mean accuracy of 75% (mean sensitivity, specificity, PPV, and NPV of 80%, 69%, 78%, and 71% respectively). The AUC per reader were 0.82 and 0.85 (Figure 1B).
Conclusion
We demonstrate that the use of CT radiomic analysis with machine learning for identifying a priori primary refractory DLBCL patients is feasible. These models provide a relatively high prediction accuracy, which currently cannot be done in the clinical setting based on standard, largely qualitative, imaging characteristics.
The main limitations of our study include small patient numbers in this pilot study and exclusion of extranodal sites. The next step for this project would be to evaluate this approach in a larger cohort that includes a second independent institution. CT-based radiomics is promising and should be further explored to achieve this unmet need for predicting prDLBCL prior to therapy initiation.
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Forghani:GE Healthcare: Consultancy, Honoraria, Research Funding; 4Intel Inc: Equity Ownership, Membership on an entity's Board of Directors or advisory committees, Other: Founder. Reinhold:FRQS: Other: FRQS Grant. Assouline:Pfizer: Consultancy, Honoraria, Speakers Bureau; Janssen: Consultancy, Honoraria, Speakers Bureau; Abbvie: Consultancy, Honoraria; F. Hoffmann-La Roche Ltd: Consultancy, Honoraria.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
In this article,a novel unordered classification rule list discovery algorithm is presented based on Ant Colony Optimization（ACO）. The proposed classifier is compared empirically with two other ...ACO-based classification techniques on 26 data sets,selected from miscellaneous domains,based on several performance measures. As opposed to its ancestors,our technique has the flexibility of generating a list of IF-THEN rules with unrestricted order. It makes the generated classification model more comprehensible and easily interpretable.The results indicate that the performance of the proposed method is statistically significantly better as compared with previous versions of AntMiner based on predictive accuracy and comprehensibility of the classification model.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Polymers/ZnO nanocomposites have been receiving great interest due to their wide range of applications in optical devices. So, there is an essential need to enhance their optical properties. In this ...work, nanocomposites of four types of polymer and ZnO nanoparticles have been prepared as flexiable foil by using the casting method. Poly (methyl methacrylate) (PMMA), poly (vinylidene fluoride) (PVDF), polyvinyl alcohol (PVA), and polystyrene (PS) are used as polymer matrix while different concentrations of ZnO nanoparticles are used as filler. The analysis of energy dispersive X-ray (EDX) is satisfied of the high purity of as-prepared samples. UV-visible transmittance spectra have shown low transmittance in UV region which is inversely proportional with the concentration of ZnO nanoparticles. Linear absorption coefficient (α) has shown the presence of absorption edges. The energy gap is calculated, it is noticed that the optical band gap of all nanocomposites are red shifted. The values of the energy gap for all samples decreased with the increase of the weight percentage of ZnO nanoparticles in nanocomposites. However, the decrement in nanocomposites samples is different.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This research work focuses on a Near-Infra-Red (NIR) finger-images-based multimodal biometric system based on Finger Texture and Finger Vein biometrics. The individual results of the biometric ...characteristics are fused using a fuzzy system, and the final identification result is achieved. Experiments are performed for three different databases, i.e., the Near-Infra-Red Hand Images (NIRHI), Hong Kong Polytechnic University (HKPU) and University of Twente Finger Vein Pattern (UTFVP) databases. First, the Finger Texture biometric employs an efficient texture feature extracting algorithm, i.e., Linear Binary Pattern. Then, the classification is performed using Support Vector Machine, a proven machine learning classification algorithm. Second, the transfer learning of pre-trained convolutional neural networks (CNNs) is performed for the Finger Vein biometric, employing two approaches. The three selected CNNs are AlexNet, VGG16 and VGG19. In Approach 1, before feeding the images for the training of the CNN, the necessary preprocessing of NIR images is performed. In Approach 2, before the pre-processing step, image intensity optimization is also employed to regularize the image intensity. NIRHI outperforms HKPU and UTFVP for both of the modalities of focus, in a unimodal setup as well as in a multimodal one. The proposed multimodal biometric system demonstrates a better overall identification accuracy of 99.62% in comparison with 99.51% and 99.50% reported in the recent state-of-the-art systems.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
Blind source separation (BSS) is a problem that appears in many research fields. Fast Independent components analysis (FastICA) is one of the techniques to solve the problem. The researchers have ...verified the effectiveness of the technique through the offline analysis of the public datasets. The development of a real-time portable system involving such a computationally complex analysis requires an efficient hardware implementation of FastICA. A Field programmable gate array (FPGA) and an application-specific integrated circuit (ASIC) are two promising hardware platforms to implement FastICA. This work proposes a new method, called ALgebraic Jacobi Method (ALJM), for performing eigenvalue decomposition (EVD) required for the implementation of FastICA. We use a simplification, a polynomial approximation, and the Newton-Raphson method for calculating the Jacobi rotation. In this way, we ensure hardware reusability between the EVD stage and the weight vector estimation (WVE) stage of FastICA which reduces the computational complexity and the power consumption, without compromising its computation speed. We evaluate the ALJM-based FastICA by performing BSS on the linear mixtures of the deterministic and the random signals and comparing the performance results with the existing methods. After verifying its functionality and numerical stability, we propose a scalable systolic processing array (SPA) for the ALJM-based FastICA and implement it on Spartan-6 FPGA. By comparing the existing implementations of FastICA, in terms of speed, area, and power, we conclude that the ALJM-based FastICA is one of the most efficient methods for prototyping and commercializing a real-time portable system comprising FastICA.
The linear and nonlinear optical properties of polymer/inorganic nanocomposites have received a great interest because of their potential application such as optical limiting devices. A flexible foil ...like polymer/ZnO nanocomposites and polymer/ZnO/CuO nanocomposites have been prepared via casting method. ZnO and CuO nanoparticles were used as filler, while four different types of polymer were used as polymer matrix. The purity and composition of the nanocomposites were confirmed via EDX analysis and EDS mapping. Surface morphology of samples was tested by FESEM that were shown the dispersion of ZnO and CuO nanoparticles successfully. To study the influence of adding CuO nanoparticles on polymer/ZnO nanocomposites; the liner transmittance was measured and linear absorption coefficient was calculated. The results show a decrease in linear transmittance and increase in linear absorption coefficient when CuO nanoparticles was added. Then, the absorption coefficient and refractive index of the as-prepared sample were analysed using an open and closed aperture single beam Z-scan technique via Q-switched Nd-YAG pulse laser at 532 nm. The nonlinear refractive index was in the order of 10
−12
cm
2
/W with a negative sign whereas the nonlinear absorption coefficient was in the order of 10
−7
cm/W. The real part, imaginary part and the absolute value of the third order nonlinear optical susceptibility χ
(3)
were calculated. The χ
(3)
was in the order of 10
−6
esu. The effect of adding CuO nanoparticles to nanocomposites was enhanced their nonlinear optical properties. Consequently, a good optical limiting was obtained. The optical limiting threshold of the samples was measured. The results showed that the prepared nanocomposites can be considered as an excellent candidate for optical limiting devices, which clearly affected by the adding CuO nanoparticles and the type of polymer matrix. Nanocomposites PMMA/ZnO/CuO and PS/ZnO/CuO showed the low optical limiting threshold, which were equal to 60 and 50 Mw/cm
2
, respectively.
Background Depression and anxiety are common psychological conditions associated with polycystic ovarian syndrome (PCOS). It is important to understand the role of various demographic and ...socio-economic factors that contribute to the development of these psychological conditions. Objectives The aims of this study were to determine the prevalence of anxiety and depression in women with PCOS and to find the association of various demographic and socio-economic factors with anxiety and depression. Methods This was a single-center cross-sectional study conducted at a tertiary care hospital in Islamabad, Pakistan, from May 2021 to August 2022. All female patients, aged 18 to 40 years and diagnosed with PCOS, who presented to the department of Gynecology during the study period were eligible to be enrolled in the study. The Hospital Anxiety and Depression scale (HADS) was used to determine the level of anxiety and depression in the participants. HADS comprises 14 items scored on a Likert scale ranging from 0 to 3. Seven items correspond to depression and anxiety each. The scores range from 0 to 21 for both domains. A score of 7 or less was considered normal, 8-10 as borderline, and 11 or above as abnormal for both anxiety and depression. Data were analyzed using IBM SPSS Statistics for Windows, Version 26.0 (IBM Corp., Armonk, NY, USA). Results A total of 74 patients with PCOS were included in the study. The mean age of all the participants was 26.8 ± 5.2 and the mean body mass index (BMI) was 28.7 ± 5.4. The presence of PCOS-related symptoms was observed in all 74 cases. Menstrual cycle abnormalities were the most common symptom, which was present in 57 (77.0%) cases, followed by weight gain, which was present in 50 (67.6%) cases, and hirsutism, which was present in 41 (55.4%) cases. Diabetes mellitus and hypertension were present only in three (4.1%) and two (2.7%) cases, respectively, and positive family history of depression and/or anxiety was reported by 20 (27%) cases. The mean HAD score was 7 ± 3.8 for depression and 8 ± 3.7 for anxiety. Depression was diagnosed in 13 (17.6%) cases, and anxiety was diagnosed in 15 (20.3%) cases. Depression was found to be significantly associated with BMI (p = 0.015), level of education (p = 0.033), and monthly household income (p = 0.004). Anxiety was found to be associated with employment status (p = 0.009) and current pregnancy (p = 0.007). Rest of the factors such as age, marital status, ethnicity, menstrual irregularities, comorbidities such as diabetes mellitus and hypertension, and a family history of PCOS, anxiety, or depression did not show statistically significant association with either anxiety or depression (p < 0.05). Conclusion Anxiety and depression are common in patients with PCOS. These psychological conditions are associated with various demographic and socio-economic factors such as BMI, level of education, monthly household income, employment status, and pregnancy. It is recommended to involve a multidisciplinary team while managing patients with PCOS to timely identify and treat these psychological conditions in these patients.
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