dataesr/scientific-paragraphs-categorization huggingface dataset:
80% on the validation set, and 90. 00% on the testing set. This suggests that the ByPatch harmonization, combined with the previously selected optimal pipeline options, was most effective in reducing variability between patches and improving classification performance. In contrast, Configuration 6 and Configuration 8 for the IDC dataset yielded comparable results, with Configuration 8 exhibiting slightly better performance on the testing set (82. 00% compared to 81. 00% for Configuration 6).
Comparison of our results with existing literature After selecting the different blocks of the pipeline, we compared the results of our best models with recently published works using the feature engineering approach on the same datasets. In order to make the results comparable, we selected the models using the BreaKHis database with a ratio of 70% for model training and 30% for model testing with a zoom level of histopathological patches at x40 (Table  11 ). Four of our models obtained higher testing accuracy (90% -95. 3%) than the results published in recent works (ranging between 83. 9% and 89. 8%) and where the maximum achieved in  24  were 89. 8%. As shows Table  12 , Celtik et al. 2020  25  achieved 91. 57% balanced accuracy with their DenseNet-161 model and 90. 96% balanced accuracy using the ResNet-50 architecture on IDC data.
Soumya et al. , 2021 26 used a set of 782 features computed on the IDC dataset, followed by feature selection using Pearson's correlation coefficient to obtain a dataset with four features that are then used for classification and yielded the highest accuracy (92. 55%). Asare et al. 2020  27  , proposes a simple convolutional neural network model to distinguish benign and malignant breast cancer tumors in histopathological images in the IDC dataset by using different optimization algorithms and implementing several data augmentation techniques that regulate overfitting and improve the generalization ability of the proposed model. The accuracy, sensitivity, and specificity obtained were 89. 92%, 94. 02%, and 86. 42%, respectively. Also, choudhary et al. 2021  28  , based their models on the three popular pre-trained CNNs, VGG19, ResNet34 and ResNet50.
They obtain 91. 25% with VGG19 while with ResNet34, the accuracy increases slightly to 91. 80% and finally the best accuracy was obtained using the ResNet50 model with an accuracy of 92. 07%. The results obtained by our models show the importance of using the ComBatOutliers method coupled with the data balancing method. Indeed, with our model having the Config4 configuration, we obtain an average accuracy of 92. 7% on 20 splits and a maximum accuracy of 94. 7%. The advantage of this approach compared to deep learning, in Refs. 25, [27] [28] [29] [30] [31] [32] [33] [34] [35]  , is the interpretability of our models. Indeed, the different configurations used reveal the requirements of the features in terms of harmonization by a priori defined variability which was in our case the edge images with the central images, balancing between the numbers of malignant and benign patches and harmonization of atypical data within the database for a better classification.
Stability of the results In this section, the stability of the results obtained with 20 different random splits of the dataset into training/ validation sets and testing set was evaluated. The results showed that the model using StandardScaling for normalization and SMOTENN to reduce the imbalance had better stability, with a deviation from the mean of 0. 5%, 0. 6% and 1. 6% for training, validation, and testing, respectively (as shown in Table  9 ). This could be due to the fact that StandardScaling normalizes the data to have a mean of 0 and a standard deviation of 1, which helps the model to learn more efficiently and reduces the risk of overfitting. Additionally, SMOTENN not only balances the dataset by oversampling the minority class but also cleans up the noise and borderline samples using the edited nearest neighbors algorithm.
This makes the training data more representative and easier to learn. However, the model that obtained the best classification results had slightly lower stability. This model used StandardScaling, ComBatOutlier method to harmonize the atypical data, and SMOTENN to balance the database. The average classification percentages for this model were 99. 04%, 96. 91%, and 93. 4% for training, validation, and testing, respectively. The deviation from the mean was 0. 9%, 1%, and 1. 8% for training, validation, and testing, respectively. This may be due to the fact that the ComBatOutlier method helps to adjust the batch effect and remove outliers, thus improving the quality of the data. However, this process may also introduce some instability, as the method is sensitive to the choice of reference sample and the amount of data available for harmonization.
Multicenter models The second contribution presented in this paper concerns the potential improvement of the robustness of the models when applied to data of different origin. We have evaluated the performance of several models after a multicenter harmonization based on the criteria that allowed us to obtain the best results in our previous tests. Multicenter models are essential when dealing with data from multiple sources or centers, as the data can have different characteristics and distributions. Therefore, it is necessary to harmonize the data to ensure that the models perform well on all datasets. In this study, we have performed multicenter harmonization using the ComBat method and StandardScaling normalization. The comparison of the models (Table  13 ) highlighted that despite their good performance on their own database (87.
55% and 93. 26% respectively for IDC and BreaKHis), they do not perform well on data from the other database (58. 16% and 47. 20% respectively for IDC and BreaKHis). This demonstrates the clear need for an additional harmonization to improve the robustness of the model against external data. The use of ComBat method for harmonization has been shown to be effective in previous studies. ComBat is a batch-effect correction method that adjusts for systematic variation between batches while preserving biological variability. It has been successfully applied to various biological datasets, including gene expression and neuroimaging data. In our study, we have used ComBat to adjust for any differences in the datasets that could affect model performance. The best results obtained with multicenter harmonization were for the model that relied on StandardScaling method and outliers harmonization, with ComBat achieving 80% and 81% for the classification of patches from IDC and BreaKHis databases respectively.
This result suggests that the use of both ComBat and StandardScaling can improve the performance of the models on external data. StandardScaling normalization scales the data to have zero mean and unit variance, making the data more comparable between different datasets. As with the best models presented above for IDC and BreaKHis, we provided additional details regarding the performance of our best multicenter models (Table  14 ). These models were trained to handle the multicenter problem, where the models were tested on patches from multiple datasets (IDC and BreaKHis).
Discussion on our best model In our study, the optimal model configurations were identified as Configuration 6 (Standard Scaling, ComBat for Outlier Handling, and SMOTENN) for the IDC database, and Configuration 8 (Standard Scaling, ComBat for Outliers, SMOTENN, and Harmonisation by Patch) for the BreaKHis database. For BreaKHis, implementing Standard Scaling is pivotal to ensure all data attributes are on the same scale. The "ComBat for Outliers" method, as discussed in Section "Intra-database harmonization module", is crucial for aligning atypical samples to a reference cluster represented by non-atypical samples. Furthermore, the optimal data balancing method was determined to be SMOTENN, as elaborated in Section "Balancing". SMOTENN not only oversamples minority classes but also undersamples samples close to the majority class.
The final pre-classification step involves Harmonization by Patch, which separates border patches from central patches and harmonizes them using the ComBat method, thereby reducing variability between these two clusters. For the IDC database, the same configuration settings, excluding the "Harmonisation by Patch" step, yielded the best results. The significance of the "Harmonisation by Patch" module becomes particularly evident when dealing with datasets that exhibit high intra-database variability and a limited number of samples. Such characteristics are prevalent in the BreaKHis dataset but are less pronounced in the IDC dataset. This specialized harmonization step helps in mitigating the adverse effects of variability within the database, thereby improving the model's classification accuracy.
The necessity for this step in BreaKHis, but not in IDC, could be attributed to the sufficiently large sample size in the IDC dataset (277524 patches), which allows the model to adequately learn despite the existing variability between central and border patches. Insights into feature importance are presented in Fig. 7  through the use of SHAP (Shapley Additive Explanations). We observe that the separation into RGB channels of the histopathological slides plays a significant role, with a higher number of influential features in the red (5 out of 20 features) and green (6 features) channels, as opposed to only 3 features from the blue channel, which had lesser influence on the classification. Additionally, general features like autocorrelation on pixel sequences were found to have a strong influence on the classification, implying a strong relationship between sequences of similar pixels.
Limitations While our models have shown promising results, they also have several limitations that could be addressed in future iterations. One major drawback is the complexity of the model, which can make the classification process very time-consuming, particularly when dealing with large databases like IDCDB. To improve efficiency, we can explore various methods for harmonizing cluster patches, such as refining the harmonization process between patches and between patches by class. One possible solution is to export the coefficients of the ComBat algorithm used during the training phase, enabling us to harmonize the sample of test bases or even new data. Additionally, we can consider diversifying harmonization methods and incorporating numerical/clinical data to complement the images. Another limitation lies in our feature engineering approach, which while successful in producing an accurate interpretation of relevant features that influence the results (approximately 94%), may be inadequate in representing the input images.
Conclusions The ComBat method is currently well known for feature realignment from multicenter data, but we introduce ComBat with automatic outlier detection to address non-biological variability, such as acquisition defects and internal edge staining in the case of histopathology slides. We also worked on different applications of the ComBat method within the same database to present two other harmonization approaches: Patch Harmonization and Patch Harmonization in each class. We noticed that for the BreaKHis dataset, harmonizing ComBatOutliers is sufficient to obtain results that rival the literature, however, with the IDC dataset, applying the patch harmonization approach with ComBatOutliers provides the best results. This may be due to the volume of the dataset where a massive dataset like IDC (277524 patches) contains much more variability than BreaKHIs (1490 patches).
The pipeline contains different blocks that will allow the user to adapt the use of the harmonizations according to his study case. In this paper we presented two contributions dedicated to improve the performance of classification in the presence of heterogeneous data, including different databases, in the context of benign/cancerous classification of patches from histological slides. Our first contribution was a pipeline made available to researchers working in this field. It is implemented here for the classification of malignant and benign patches in invasive ductal carcinoma of the breast. This pipeline has shown effectiveness in producing models that outperformed recent ones from the literature based on feature engineering. The second contribution concerns the harmonization of data at different stages of the process, both to deal with the problem of outliers and to resolve the pitfalls of multicenter studies.
Data availibility The datasets used in this study were obtained from two open-access sources. The BreaKHis dataset  13  was acquired by requesting access from the authors. The Invasive Ductal Carcinoma (IDC) dataset, described and published in Ref. 12  , is available on Kaggle at the following link: https:// www. kaggle. com/ datas ets/ pault imoth ymoon ey/ breast-histo patho logy-images.
Description of Cohorts This retrospective study included 323 patients presented with advanced NSCLC and treated with nivolumab or pembrolizumab between 2015 and 2021. The cohorts were obtained from two thoracic oncology reference centers, the Institut Universitaire de Cardiologie et de Pneumologie de Québec (Quebec Heart and Lung Institute, IUCPQ) and the Centre Hospitalier Universitaire de Montréal (CHUM). The samples used in this study came from the IUCPQ's Quebec Respiratory Health Network Tissue Bank (https://rsr-qc. ca/ biobanque/). A research consent form was obtained from each patient, and the study was approved by the institutional review board at the two academic institutions where patient data were acquired (MP-10-2020-3397/CÉR CHUM: 19. 390). All patients with advanced NSCLC treated with ICIs and who had a pre-ICI CT scan were eligible for retrospective review.
Response Evaluation Criteria in Solid Tumors version 1. 1 was used to assess tumor response. The progression was defined by the qualitative evaluation of the radiologist. All patients were followed until death or until the date of censoring at the last time the subject was known to be alive, that is, in January 2022. To assess disease progression after treatment initiation, at least one breathhold chest pre-ICI CT scan (2 mo before treatment) and one post-treatment CT scan (every 8 wk for nivolumab and every 9 wk for pembrolizumab) were available. The two centers' data set included 323 patients in total, with 174 patients from the discovery cohort (CHUM) and 149 patients from the in-house validation cohort (IUCPQ) who were administered with pembrolizumab or nivolumab.
Extraction of Radiomics Features and Preprocessing PyRadiomics (version 3. 0. 1),  22  an open-source Python tool for extracting custom radiomics characteristics from CT scan data region of interests, was used. These are interpretable engineered features of the tumor that comply with the image biomarker standardization initiative. All scans' slice thicknesses were interpolated to voxel sizes of 1 Â 1 Â 1 mm 3 , and features were computed in three dimensional. The radiomics characteristics were extracted and categorized into four primary categories in a traditional pipeline using PyRadiomics, including tumor intensity-based, shapebased, texture-based, and wavelet-based features. Intensity-based features are first-order statistical features that quantify tumor intensity characteristics on the basis of a histogram of all voxel intensity values.
By putting together several weak models, such as decision trees, ensemble methods like gradient boosting make stronger models. In XGBoost, errors in existing models are fixed by adding new models. This makes the overall performance better. XGBoost is easy to use, has low computational costs, and is known to be good at making predictions. [30] [31] [32]  Analysis Framework Figure  1  illustrates the analysis pipeline for developing the radiomics model and the integrated radiomicsclinical model using the feature selection method and machine learning approach. The CHUM data set was used as the discovery cohort, whereas the IUCPQ data set was used for validation. On the discovery data set, we first performed the prevalidation phase, which consisted of 10 iterations/repetition of fivefold cross-validation for the predictive model presented previously.
We incrementally chose features for the ReliefF feature selection method and XGBoost machine learning method from the generated features (including those maintained after the LASSO). This was carried out in the discovery data set's prevalidation phase, which consisted of fivefold cross-validation with 10-times repetitions. For each fold, the ReliefF feature selection approach was used at this stage. The highest C-index for OS, PFS, and AUC for PD-L1 was used to determine the best number of features using Relief-based method. GridSearchCV from SciKit-Learn was used to conduct a cross-validation for XGBoost's hyperparameter tuning. Scikit-learn version 1. 0. 2, Python version 3. 9. 13, XGBoost version 1. 6. 2, and Skrebate version 0. 62 were used to implement the ReliefF feature selection and the XGBoost regressor and classifier, respectively.
The IUCPQ validation cohort consists of 121 patients with a mean age of 67. 7 years (7. 0). Of these patients, 69. 4% were former smokers, whereas 24. 8% are current smokers. The number of nonsmoker patients for PD-L1, PFS, and OS was less than 10% in both cohorts. We retrospectively selected data from patients who had been treated with at least one cycle of nivolumab or pembrolizumab at the CHUM and IUCPQ centers. The number of patients was 90 and 84 who were treated with nivolumab or pembrolizumab, respectively, on the CHUM cohort, and 82 and 67 on the IUCPQ cohort for the OS and PFS task. For PD-L1, 83 patients were treated with nivolumab and 63 patients were treated with pembrolizumab in the CHUM cohort; 81 patients were treated with nivolumab and 40 patients were treated with pembrolizumab in the IUCPQ cohort.
The range of OS values was 0. 3 to 67. 3 months in the CHUM data set and 0. 4 to 59. 4 months in the IUCPQ data set. In addition, the range of PFS values was 0. 13 to 62. 1 months in the CHUM data set and 0. 13 to 57. 7 months in the IUCPQ data set.
,  18  the authors built the predictive models for PFS and OS using 21 patients from a single institution who were treated with pembrolizumab only. Despite obtaining an AUC of close to 0. 72, major limitations of this study include the sample size and the potential biases in using only texture features. Considering the interpretation of the texture features, this reflects the tumor aggressiveness rather than biomarkers of response to ICIs, thus, limiting the model's applicability. In another retrospective study, Liu et al. 17  developed radiomics model to predict survival outcomes in a cohort of 46 patients with NSCLC treated with nivolumab alone. The models were trained on a small data set with leaveout-one-fold cross-validation, suggesting a need to test for the robustness on diverse datasets.
Finally, Trebeschi et al. 12  leveraged radiomics features to assess ICI response in patients with NSCLC and melanoma. Although their model was able to predict progressive disease in NSCLC with a good accuracy, it achieved poor performance on both pulmonary and hepatic melanoma lesions (AUC of 0. 55). To assess the performance of radiomics models, they used 133 and 70 patients in the discovery and test sets from a single institution, lacking sufficient validation and generalizability. To overcome the above-mentioned limitations, in this study, we considered diverse data sets from two academic institutions to train and test the predictive models. To the.
December 2023 Imaging-Based Biomarkers in NSCLC best of our knowledge, no study has been published on radiomics biomarkers predicting three clinical end points in patients with NSCLC treated with nivolumab and pembrolizumab using a large cohort. The significant number of patients treated with these two compounds and trained/evaluated in two institutions (n ¼ 323) distinguishes our study from prior studies. On the basis of this premise, we leveraged pretreatment CT imaging data along with the clinical variables to develop predictive biomarkers for three clinical end points-OS, PFS, and PD-L1 expression. To build the predictive models, we used a total of 323 patients across two institutions. The PyRadiomics platform was used to extract 851 radiomics features from the pre-ICI CT scan data, which were then used to develop the predictive models.
Acknowledgments Manem holds a salary support award from the  Fonds de Recherche du Québec-Santé (FRQS :  Quebec Foundation for Health Research ) and was supported by the  FRQS Foundation  grant from the  Quebec Heart & Lung Institute Research Center. Joubert is supported by a salary support award from the  Fond de Recherche du Québec-Santé  and is a member of the  Quebec Respiratory Health Research Network. Routy  is supported by a salary support award from the  Fond de Recherche du Québec-Santé. The authors thank the  Imagia-Canexia-Health company  for providing the annotated images, which was done through their Evidens platform. We thank the  Quebec Heart & Lung Institute Research Center biobank  for providing the specimens.
In this work, we present a joint analysis of radio, Sunyaev-Zel'dovich (SZ), and X-ray observations realized with the Australia Telescope Compact Array (ATCA), SPT, and Chandra, respectively. We use the ATCA data in order to estimate the AGN jet power at the BCG location of 48 SPT clusters at 0. 4 < z < 1. 3 selected to evolve like the progenitors of well-studied systems at z ∼ 0. We further estimate the X-ray luminosity within the cooling radius of these clusters from a joint analysis of the Chandra and SPT data in order to characterize the redshift evolution of the feedback-cooling balance during cluster growth. In Section 2 we summarize the cluster selection procedure as well as the data used in this paper. In Section 3 we present how we estimate the cavity power in the core of each cluster.
Three of these clusters have been further observed with Chandra thanks to another large program (PI: J. Hlavacek-Larrondo). A third large program (PI: M. McDonald) targeted 18 clusters at z > 0. 7 and allowed us to obtain an We subdivided the sample into cool-core (red) and noncool-core systems (purple). We also show the clusters considered in  Rafferty et al. (2006; squares), Hlavacek-Larrondo et al. (2012; triangles), and Hlavacek-Larrondo et al. (2015; stars)  with significant detections of X-ray cavities. They enabled the study of the feedback-cooling equilibrium at lower redshifts. The diagonal lines give the redshift evolution of the mean mass growth obtained by  Fakhouri et al. (2010)  for clusters with total mass between 6. 3 × 10 14 M e and 1. 3 × 10 15 M e at z = 0. Clusters that do not pass our progenitor selection cuts (diagonal lines) are shown with empty symbols.
2016). Targeted observations of XVP clusters were made in 2015 January at 2 GHz with an rms noise varying between 28 and 55 μJy beam -1. Some of these clusters were further followed up at 5 and 9 GHz in 2016 August if a strong detection was made at 2 GHz. We reach an rms noise varying between 30 and 67 μJy beam -1 at 5 GHz and between 19 and 77 μJy beam -1 at 9 GHz. All observations have been reduced using the 2015 February 21 release of the MIRIAD software  (Sault et al. 1995). Among the 73 SPT clusters satisfying our progenitor selection, 48 have available ATCA data. If a significant radio source is observed in the ATCA data of a given cluster, we make sure that it is located within 5″ of the BCG to exclude potential foreground or background contamination. We estimate the BCG location in these clusters from a visual inspection of available optical and IR imaging from  Bleem et al.