bluuebunny/crossref_metadata_2025_split huggingface dataset:
Abstract PO-053: Machine learning CADx process for classification of lung nodules below the Lung-RADS 4A threshold in LDCT scans
Authors: Abraham Rohan , Janzen Ian , Seyyedi Saeed , Khattra Sukhinder , Mayo John , Yuan Ren , Meyers Renelle , Lam Stephen , MacAulay Calum 
Published: March 2021

Abstract
Lung Cancer screening trials have demonstrated significant mortality reduction. Low-Dose Computed Tomography (LDCT) screening can frequently discover many small nodules in at risk participants. However classification of these, sub-cm nodules as cancerous or benign is a challenging task even for expert clinicians. In this work we use machine learning (ML) techniques to differentiate, cancerous (clinically confirmed) and benign nodules (&gt;5 years of follow-up). Data for this study is drawn from a screening study (PanCan) from which we selected 613 distinct nodules (141 cancerous, and ~size matched 472 benign). We analyzed texture and shape features (~170) that are extracted from the nodule with and without perimeter transition pixels to control for perimeter effects. Features are also extracted from the ring of parenchyma surrounding the nodule to account for tumor effects on surrounding tissue. From the equivalent location in the opposite lung, parenchymal characteristics were extracted which we use to normalize the nodule texture features in an effort to reduce scanner bias. Our preliminary results for machine learning classification have shown model accuracies of up to ~80% (feature selection and classification algorithm dependent). Radiomic feature data can be combined with patient demographic variables such as age, sex, and smoking status to further improve our models, reaching ~84% classification accuracy. When normalized by the opposite lung, 58% of texture features showed improved classification ability. A single feature from the ring of parenchyma surrounding the nodule achieved 73.5% accuracy. Others have shown that nodule area/volume is a good classifier; for this data set it gave 68% accuracy. Data exploration to study classification accuracies among different subsets of patients reveals a number of interesting trends. We have noted for instance that in patients without emphysema, tumors can be classified with much higher accuracy (&gt;90%) than in those who suffer from it. This and other results detailing classification among specific patient groupings may prove relevant in any potential future clinical implementation of these results.
Citation Format: Rohan Abraham, Ian Janzen, Saeed Seyyedi, Sukhinder Khattra, John Mayo, Ren Yuan, Renelle Meyers, Stephen Lam, Calum MacAulay. Machine learning CADx process for classification of lung nodules below the Lung-RADS 4A threshold in LDCT scans [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-053.
Abstract PO-059: Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation based on machine learning algorithms
Authors: Liang Ja-Der , Yang Ta-Wei , Chen Po-When , Chou Cheng-Fu , Wu Yao-Ming 
Published: March 2021

Abstract
Background: Hepatocellular carcinoma (HCC) has been one of the leading causes of cancer death. Despite accurate diagnosis and effective treatments, the recurrence rate of HCC is still high. To build a recursive model by analyzing preoperative reports is helpful for follow-up and observing recurrence of tumor. Methods: Newly diagnosed HCC patients in National Taiwan University Hospital (NTUH) who received radiofrequency ablation (RFA) as the first treatment, were enrolled. Among 334 enrolled patients, 256 patients did not have recurrent HCC one year after RFA treatment and the other 78 patients had HCC recurrences. Data was processed with workflow of feature extraction and data imputation to acquire 16 features. We use different machine-learning methods to build the recurrence prediction model of HCC. The model performance among linear regression (LR), support vector machine (SVM), random forest (RF), and deep neural network (DNN) under a variety of experimental environment, including different parameters of models, data normalizations, and methods of data up-sampling were compared. Results: DNN model has the relatively best result that is the highest accuracy (82.65%) and second balanced accuracy (BAC=66.03%) among all models. SVM with feature selection according to importance in RF has the best BAC result (66.48%) with 10% lower accuracy (72.51%) compared with DNN. LR has the worst BAC (50.00%). Conclusion: Among all prediction models, DNN achieved the best results by considering both accuracy and balanced accuracy. Establishment of prediction model could identify the risk patients with close monitoring of tumor recurrence.
Citation Format: Ja-Der Liang, Ta-Wei Yang, Po-When Chen, Cheng-Fu Chou, Yao-Ming Wu. Recurrence predictive models for patients with hepatocellular carcinoma after radiofrequency ablation based on machine learning algorithms [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-059.
Abstract PO-062: Fully automated artificial intelligence based breast cancer scanning through transilluminated image acquisition for mass screening appliances
Authors: Ponram P. 
Published: March 2021

Abstract
Early detection of breast cancer is an effective tool in better management and treatment of the same. Mass screening is one of the available methods to detect early breast masses. Current technologies such as ultrasonography and mammography are golden standards of breast cancer detection. But these imaging modalities have several complexities when used for mass screening such as labor-intensive and cost. Hence a low-cost automated breast screening device is proposed in this project. A transillumination-based non-ionizing and non-radiating near-infrared optical wave are used for the transillumination of breast masses and their corresponding images will be acquired. The acquired images will be given to a deep learning artificial intelligence network for automated detection and classification of the tumor. The AI network will be pre-trained with double validated data with clinician expertise in the domain. The AI outcome will be displayed to the user and also taken as backpropagation feedback to the AI network to train the system further. This device shall be used as a system for mass screening applications from which suspected cases can e referred for mammography for final confirmation of tumor. The automated system shall be used in rural places with less skilled human intervention for mass public screening programs. This will reduce the disease burden on the huge women population vulnerable to the disease.
Citation Format: P. Ponram. Fully automated artificial intelligence based breast cancer scanning through transilluminated image acquisition for mass screening appliances [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-062.
Abstract PO-069: A deep learning model assists urine cytology reporting with computational estimates of the nuclear/cytoplasmic ratios of the urothelial cells based on the Paris System
Authors: Yang Wei-Lei , Li Chi-Bin , Ou Yen-Chuan , Lin Yi-Sheng , Tsao Tang-Yi , Chang Ming-Chen , Hang Jen-Fan , Liu Tien-Jen 
Published: March 2021

Abstract
The Paris System (TPS) for Reporting Urinary Cytology is an international system with standardized terminology and cytomorphologic criteria that aims for better risk stratification to identify high-grade urothelial carcinoma in urinary cytology specimens. Based on TPS, the nuclear/cytoplasmic ratio (N/C ratio) of the observed urothelial cells is a crucial criterion and the cutoff values of 0.5 and 0.7 are used for the diagnosis of Atypical Urothelial Cells (AUC) and High-Grade Urothelial Carcinoma (HGUC)/Suspicious for HGUC respectively. Although the N/C ratio is a clear and measurable criterion, there are still challenges for adopting TPS into clinical practice. As urine cytology mainly relies on manual microscopic examination by cytopathologists and cytotechnologists, determining the N/C ratio through the naked eyes results in intra- and inter-observer variations and therefore generates subjective, inconsistent, and unreproducible reports. To overcome the aforementioned issues, the authors developed a deep learning-based image analysis model to assist calculating the N/C ratio and classifying urothelial cells computationally. Deidentified urine cytology slides were digitalized as whole-slide images (WSIs) for analyzing. An “active learning” method was used to improve results over time with only a small amount of upfront training data required. First, a small amount of annotated WSIs were provided to train the initial computational model. Next, the experts reviewed the results predicted by the initial model, and provide feedback for the model to learn and improve. The above steps were repeated by experts reviewing the “improved model” and providing feedbacks, thus improving the initial/intermediate models continuously until a satisfactory result was achieved. Preliminary results showed that our model was able to determine the areas of cytoplasm and nucleus in urothelial cells as well as calculate the N/C ratios using the WSI. Moreover, it enumerates the AUC and HGUC cells on the WSI and provides quantification and distribution information on the whole slide scale. This computational model provides a significant advantage as an objective tool to assist urine cytology analysis of the N/C ratio with greater accuracy and reproducibility.
Citation Format: Wei-Lei Yang, Chi-Bin Li, Yen-Chuan Ou, Yi-Sheng Lin, Tang-Yi Tsao, Ming-Chen Chang, Jen-Fan Hang, Tien-Jen Liu. A deep learning model assists urine cytology reporting with computational estimates of the nuclear/cytoplasmic ratios of the urothelial cells based on the Paris System [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-069.
Abstract PO-070: Bimodality of gene expression in cancer patient tumors as interpretable biomarkers for drug sensitivity
Authors: Ba-alawi Wail , Nair Sisira Kadambat , Li Bo , Mammoliti Anthony , Smirnov Petr , Mer Arvind Singh , Penn Linda , Haibe-Kains Benjamin 
Published: March 2021

Abstract
Identifying biomarkers predictive of cancer cells’ response to drug treatment constitutes one of the main challenges in precision oncology. Recent large-scale cancer pharmacogenomic studies have boosted the research for finding predictive biomarkers by profiling thousands of human cancer cell lines at the molecular level and screening them with hundreds of approved drugs and experimental chemical compounds. Many studies have leveraged these data to build predictive models of response using various statistical and machine learning methods. However, a common challenge in these methods is the lack of interpretability as to how they make the predictions and which features were the most associated with response, hindering the clinical translation of these models. To alleviate this issue, we develop a new machine learning pipeline based on the recent LOBICO approach that explores the space of bimodally expressed genes in multiple large in vitro pharmacogenomic studies and builds multivariate, nonlinear, yet interpretable logic-based models predictive of drug response. Using our method, we used a compendium of three of the largest pharmacogenomic data sets to build robust and interpretable models for 101 drugs that span 17 drug classes with high validation rate in independent datasets.
Citation Format: Wail Ba-alawi, Sisira Kadambat Nair, Bo Li, Anthony Mammoliti, Petr Smirnov, Arvind Singh Mer, Linda Penn, Benjamin Haibe-Kains. Bimodality of gene expression in cancer patient tumors as interpretable biomarkers for drug sensitivity [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-070.
Abstract PO-079: Fused LASSO application for gastric cancer image segmentation
Authors: Jung Jiwon , Roh Jin , Park Chan-Sik 
Published: March 2021

Abstract
Despite recent advances in digital pathology, the development of a robust deep learning model for identifying tumor regions on digitalized slide images has been plagued by problems related to the patch-based approach attributable to the narrow and limited field-of-views. In this study, we explored the effect of the fused lasso technique on diagnostic performance using gastric cancer pathology images. Our goal is to develop a speedy tumor segmentation scheme that overcomes performance degradation arising from the limitations of the patch-based approach. We used 27 specimens with partial annotation from tissue-microarrays (TMAs) with gastric cancer from the Asan Medical Center. Our segmentation adjustment method was tested on multiple renowned deep learning model architectures to detect malignancy on tissue slides. In the fused lasso formulation, fusion and lasso penalty were adopted to penalize variables with L1-norms based on both the patchwise predictions and their pairwise differences between adjoining patches. To obtain well-structured boundaries and discard irrelevant observations, the best performing hyperparameters controlling the degree to be penalized were adaptively determined via cross-validation. We demonstrated that adoption of fused lasso penalties can produce segmentation close to that generated by experts without a significant increase in computing time. The average test performance in terms of IOU, dice, accuracy, precision, recall, and AUROC increased by a degree of 3.74%p, 3.27%p, 0.93%p, 5.32%p, 3.20%p, and 1.58%p, respectively. This study provides a use case of the application of fused lasso modeling for precisely identifying tumor area, which can alleviate the bottleneck in gastric cancer research.
Citation Format: Jiwon Jung, Jin Roh, Chan-Sik Park. Fused LASSO application for gastric cancer image segmentation [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-079.
Abstract PO-080: Deep learning-based analysis of heterogeneity of breast cancer cells using lens-free digital in-line holography
Authors: Song Tzu-Hsi , Cao Mengzhi , Min Jouha , Im Hyungsoon , Lee Hakho , Lee Kwonmoo 
Published: March 2021

Abstract
Lens-free digital in-line holography (LDIH) produces cellular diffraction patterns from a large field of view that lens-based microscopes cannot achieve. It is a promising diagnostic tool allowing comprehensive cellular analysis with high-throughput capability. Since these diffraction images are far more complicated to discern, conventionally computational algorithms are used to reconstruct cellular images for the diffraction patterns. However, it is inefficient and prone to errors. Here, we developed a deep learning architecture, HoloNet to directly analyze the diffraction patterns from LDIH. In addition to the standard CNN (Convolutional Neural Network), the HoloNet includes a holo-branch that extracts large features from holograms and integrate them with the small features from the standard CNN. Using HoloNet, we accurately predicted the intensity values of ER/PR and HER2 in breast cancer cells and classified four known subtypes of breast cancer cells. Moreover, we applied a HoloNet dual embedding, which learns high-level diffraction features related to breast cancer cell types and the intensities of ER/PR and HER2. This allowed us to identify potential subtypes of breast cancer cells from the cell lines and the fine needle aspiration samples from breast cancer patients. We demonstrate that our HoloNet can enable LDIH to perform more detailed and rapid analyses of subtypes of breast cancer cells.
Citation Format: Tzu-Hsi Song, Mengzhi Cao, Jouha Min, Hyungsoon Im, Hakho Lee, Kwonmoo Lee. Deep learning-based analysis of heterogeneity of breast cancer cells using lens-free digital in-line holography [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-080.
Abstract PO-083: Artificial intelligence based detection of breast cancer from transilluminated optical data
Authors: Ponram P. 
Published: March 2021

Abstract
Breast Cancer is a second deadly cancer disease and more than 2 million new cases are being reported annually in recent years. Early diagnosis and mass screening is validated as an effective tool for better disease management. The development of non-invasive affordable first-level screening techniques is required for efficient mass screening. The proposed system consists of a near infra-red optical transmitter connected with a signal modulator and receiver coupled with a demodulator mechanism. A modulated optical waveform provides deep tissue details by high tissue penetration. The constant signal waveform generator and signal oscilloscope were connected for wave generation and measurement. The optical waveform is digitally modulated by light modulation techniques at the transmitter side and demodulated at the receiver side. The normal spot and cancerous spot of the breast phantom model are placed in the path of the optical transmitter and the reflected signal is received by the receiver prior to demodulation. Phase Jitter, Time Jitter, and 3dB noise level were measured from the received signal for the normal as well as a cancerous spot of the breast phantom. The obtained data is given as input to a backpropagation based Artificial Intelligence system for training, validation, and classification. The results indicate that AI based classification of breast tumor from optically transilluminated data will provide substantial improvement of efficiency of the AI system. Also the proposed system can be utilized along with ultrasonography as a hybrid imaging modality.
Citation Format: P. Ponram. Artificial intelligence based detection of breast cancer from transilluminated optical data [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-083.
Abstract PO-090: Application of deep learning methods in predicting survival and identifying high-risk populations from large optum real world breast cancer data
Authors: Wang Han , Luo Xiaodong , Wu Meiqi , Shen LiMing 
Published: March 2021

Abstract
Real-World Evidence (RWE) contains abundant observational data collected during routine clinical practice. With such large-scale and high-dimensional data, we can leverage the power of neural networks to increase the capacity of classical survival models in predicting the risk of breast cancer patients, and identifying potential prognosis-associated factors retrospectively. The target cohorts were extracted based on diagnosis code, biomarker, and mastectomy procedure code, we further identified metastatic breast cancer cases according to secondary diagnosis code and physician notes. Our features consist of demographic and vital data, cancer-related measurement, and medication history, which was divided into several lines of therapy. Considering the dynamic nature of cancer therapy, time-dependent survival models are more suitable to model the hazard given the current features. Therefore, we extended the proportional-hazard-based DeepSurv and logistic-hazard-based Nnet-survival to accommodate time-dependent features, and further incorporated recurrent neural networks to make better use of sequential medication information. We trained the model on the training set (70%), tuned the hyperparameters via Bayesian Optimization on the validation set (10%), and evaluated the model with c-index and integrated brier score on the test set (20%). Compared with the classical linear cox model and pooled logistic regression, the proposed model outperformed these baselines by a significant margin, indicating the promise of using deep learning and the time-dependent prognosis model for survival prediction and risk stratification in cancer patients. In addition, the output from the neural network was interpreted with SHAP values and Integrated Gradient (IG), which shed some light on the effect of adjuvant hormone therapy, especially AI and SERM, on recurrence and overall survival. Further, our model can be improved with more collected data and a better approach to handling error and missingness in the data.
Citation Format: Han Wang, Xiaodong Luo, Meiqi Wu, LiMing Shen. Application of deep learning methods in predicting survival and identifying high-risk populations from large optum real world breast cancer data [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-090.