bluuebunny/crossref_metadata_2025 huggingface dataset:
Abstract PO-041: Shape features predicting intrahepatic progression-free and overall survival for SBRT treated HCC patients using radiomics and deep learning based survival models
Authors: Wei Lise , Jiang Jue , Veeraraghavan Harini , El Naqa Issam
Published: March 2021

Purpose: Intrahepatic failure is a major concern in the treatment of hepatocellular carcinoma (HCC) but lacks clinically reliable predictive markers. This work aims to identify a radiomic signature for risk prediction of intrahepatic progression in HCC patients treated with stereotactic body radiation therapy (SBRT) by extracting magnetic resonance imaging (MRI) and contrast-enhanced CT phenotypes from tumor(s) and liver parenchyma. Materials/Methods: 94 HCC patients treated with SBRT were retrospectively analyzed. The range of follow-up time is 57-1768 days. Samples were right censored if there was no intrahepatic progression within the follow-up time. The event rates is 43/94=45.7%. 112 radiomics features were extracted from tumors and liver parenchyma from CECT and MRI using pyradiomics platform. CT images were first added a 1000 HU shift and MRI images were standardized by the mean and standard deviation. Univariate Cox analysis was applied for radiomics features analysis. Multivariate Cox models and survival neural networks were also used to predict the outcome. Multiple folds cross-validation was used for model evaluation. Models’ performances were assessed using Harrell’s c-index and Kaplan-Meier plot. Results: Sphericity, small dependence emphasis, small dependence high gray level emphasis, run high gray level emphasis and coarseness are significant predictors from MR tumors for the outcome. The Cox models showed c-index of 0.600 (CI 95%: 0.558-0.642). Sphericity and maximal correlation coefficient are significant predictors from CT tumors for the outcome, with a c-index of 0.613 (CI 95%: 0.576-0.649). Volume, elongation and sphericity are significant predictors for overall survival, with a c-index of 0.584 (CI 95%: 0.532-0.636). None of the liver parenchyma features were important predictors. Interestingly, convolutional neural networks (CNN) were applied on the contour of the tumors and the c-index was 0.668 (CI 95%: 0.612-0.723). The 2 channel (MR-tumor region and contour mask) CNN network showed better performance of 0.698 (CI 95%: 0.658-0.749). Conclusions: Shape features showed predictive power for both intrahepatic progression-free and overall survival endpoints, which was proven using both radiomics feature based Cox modeling and CNN based contour methods. Adding MR imaging improved the performance.

Citation Format: Lise Wei, Jue Jiang, Harini Veeraraghavan, Issam El Naqa. Shape features predicting intrahepatic progression-free and overall survival for SBRT treated HCC patients using radiomics and deep learning based survival models [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-041.
Abstract PO-046: MethylationToActivity: A deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors
Authors: Williams Justin , Xu Beisi , Putnam Daniel , Thrasher Andrew , Li Chunliang , Yang Jun , Chen Xiang
Published: March 2021

Although genome-wide DNA methylomes have demonstrated their clinical value as reliable biomarkers for tumor detection, subtyping, and classification, their direct biological impacts at the individual gene level remain elusive. Here we present MethylationToActivity (M2A), a machine learning framework that uses convolutional neural networks to infer promoter activities based on H3K4me3 and H3K27ac enrichment, from DNA methylation patterns for individual genes. Using publicly available datasets in real-world test scenarios, we demonstrate that M2A is highly accurate and robust in revealing promoter activity landscapes in various pediatric and adult cancers, including both solid and hematologic malignant neoplasms.

Citation Format: Justin Williams, Beisi Xu, Daniel Putnam, Andrew Thrasher, Chunliang Li, Jun Yang, Xiang Chen. MethylationToActivity: A deep-learning framework that reveals promoter activity landscapes from DNA methylomes in individual tumors [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-046.
Abstract PO-049: Pseudo-alignment resolution in discriminating mouse reads
Authors: Jahangiri-Tazehkand Soheil , Agha-Babazadeh Farnoosh , Haibe-Kains Benjamin
Published: March 2021

Patient-Derived Xenografts (PDX) are the best preclinical models to study cancer biology preclinical drug screening. PDXs are generated by implanting a piece of fresh cancer tissue directly into an immunodeficient mouse to grow and imitate a human tumor. Previous studies have shown that PDXs are able to accurately recapitulate the (epi-)genomic characteristics of the original tumor. Yet the inclusion of the mouse cells in tumor biopsies can introduce unwanted noise and bias in high throughput (epi-)genomics analyses and interpretation. Thus, filtering the mouse reads is one of the first and important steps in analyzing high throughput PDX sequencing data. There are two main approaches to account for mouse reads in analyzing bulk RNAseq data generated from PDXs. In the first approach, an external tool is used to classify the raw or aligned reads into a human, mouse, or ambiguous reads. Examples include XenoFilter, BBsplit, disambiguate, and Xenome. In the second approach, a mixed human-mouse reference genome is used when aligning the raw reads. Multiple studies have shown that the mixed reference approach produces better or similar results compared to when an external tool is used for mouse read discrimination. However, these comparisons have employed exact alignment methods when using a mixed reference approach. Recently, the significant deluge of sequencing data has brought attention to pseudo-alignment based methods, specifically Kallisto and Salmon. By taking advantage of pseudo-alignment to a reference transcriptome, these tools are capable of analyzing millions of reads in minutes while demonstrating near-optimal quantification performance. Yet their performance in analyzing PDX RNAseq data while using a mixed reference is unclear. Moreover, among the external methods, only Xenome has a pseudo-alignment strategy for read classification and the rest of the methods use exact alignment making them labor-intensive and intractable for large datasets. In this study, we compare two main mouse read discrimination pipelines when using Kallisto and Salmon. By sampling from two human and mouse lung RNAseq datasets, we have simulated five pseudo-PDX RNAseq datasets with different contamination ratios. By comparing outputs of different discriminating strategies to the ground truth, we show that Kallisto and Salmon when used with mixed human-mouse transcriptome produce results that are more correlated with the ground truth. Moreover, by differential gene expression analyses we show that fewer genes are significantly affected when using the mixed reference approach.

Citation Format: Soheil Jahangiri-Tazehkand, Farnoosh Agha-Babazadeh, Benjamin Haibe-Kains. Pseudo-alignment resolution in discriminating mouse reads [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-049.
Abstract PO-051: Development of web-based quality-assurance tool for radiotherapy target delineation for head and neck cancer: Quality evaluation of nasopharyngeal carcinoma
Authors: Kim Jun Won , Marsilla Joseph , Kazmierski Michal , Tkachuk Denis , Haibe-Kains Benjamin , Hope Andrew
Published: March 2021

Purpose: We developed Label-It, a new web-application for rapid review of radiotherapy (RT) target volumes, and used it to evaluate the relationship between target delineation compliance to the international guidelines and treatment outcomes of nasopharyngeal carcinoma (NPC) patients undergoing definitive RT. Methods and Materials: Our radiographic image database consists of anonymized simulation CT scans, RT structures, and treatment data of 3,211 head and neck cancer patients treated between July 2005 and August 2017. We used 332 patients treated with intensity-modulated RT for pathologically confirmed NPC as the study cohort and imported intermediate risk clinical target volumes of the primary tumor (IR-CTVp) receiving 56 Gy into Label-It. We determined inclusion of anatomic sites within IR-CTVp in accordance with 2018 International guideline for CTV delineation for NPC and correlated the results with time to local failure (TTLF) using Cox-regression. Results: At a median follow-up of 5.6 years, 5-year TTLF and overall survival rates were 93.1% and 85.9% respectively. The most frequently non-compliant anatomic sites were sphenoid sinus (n = 69, 20.8%), followed by cavernous sinus (n = 38, 11.4%), left and right petrous apices (n = 37 and 32, 11.1% and 9.6%), clivus (n = 14, 4.2%), and right and left foramen rotundum (n = 14 and 12, 4.2% and 3.6%). Among 23 patients with a local failure (6.9%), the number of non-compliant cases were 8 for sphenoid sinus, 7 cavernous sinus, 4 left and 3 right petrous apices, and 2 clivus. In Cox regression analysis, T4 disease (p = 0.003), RT alone (p = 0.007), cavernous sinus non-conformity (p = 0.020) were independent prognostic factors for TTLF. Conclusions: Label-It was an effective tool for rapid review of target volumes in a large patient cohort. Despite a high compliance to the international guidelines, inadequate coverage of cavernous sinus was correlated with decreased TTLF.

Citation Format: Jun Won Kim, Joseph Marsilla, Michal Kazmierski, Denis Tkachuk, Benjamin Haibe-Kains, Andrew Hope. Development of web-based quality-assurance tool for radiotherapy target delineation for head and neck cancer: Quality evaluation of nasopharyngeal carcinoma [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-051.
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

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 (>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 (>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

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

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

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

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

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

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

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

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.
Abstract PO-095: The PROState AI Cancer–Decision Support (PROSAIC-DS) pilot study: Clinical decision support technology and its role in prostate cancer MDT meetings
Authors: Santis Vishal , Enting Deborah , Patkar Vivek , Chalkidou Anastasia , Fox John , Ruta Danny , Makanjuola Jonathan K.
Published: March 2021

Background Multidisciplinary teams (MDT/tumour boards) were first introduced in the 1990s and have experienced little change to their methodology since. Universally used for the treatment of prostate cancer (CaP) in the UK new interventions are proposed to improve MDT efficiency and patient outcomes. Clinical practice guidelines (CPGs), designed to increase the uptake of evidence-based practice, suffer from lack of proper implementation. There is increasing evidence to suggest a gap between CPGs and actual treatment and the use of artificial intelligence an (AI) systems can help to increase the efficiency of the MDT. Methods We evaluated differences in MDT concordance with guidelines in the primary treatment of localised or locally advanced prostate cancer using the Deontics AI based custom clinical decision support software (CDSS) software. 59 paper cases were created by an expert clinician, 9 of which were excluded as they did not meet eligibility criteria. The remaining 50 cases were provided to the CDSS for evaluation. Simultaneously, two physicians assessed each patient case and provided their treatment recommendations. The results were assessed for concordance with UK, European and American guidelines, inter-rater reliability and trends in concordance based on patient variables.Results Overall clinician concordance with guidelines was 76%, while total concordance with all three guidelines was 28%. Overall concordance was highest with NICE guidelines, while total concordance was highest for NCCN guidelines. Inter-rater reliability was highest for the NCCN guidelines. Age < 75 (p <0.001; odds ratio [OR], 35.000), prostate volume < 46.5ml (p =0.047; OR, 4.909), and a Gleason score ≠ 8 (p =0.013; OR, 12.333), were all significantly associated with increased guideline concordance in this study. Conclusions Concordance with CPGs needs to be improved in specific patient groups. This may reflect cognitive bias, cognitive overload, or conflicting guideline recommendations and evidence base. One potential solution may be the integration of CDSS technology into the MDT setting.

Citation Format: Vishal Santis, Deborah Enting, Vivek Patkar, Anastasia Chalkidou, John Fox, Danny Ruta, Jonathan K. Makanjuola. The PROState AI Cancer–Decision Support (PROSAIC-DS) pilot study: Clinical decision support technology and its role in prostate cancer MDT meetings [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-095.
Abstract PR-02: Machine learning models to quantify lineage plasticity and neuroendocrine differentiation in high-grade prostate cancer
Authors: Knudsen Beatrice , Garraway Isla , Gertych Arkadiusz , Lewis Michael , Ferrero Alessandro , Whitaker Ross
Published: March 2021

Neuroendocrine differentiation (NED) is a type of lineage plasticity that occurs in multiple cancer types either de-novo, or as a result of treatment-induced selection pressure. NED is identified by different, partially overlapping clinical, morphological and genomic features which include small cell neuroendocrine histomorphology, gene expression signatures of Rb1 loss and lethal tumor behavior. Through the eyes of pathologists, the most extreme form of NED is small cell, neuroendocrine carcinoma (SmCC), a rare cancer type that can arise in many organs. Adenocarcinomas, the most common cancer type in the adult population can harbor clinical and genomic characteristics of SmCC without recognizable morphologic features. Therefore, we determined whether machine learning algorithms can be trained to identify salient morphologic features of NED in adenocarcinoma. Using a cohort of prostate needle biopsies, we trained complementary algorithms to generate NED scores from high-grade prostate cancer regions and tested NED scores for correlation with gene expression and pathway activation from RNA sequencing of the exact same tumor regions. Using this approach as a validation strategy, we observed correlations between NED scores and pathway activation known to be increased in SmCC. In addition, we discovered novel morphologic features through functional interpretations of pathways correlated with NED scores. Finally, we used multi-variate regression analysis to demonstrate that computer generated NED scores predict the risk of lethal prostate cancer in diagnostic prostate needle biopsies. Altogether, we demonstrate the feasibility to quantify NED - lineage plasticity in regular diagnostic H&E stained pathology slides, which can be developed into an impactful, point-of-care clinical assay for the management and treatment of prostate cancer patients.

Citation Format: Beatrice Knudsen, Isla Garraway, Arkadiusz Gertych, Michael Lewis, Alessandro Ferrero, Ross Whitaker. Machine learning models to quantify lineage plasticity and neuroendocrine differentiation in high-grade prostate cancer [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 PR-02.
Fruit and Vegetable Intake and Mortality
Authors: Wang Dong D. , Li Yanping , Bhupathiraju Shilpa N. , Rosner Bernard A. , Sun Qi , Giovannucci Edward L. , Rimm Eric B. , Manson JoAnn E. , Willett Walter C. , Stampfer Meir J. , Hu Frank B.
Published: March 2021

Background:

The optimal intake levels of fruit and vegetables for maintaining long-term health are uncertain.

Methods:

We followed 66 719 women from the Nurses’ Health Study (1984–2014) and 42 016 men from the Health Professionals Follow-up Study (1986–2014) who were free from cardiovascular disease (CVD), cancer, and diabetes at baseline. Diet was assessed using a validated semiquantitative food frequency questionnaire at baseline and updated every 2 to 4 years. We also conducted a dose-response meta-analysis, including results from our 2 cohorts and 24 other prospective cohort studies.

Results:

We documented 33 898 deaths during the follow-up. After adjustment for known and suspected confounding variables and risk factors, we observed nonlinear inverse associations of fruit and vegetable intake with total mortality and cause-specific mortality attributable to cancer, CVD, and respiratory disease (all
P
nonlinear
<0.001). Intake of ≈5 servings per day of fruit and vegetables, or 2 servings of fruit and 3 servings of vegetables, was associated with the lowest mortality, and above that level, higher intake was not associated with additional risk reduction. In comparison with the reference level (2 servings/d), daily intake of 5 servings of fruit and vegetables was associated with hazard ratios (95% CI) of 0.87 (0.85–0.90) for total mortality, 0.88 (0.83–0.94) for CVD mortality, 0.90 (0.86–0.95) for cancer mortality, and 0.65 (0.59–0.72) for respiratory disease mortality. The dose-response meta-analysis that included 145 015 deaths accrued in 1 892 885 participants yielded similar results (summary risk ratio of mortality for 5 servings/d=0.87 [95% CI, 0.85–0.88];
P
nonlinear
<0.001). Higher intakes of most subgroups of fruits and vegetables were associated with lower mortality, with the exception of starchy vegetables such as peas and corn. Intakes of fruit juices and potatoes were not associated with total and cause-specific mortality.

Conclusions:

Higher intakes of fruit and vegetables were associated with lower mortality; the risk reduction plateaued at ≈5 servings of fruit and vegetables per day. These findings support current dietary recommendations to increase intake of fruits and vegetables, but not fruit juices and potatoes.
Pictonorchestia, a new genus of riparian-hopper from New Zealand (Crustacea, Amphipoda, Senticaudata, Talitridae)
Authors: LOWRY JAMES K. , SPRINGTHORPE ROGER
Published: March 2021

Pictonorchestia, a new genus of talitrid amphipod is established for Orchestia dentata Filhol, 1885, from New Zealand. It is characterised by the dactylus of gnathopod 2 which has a well-developed projection on the posterior margin which interlocks with a shallow sinus on the palm and a proximal spine which defines the palm. It differs from other talitrid genera in having gnathopod 1 carpus and propodus with palmate lobes, gnathopod 2 dactylus not modified distally, pleopods all well-developed, uropod 1 exopod with marginal setae in one row. Pictonorchestia is also unusual in having an incrassate antenna 2, but pereopod 7 not sexually dimorphic, a combination shared by only ten other genera, none of which have the gnathopod 2 dactylus with a prominent projection on the posterior margin.
AMOC and Climate Responses to Dust Reduction and Greening of the Sahara during the Mid-Holocene
Authors: Zhang Ming , Liu Yonggang , Zhang Jian , Wen Qin
Published: March 2021

North Africa was green during the mid-Holocene [about 6000 years ago (6 ka)] and emitted much less dust to the atmosphere than in the present day. Here we use a fully coupled atmosphere–ocean general circulation model, CESM1.2.2, to test the impact of dust reduction and greening of the Sahara on the Atlantic meridional overturning circulation (AMOC) during this period. Results show that dust removal leads to a decrease of AMOC by 6.2% while greening of the Sahara with 100% shrub (100% grass) cover causes an enhancement of the AMOC by 6.1% (4.8%). The AMOC is increased by 5.3% (2.3%) when both the dust reduction and green Sahara with 100% shrub (100% grass) are considered. The AMOC changes are primarily due to the precipitation change over the west subtropical North Atlantic, from where the salinity anomaly is advected to the deep-water formation region. Global-mean surface temperature increases by 0.09° and 0.40°C (0.25°C) when global dust is removed and when North Africa and the Arabian region are covered by shrub (grass), respectively, showing a dominating effect of vegetation over dust. The comparison between modeled and reconstructed sea surface temperature is improved when the effect of vegetation is considered. The results may have implications for climate impact of future wetting over North Africa, either through global warming or through building of solar farms and wind farms.
Inverted circadian variation of arterial pressure in a geriatric patient: an indicator of autonomic dysfunction
Authors: Lieten Siddhartha , Debain Aziz , Bravenboer Bert , Mets Tony
Published: March 2021

Background

Orthostatic hypotension (OH) in geriatric patients frequently involves a component of autonomic failure (AF). The combination of OH with nocturnal hypertension (NHT) is indicative of AF, which is described as pure (PAF), when neurologic symptoms are absent, or as multisystem atrophy (MSA), when combined with motor disturbance (Parkinsonism or Parkinson disease).

Case presentation

An 87-year-old man presented with long-lasting OH. He frequently fell, causing several fractures, and he developed heart failure. Blood pressure (BP) registration revealed a reversal of the day-night rhythm with NHT. An 18-FDG PET brain CT scan showed cerebellar hypometabolism, indicating MSA.

Conclusions

This case demonstrates the use of continuous BP registration in geriatric patients with OH for diagnosing NHT. It illustrates the usefulness of 18-FDG PET brain CT scan to specify the nature of the AF. The case also illustrates the difficulty of managing the combination of OH and NHT.
Effect of empagliflozin on ectopic fat stores and myocardial energetics in type 2 diabetes: the EMPACEF study
Authors: Gaborit B. , Ancel P. , Abdullah A. E. , Maurice F. , Abdesselam I. , Calen A. , Soghomonian A. , Houssays M. , Varlet I. , Eisinger M. , Lasbleiz A. , Peiretti F. , Bornet C. E. , Lefur Y. , Pini L. , Rapacchi S. , Bernard M. , Resseguier N. , Darmon P. , Kober F. , Dutour A.
Published: March 2021

Background

Empagliflozin is a sodium-glucose cotransporter 2 (SGLT2) inhibitor that has demonstrated cardiovascular and renal protection in patients with type 2 diabetes (T2D). We hypothesized that empaglifozin (EMPA) could modulate ectopic fat stores and myocardial energetics in high-fat-high-sucrose (HFHS) diet mice and in type 2 diabetics (T2D).

Methods

C57BL/6 HFHS mice (n = 24) and T2D subjects (n = 56) were randomly assigned to 12 weeks of treatment with EMPA (30 mg/kg in mice, 10 mg/day in humans) or with placebo. A 4.7 T or 3 T MRI with 1H-MRS evaluation–myocardial fat (primary endpoint) and liver fat content (LFC)–were performed at baseline and at 12 weeks. In humans, standard cardiac MRI was coupled with myocardial energetics (PCr/ATP) measured with 31P-MRS. Subcutaneous (SAT) abdominal, visceral (VAT), epicardial and pancreatic fat were also evaluated.

The primary efficacy endpoint was the change in epicardial fat volume between EMPA and placebo from baseline to 12 weeks. Secondary endpoints were the differences in PCr/ATP ratio, myocardial, liver and pancreatic fat content, SAT and VAT between groups at 12 weeks.

Results

In mice fed HFHS, EMPA significantly improved glucose tolerance and increased blood ketone bodies (KB) and β-hydroxybutyrate levels (p < 0.05) compared to placebo. Mice fed HFHS had increased myocardial and liver fat content compared to standard diet mice. EMPA significantly attenuated liver fat content by 55%, (p < 0.001) but had no effect on myocardial fat.

In the human study, all the 56 patients had normal LV function with mean LVEF = 63.4 ± 7.9%. Compared to placebo, T2D patients treated with EMPA significantly lost weight (− 2.6 kg [− 1.2; − 3.7]) and improved their HbA1c by 0.88 ± 0.74%. Hematocrit and EPO levels were significantly increased in the EMPA group compared to placebo (p < 0.0001, p = 0.041). EMPA significantly increased glycosuria and plasma KB levels compared to placebo (p < 0.0001, p = 0.012, respectively), and significantly reduced liver fat content (− 27 ± 23 vs. − 2 ± 24%, p = 0.0005) and visceral fat (− 7.8% [− 15.3; − 5.6] vs. − 0.1% [− 1.1;6.5], p = 0.043), but had no effect on myocardial or epicardial fat. At 12 weeks, no significant change was observed in the myocardial PCr/ATP (p = 0.57 between groups).

Conclusions

EMPA effectively reduced liver fat in mice and humans without changing epicardial, myocardial fat or myocardial energetics, rebutting the thrifty substrate hypothesis for cardiovascular protection of SGLT2 inhibitors.

Trial registration NCT, NCT03118336. Registered 18 April 2017, https://clinicaltrials.gov/ct2/show/NCT03118336
High expression of CDCA7 predicts poor prognosis for clear cell renal cell carcinoma and explores its associations with immunity
Authors: Liu Shouyong , Wang Yi , Miao Chenkui , Xing Qianwei , Wang Zengjun
Published: March 2021

Background

Cell division cycle-associated 7 (CDCA7), as a member of the cell division cycle associated family, was reported to be aberrantly expressed in both solid tumors and hematological tumors, suggesting its essential role in promoting tumorigenesis. Hence, we aimed to explore its comprehensive roles of overall survival (OS) in clear cell renal cell carcinoma (ccRCC) and emphasize its associations with immunity.

Methods

The RNA sequencing data and corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) was adopted to explore CDCA7 associated signaling pathways. Univariate and multivariate Cox regression analyses were carried out to assess independent prognostic factors. Furthermore, roles of CDCA7 in human immunity were also investigated.

Results

Our results suggested that CDCA7 was overexpressed in ccRCC and its elevated expression was related to shorter OS (P < 0.01). Univariate and multivariate Cox regression analyses identified CDCA7 as an independent prognostic factor (both P < 0.05). The prognostic nomogram integrating CDCA7 expression level and clinicopathologic variables was constructed to predict 1-, 3- and 5-year OS. GSEA indicated that high CDCA7 expression was related to the apoptosis pathway, cell cycle pathway, JAK-STAT pathway, NOD like receptor pathway, P53 pathway, T cell receptor pathway and toll like receptor pathway, etc. Moreover, CDCA7 was significantly related to microsatellite instability (MSI, P < 0.001) and tumor mutational burden (TMB, P < 0.001). As for immunity, CDCA7 was remarkably associated with immune infiltration, tumor microenvironment, immune checkpoint molecules and immune pathways.

Conclusions

CDCA7 could serve as an independent prognostic factor for ccRCC and it was closely related to MSI, TMB, and immunity.