Exercise interventions for advanced cancer palliative care patients: A systematic literature review and descriptive evidence synthesis of randomized controlled trials.

BACKGROUND: Exercise is often recommended for cancer patients. However, for advanced cancer palliative care patients, it is unclear whether exercise, as a lifestyle intervention, is beneficial for palliative outcomes. AIM: To examine randomized controlled trials assessing the effectiveness of lifestyle exercise interventions on palliative outcomes in patients with advanced stage cancer. DESIGN: Systematic review and descriptive evidence synthesis. DATA SOURCES: Pubmed/Medline, Embase, CINAHL, PsychInfo, and Web of Science were systematically searched from inception to 2022. Two reviewers identified articles and removed duplicates. Next two reviewers independently screened titles and abstracts and then assessed full-texts articles for eligibility. Finally, all six reviewers examined full-text articles for eligibility and conducted the evidence synthesis. RESULTS: Eight randomized controlled trials were included. Studies were heterogeneous making direct comparisons challenging, but were grouped along three categories: aerobic, resistance, or resistance-aerobic exercises. One of three aerobic studies had positive quality-of-life outcomes. Fatigue improved in one aerobic and one combination resistance-aerobic study. Most resistance-aerobic studies and one aerobic study showed improved physical function. All resistance studies showed improvement in at least one outcome. Across all studies, ill health was the most common reason for participant dropout. The most commonly used assessment tools were: Functional Assessment of Cancer Therapy: Fatigue, European Organization for Research and Treatment of Cancer Quality-of-life Questionnaire Core 30, and accelerometer. CONCLUSION: Current randomized controlled trials regarding effects of exercise interventions on palliative outcomes for advanced cancer patients show great variability. While studies show promise, no generalizable conclusions can be made. Further research is needed.

A hierarchical anatomical framework and workflow for organizing stereotactic encephalography in epilepsy.

Stereotactic electroencephalography (SEEG) is an increasingly utilized method for invasive monitoring in patients with medically intractable epilepsy. Yet, the lack of standardization for labeling electrodes hinders communication among clinicians. A rational clustering of contacts based on anatomy rather than arbitrary physical leads may help clinical neurophysiologists interpret seizure networks. We identified SEEG electrodes on post-implant CTs and registered them to preoperative MRIs segmented according to an anatomical atlas. Individual contacts were automatically assigned to anatomical areas independent of lead. These contacts were then organized using a hierarchical anatomical schema for display and interpretation. Bipolar-referenced signal cross-correlations were used to compare the similarity of grouped signals within a conventional montage versus this anatomical montage. As a result, we developed a hierarchical organization for SEEG contacts using well-accepted, free software that is based solely on their post-implant anatomical location. When applied to three example SEEG cases for epilepsy, clusters of contacts that were anatomically related collapsed into standardized groups. Qualitatively, seizure events organized using this framework were better visually clustered compared to conventional schemes. Quantitatively, signals grouped by anatomical region were more similar to each other than electrode-based groups as measured by Pearson correlation. Further, we uploaded visualizations of SEEG reconstructions into the electronic medical record, rendering them durably useful given the interpretable electrode labels. In conclusion, we demonstrate a standardized, anatomically grounded approach to the organization of SEEG neuroimaging and electrophysiology data that may enable improved communication among and across surgical epilepsy teams and promote a clearer view of individual seizure networks.

DICOM Image ANalysis and Archive (DIANA): an Open-Source System for Clinical AI Applications.

In the era of data-driven medicine, rapid access and accurate interpretation of medical images are becoming increasingly important. The DICOM Image ANalysis and Archive (DIANA) system is an open-source, lightweight, and scalable Python interface that enables users to interact with hospital Picture Archiving and Communications Systems (PACS) to access such data. In this work, DIANA functionality was detailed and evaluated in the context of retrospective PACS data retrieval and two prospective clinical artificial intelligence (AI) pipelines: bone age (BA) estimation and intra-cranial hemorrhage (ICH) detection. DIANA orchestrates activity beginning with post-acquisition study discovery and ending with online notifications of findings. For AI applications, system latency (exam completion to system report time) was quantified and compared to that of clinicians (exam completion to initial report creation time). Mean DIANA latency was 9.04 ± 3.83 and 20.17 ± 10.16 min compared to clinician latency of 51.52 ± 58.9 and 65.62 ± 110.39 min for BA and ICH, respectively, with DIANA latencies being significantly lower (p < 0.001). DIANA's capabilities were also explored and found effective in retrieving and anonymizing protected health information for "big-data" medical imaging research and analysis. Mean per-image retrieval times were 1.12 ± 0.50 and 0.08 ± 0.01 s across x-ray and computed tomography studies, respectively. The data herein demonstrate that DIANA can flexibly integrate into existing hospital infrastructure and improve the process by which researchers/clinicians access imaging repository data. This results in a simplified workflow for large data retrieval and clinical integration of AI models.

Prognostication of patients with COVID-19 using artificial intelligence based on chest x-rays and clinical data: a retrospective study.

BACKGROUND: Chest x-ray is a relatively accessible, inexpensive, fast imaging modality that might be valuable in the prognostication of patients with COVID-19. We aimed to develop and evaluate an artificial intelligence system using chest x-rays and clinical data to predict disease severity and progression in patients with COVID-19. METHODS: We did a retrospective study in multiple hospitals in the University of Pennsylvania Health System in Philadelphia, PA, USA, and Brown University affiliated hospitals in Providence, RI, USA. Patients who presented to a hospital in the University of Pennsylvania Health System via the emergency department, with a diagnosis of COVID-19 confirmed by RT-PCR and with an available chest x-ray from their initial presentation or admission, were retrospectively identified and randomly divided into training, validation, and test sets (7:1:2). Using the chest x-rays as input to an EfficientNet deep neural network and clinical data, models were trained to predict the binary outcome of disease severity (ie, critical or non-critical). The deep-learning features extracted from the model and clinical data were used to build time-to-event models to predict the risk of disease progression. The models were externally tested on patients who presented to an independent multicentre institution, Brown University affiliated hospitals, and compared with severity scores provided by radiologists. FINDINGS: 1834 patients who presented via the University of Pennsylvania Health System between March 9 and July 20, 2020, were identified and assigned to the model training (n=1285), validation (n=183), or testing (n=366) sets. 475 patients who presented via the Brown University affiliated hospitals between March 1 and July 18, 2020, were identified for external testing of the models. When chest x-rays were added to clinical data for severity prediction, area under the receiver operating characteristic curve (ROC-AUC) increased from 0·821 (95% CI 0·796-0·828) to 0·846 (0·815-0·852; p<0·0001) on internal testing and 0·731 (0·712-0·738) to 0·792 (0·780-0 ·803; p<0·0001) on external testing. When deep-learning features were added to clinical data for progression prediction, the concordance index (C-index) increased from 0·769 (0·755-0·786) to 0·805 (0·800-0·820; p<0·0001) on internal testing and 0·707 (0·695-0·729) to 0·752 (0·739-0·764; p<0·0001) on external testing. The image and clinical data combined model had significantly better prognostic performance than combined severity scores and clinical data on internal testing (C-index 0·805 vs 0·781; p=0·0002) and external testing (C-index 0·752 vs 0·715; p<0·0001). INTERPRETATION: In patients with COVID-19, artificial intelligence based on chest x-rays had better prognostic performance than clinical data or radiologist-derived severity scores. Using artificial intelligence, chest x-rays can augment clinical data in predicting the risk of progression to critical illness in patients with COVID-19. FUNDING: Brown University, Amazon Web Services Diagnostic Development Initiative, Radiological Society of North America, National Cancer Institute and National Institute of Biomedical Imaging and Bioengineering of the National Institutes of Health.

Machine Learning-Based Prediction of COVID-19 Severity and Progression to Critical Illness Using CT Imaging and Clinical Data.

OBJECTIVE: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. MATERIALS AND METHODS: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. RESULTS: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. CONCLUSION: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

Ablation Therapy for Advanced Stage Non-Small Cell Lung Cancer: A National Cancer Database Study.

PURPOSE: To compare overall survival (OS) of ablation with no treatment for patients with advanced stage non-small cell lung cancer. METHODS: Patients with clinical stage IIIB (T1-4N3M0, T4N2M0) and stage IV (T1-4N0-3M1) non-small cell lung cancer, in accordance with the American Joint Committee on Cancer, 7th edition, who did not receive treatment or who received ablation as their sole primary treatment besides chemotherapy from 2004 to 2014, were identified from the National Cancer Data Base. OS was estimated using the Kaplan-Meier method and evaluated by log-rank test, univariate and multivariate Cox proportional hazard regression, and propensity score-matched analysis. Relative survival analyses comparing age- and sex-matched United States populations were performed. RESULTS: A total of 140,819 patients were included. The 1-, 2-, 3- and 5-year survival rates relative to age- and sex-matched United States population were 28%, 18%, 12%, and 10%, respectively, for ablation (n = 249); and 30%, 15%, 9%, and 5%, respectively for no treatment (n = 140,570). Propensity score matching resulted in 249 patients in the ablation group versus 498 patients in the no-treatment group. After matching, ablation was associated with longer OS than that in the no-treatment group (median, 5.9 vs 4.7 months, respectively; hazard ratio, 0.844; 95% confidence interval, 0.719-0.990; P = .037). These results persisted in patients with an initial tumor size of ≤3 cm. CONCLUSIONS: Preliminary results suggest ablation may be associated with longer OS in patients with late-stage non-small cell lung cancer than survival in those who received no treatment.

Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT.

Background Coronavirus disease 2019 (COVID-19) and pneumonia of other diseases share similar CT characteristics, which contributes to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system for differentiating COVID-19 and other pneumonia at chest CT and assessing radiologist performance without and with AI assistance. Materials and Methods A total of 521 patients with positive reverse transcription polymerase chain reaction results for COVID-19 and abnormal chest CT findings were retrospectively identified from 10 hospitals from January 2020 to April 2020. A total of 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia at chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by a two-layer fully connected neural network to pool slices together. The final cohort of 1186 patients (132 583 CT slices) was divided into training, validation, and test sets in a 7:2:1 and equal ratio. Independent testing was performed by evaluating model performance in separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results The final model achieved a test accuracy of 96% (95% confidence interval [CI]: 90%, 98%), a sensitivity of 95% (95% CI: 83%, 100%), and a specificity of 96% (95% CI: 88%, 99%) with area under the receiver operating characteristic curve of 0.95 and area under the precision-recall curve of 0.90. On independent testing, this model achieved an accuracy of 87% (95% CI: 82%, 90%), a sensitivity of 89% (95% CI: 81%, 94%), and a specificity of 86% (95% CI: 80%, 90%) with area under the receiver operating characteristic curve of 0.90 and area under the precision-recall curve of 0.87. Assisted by the probabilities of the model, the radiologists achieved a higher average test accuracy (90% vs 85%, Δ = 5, P < .001), sensitivity (88% vs 79%, Δ = 9, P < .001), and specificity (91% vs 88%, Δ = 3, P = .001). Conclusion Artificial intelligence assistance improved radiologists' performance in distinguishing coronavirus disease 2019 pneumonia from non-coronavirus disease 2019 pneumonia at chest CT. © RSNA, 2020 Online supplemental material is available for this article.

Performance of Radiologists in Differentiating COVID-19 from Non-COVID-19 Viral Pneumonia at Chest CT.

Background Despite its high sensitivity in diagnosing coronavirus disease 2019 (COVID-19) in a screening population, the chest CT appearance of COVID-19 pneumonia is thought to be nonspecific. Purpose To assess the performance of radiologists in the United States and China in differentiating COVID-19 from viral pneumonia at chest CT. Materials and Methods In this study, 219 patients with positive COVID-19, as determined with reverse-transcription polymerase chain reaction (RT-PCR) and abnormal chest CT findings, were retrospectively identified from seven Chinese hospitals in Hunan Province, China, from January 6 to February 20, 2020. Two hundred five patients with positive respiratory pathogen panel results for viral pneumonia and CT findings consistent with or highly suspicious for pneumonia, according to original radiologic interpretation within 7 days of each other, were identified from Rhode Island Hospital in Providence, RI. Three radiologists from China reviewed all chest CT scans (n = 424) blinded to RT-PCR findings to differentiate COVID-19 from viral pneumonia. A sample of 58 age-matched patients was randomly selected and evaluated by four radiologists from the United States in a similar fashion. Different CT features were recorded and compared between the two groups. Results For all chest CT scans (n = 424), the accuracy of the three radiologists from China in differentiating COVID-19 from non-COVID-19 viral pneumonia was 83% (350 of 424), 80% (338 of 424), and 60% (255 of 424). In the randomly selected sample (n = 58), the sensitivities of three radiologists from China and four radiologists from the United States were 80%, 67%, 97%, 93%, 83%, 73%, and 70%, respectively. The corresponding specificities of the same readers were 100%, 93%, 7%, 100%, 93%, 93%, and 100%, respectively. Compared with non-COVID-19 pneumonia, COVID-19 pneumonia was more likely to have a peripheral distribution (80% vs 57%, P < .001), ground-glass opacity (91% vs 68%, P < .001), fine reticular opacity (56% vs 22%, P < .001), and vascular thickening (59% vs 22%, P < .001), but it was less likely to have a central and peripheral distribution (14% vs 35%, P < .001), pleural effusion (4% vs 39%, P < .001), or lymphadenopathy (3% vs 10%, P = .002). Conclusion Radiologists in China and in the United States distinguished coronavirus disease 2019 from viral pneumonia at chest CT with moderate to high accuracy. © RSNA, 2020 Online supplemental material is available for this article. A translation of this abstract in Farsi is available in the supplement. ترجمه چکیده این مقاله به فارسی، در ضمیمه موجود است.

Novel Circulating Tumor Cell Assay for Detection of Colorectal Adenomas and Cancer.

OBJECTIVES: There is a significant unmet need for a blood test with adequate sensitivity to detect colorectal cancer (CRC) and adenomas. We describe a novel circulating tumor cell (CTC) platform to capture colorectal epithelial cells associated with CRC and adenomas. METHODS: Blood was collected from 667 Taiwanese adults from 2012 to 2018 before a colonoscopy. The study population included healthy control subjects, patients with adenomas, and those with stage I-IV CRC. CTCs were isolated from the blood using the CellMax platform. The isolated cells were enumerated, and an algorithm was used to determine the likelihood of detecting adenoma or CRC. Nominal and ordinal logistic regression demonstrated that CTC counts could identify adenomas and CRC, including CRC stage. RESULTS: The CellMax test demonstrated a significant association between CTC counts and worsening disease status (Cuzick's P value < 0.0001) with respect to the adenoma-carcinoma sequence. The test showed high specificity (86%) and sensitivity across all CRC stages (95%) and adenomatous lesions (79%). The area under the curve was 0.940 and 0.868 for the detection of CRC and adenomas, respectively. DISCUSSION: The blood-based CTC platform demonstrated high sensitivity in detecting adenomas and CRC, as well as reasonable specificity in an enriched symptomatic patient population. TRANSLATIONAL IMPACT: If these results are reproduced in an average risk population, this test has the potential to prevent CRC by improving patient compliance and detecting precancerous adenomas, eventually reducing CRC mortality.

Analytical validation of the CellMax platform for early detection of cancer by enumeration of rare circulating tumor cells.

The CellMax (CMx®) platform was developed to enrich for epithelial circulating tumor cells (CTCs) in the whole blood. This report provides assay performance data, including accuracy, linearity, limit of blank, limit of detection (LOD), specificity, and precision of enumeration of cancer cell line cells (CLCs) spiked in cell culture medium or healthy donor blood samples. Additionally, assay specificity was demonstrated in 32 young healthy donors and clinical feasibility was demonstrated in a cohort of 47 subjects consisting of healthy donors and patients who were colonoscopy verified to have colorectal cancer, adenomas, or a negative result. The CMx platform demonstrated high accuracy, linearity, and sensitivity for the enumeration of all CLC concentrations tested, including the extremely low range of 1 to 10 cells in 2 mL of blood, which is most relevant for early cancer detection. Theoretically, the assay LOD is 0.71 CTCs in 2 mL of blood. The analytical specificity was 100% demonstrated using 32 young healthy donor samples. We also demonstrated precision across multiple days and multiple operators, with good reproducibility of recovery efficiency. In a clinical feasibility study, the CMx platform identified 8 of 10 diseased subjects as positive (80% clinical sensitivity) and 4 of 5 controls as negative (80% clinical specificity). We also compared processing time and transportation effects for similar blood samples from two different sites and assessed an artificial intelligence-based counting method. Finally, unlike other platforms for which captured CTCs are retained on ferromagnetic beads or tethered to the slide surface, the CMx platform's unique airfoam-enabled release of CTCs allows captured cells to be transferred from a microfluidic chip to an Eppendorf tube, enabling a seamless transition to downstream applications such as genetic analyses and live cell manipulations.

Exploring Glycan Markers for Immunotyping and Precision-targeting of Breast Circulating Tumor Cells.

BACKGROUND AND AIMS: Recognition of abnormal glycosylation in virtually every cancer type has raised great interest in exploration of the tumor glycome for biomarker discovery. Identifying glycan markers of circulating tumor cells (CTCs) represents a new development in tumor biomarker discovery. The aim of this study was to establish an experimental approach to enable rapid screening of CTCs for glycan marker identification and characterization. METHODS: We applied carbohydrate microarrays and a high-speed fiber-optic array scanning technology (FAST scan) to explore potential glycan markers of breast CTCs (bCTCs) and targeting antibodies. An anti-tumor monoclonal antibody, HAE3-C1 (C1), was identified as a key immunological probe in this study. RESULTS: In our carbohydrate microarray analysis, C1 was found to be highly specific for an O-glycan cryptic epitope, gp(C1). Using FAST-scan technology, we established a procedure to quantify expression levels of gp(C1) in tumor cells. In blood samples from five stage IV metastatic breast cancer patients, the gp(C1) positive CTCs were detected in all subjects; ∼40% of bCTCs were strongly gp(C1) positive. Interestingly, CTCs from a triple-negative breast cancer patient with multiple sites of metastasis were predominantly gp(C1) positive (92.5%, 37/40 CTCs). CONCLUSIONS: Together we present here a practical approach to examine rare cell expression of glycan markers. Using this approach, we identified an O-core glyco-determinant gp(C1) as a potential immunological target of bCTCs. Given its bCTC-expression profile, this target warrants an extended investigation in a larger cohort of breast cancer patients.

Case study of the morphologic variation of circulating tumor cells.

We report a detailed cytomorphologic evaluation of the circulating component of widely metastatic breast carcinoma. A previously healthy 38-year-old woman was diagnosed with breast cancer. Wide local excision revealed a 1.7-cm infiltrating ductal adenocarcinoma, BSR score 7/9 with angiolymphatic invasion, and 4/20 lymph nodes positive for carcinoma. Five years later, a bone marrow biopsy revealed involvement of bone marrow by metastatic breast carcinoma, and shortly thereafter, metastases were identified in the liver and lung hilum. She enrolled in a clinical investigation for the detection of circulating tumor cells (CTCs) in breast carcinoma. A total of 659 CTCs were identified in a 10-mL blood sample using an immunofluorescent protocol targeting cytokeratins and detected using fiber-optic array scanning technology. The detected CTCs were subsequently stained with a Wright-Giemsa stain, and representative cells were evaluated in detail by light microscopy for morphologic evaluation. We find that the patient's CTCs exhibit a high degree of pleomorphism including CTCs with high and low nuclear-to-cytoplasmic ratios along with CTCs exhibiting early and late apoptotic changes. In addition, in comparison with her tumor cells in other sites, the full morphologic spectrum of cancer cells present in primary and metastatic tumor is also present in peripheral blood circulation.