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Background and Objectives: Colorectal cancer (CRC) ranks as the third most prevalent cancer globally and is the third leading cause of cancer-related deaths. In 2020 alone, there were over 1.9 million new cases of CRC and nearly 0.9 million deaths worldwide. The incidence and outcomes of CRC exhibit significant geographical and temporal variations, largely influenced by diverse risk factors among populations. Recognizing the prognostic factors and the presenting symptoms of CRC, a leading global cancer with high mortality, can enhance early detection and thereby improve clinical outcomes. Materials and Methods: This retrospective, observational study analyzed 724 CRC elderly patients aged 70 and over (median age 80, 53.17% male), treated at a single center. Data on demographics, clinical characteristics, and outcomes were collected. Overall survival was analyzed using Kaplan–Meier curves, with stratification based on tumor location, disease staging, lymph node involvement, and family history. Results: Our study encompassed all CRC cases treated with surgery and systemic therapies (chemotherapy or biological agents) from July 2002 to September 2020. We focused on comparing prognosis between left-sided and right-sided CRC, as well as rectal cancer. We found that left-sided CRC demonstrated a superior prognosis compared to rectal cancer (p = 0.0022). Furthermore, among patients with CRC, tumors originating in the rectum were associated with worse outcomes compared to those arising in both the right and left colon, regardless of disease stage (p = 0.0049). Additionally, a family history of CRC was associated with poorer prognosis, impacting both metastatic (p = 0.0022) and localized disease (p = 0.035). The main symptoms prompting patients to start an investigation of CRC were abdominal pain (31.49%), anemia (18.08%), rectal bleeding (hematochezia) (17.82%), change in bowel habits (9.94%), and weight loss (7.60%). Conclusions: This study provides valuable insights into the symptoms prompting initial investigation and the prognostic factors associated with CRC in an elderly population with varied characteristics. It underscores the need for increased vigilance in recognizing key symptoms and the importance of personalized treatment strategies tailored to these prognostic factors.

Conducting clinical trials in inflammatory bowel disease (IBD) presents ongoing challenges that limit efficiency and trial outcomes. Key barriers include slow and unpredictable patient recruitment, variability in clinical practice across sites, and limited agility in responding to protocol changes. Advances in AI and machine learning now make it possible to transform patient- and site-level data into actionable, precision-guided insights that address longstanding challenges in development. At PhaseV, we are operationalizing this promise through our ClinOps Platform—a next-generation platform that harnesses the combined power of AI, causal machine learning, and real-time analytics to deliver trials that are adaptive, inclusive, and patient-centered from the outset. PhaseV’s ClinOps platform reframes site selection as a covariate-based optimization opportunity. Patient- and site-level covariates—including age, body mass index (BMI), ethnicity, genomic markers, comorbidities, and treatment history—are modeled using causal ML to estimate each site’s likelihood of achieving recruitment targets and data quality outcomes, aligned with protocol inclusion/exclusion (I/E) criteria and therapeutic mechanism of action. This enables precision-guided selection that enhances population diversity and efficiency. Once trials are underway, an interactive dashboard provides continuous oversight of recruitment, visit adherence, screening rates, protocol deviations, and data timeliness. This innovation is particularly critical in IBD, where heterogeneous disease presentation, fluctuating patient conditions, and evolving standards of care demand adaptive and precisely targeted trial strategies to achieve meaningful outcomes. PhaseV’s offering is further enhanced by a strategic collaboration with the Crohn’s & Colitis Foundation. By integrating insights from the Foundation’s IBD Plexus® Research Accelerator, a globally comprehensive repository of IBD patient data and biosamples, with PhaseV’s causal machine learning solutions, we amplify the power and precision of our analyses, enabling more patient-centric trials and advancing IBD research with adaptability. PhaseV’s approach transforms clinical operations in IBD trials from reactive tracking to intelligent orchestration. Every data point becomes an operational lever, every site an active contributor, and every trial a self-optimizing system—shifting from monitoring to optimization, and from hindsight to foresight. Most importantly, this means focusing on outcomes that matter to patients—timely access to therapies, better quality of life, and inclusive participation that reflects the diverse needs of the IBD community. By applying our clinical operations approach, we create trial environments that recruit faster, adapt smarter, and deliver more successful, patient-relevant results. Figure 1:

The enlargement of the liver and spleen (hepatosplenomegaly) is a common manifestation of Gaucher disease (GD). An accurate estimation of the liver and spleen volumes in patients with GD, using imaging tools such as magnetic resonance imaging (MRI), is crucial for the baseline assessment and monitoring of the response to treatment. A commonly used method in clinical practice to estimate the spleen volume is the employment of a formula that uses the measurements of the craniocaudal length, diameter, and thickness of the spleen in MRI. However, the inaccuracy of this formula is significant, which, in turn, emphasizes the need for a more precise and reliable alternative. To this end, we employed deep-learning techniques, to achieve a more accurate spleen segmentation and, subsequently, calculate the resulting spleen volume with higher accuracy on a testing set cohort of 20 patients with GD. Our results indicate that the mean error obtained using the deep-learning approach to spleen volume estimation is 3.6 ± 2.7%, which is significantly lower than the common formula approach, which resulted in a mean error of 13.9 ± 9.6%. These findings suggest that the integration of deep-learning methods into the clinical routine practice for spleen volume calculation could lead to improved diagnostic and monitoring outcomes.

The diagnosis of primary bone tumors is challenging, as the initial complaints are often non-specific. Early detection of bone cancer is crucial for a favorable prognosis. Incidentally, lesions may be found on radiographs obtained for other reasons. However, these early indications are often missed. In this work, we propose an automatic algorithm to detect bone lesions in conventional radiographs to facilitate early diagnosis. Detecting lesions in such radiographs is challenging: first, the prevalence of bone cancer is very low; any method must show high precision to avoid a prohibitive number of false alarms. Second, radiographs taken in health maintenance organizations (HMOs) or emergency departments (EDs) suffer from inherent diversity due to different X-ray machines, technicians and imaging protocols. This diversity poses a major challenge to any automatic analysis method. We propose to train an off-the-shelf object detection algorithm to detect lesions in radiographs. The novelty of our approach stems from a dedicated preprocessing stage that directly addresses the diversity of the data. The preprocessing consists of self-supervised region-of-interest detection using vision transformer (ViT), and a foreground-based histogram equalization for contrast enhancement to relevant regions only. We evaluate our method via a retrospective study that analyzes bone tumors on radiographs acquired from January 2003 to December 2018 under diverse acquisition protocols. Our method obtains 82.43% sensitivity at 1.5% false-positive rate and surpasses existing preprocessing methods. For lesion detection, our method achieves 82.5% accuracy and an IoU of 0.69. The proposed preprocessing method enables to effectively cope with the inherent diversity of radiographs acquired in HMOs and EDs.

Breast cancer is the most common malignancy in women. Mammographic findings such as microcalcifications and masses, as well as morphologic features of masses in sonographic scans, are the main diagnostic targets for tumor detection. However, improved specificity of these imaging modalities is required. A leading alternative target is neoangiogenesis. When pathological, it contributes to the development of numerous types of tumors, and the formation of metastases. Hence, demonstrating neoangiogenesis by visualization of the microvasculature may be of great importance. Super resolution ultrasound localization microscopy enables imaging of the microvasculature at the capillary level. Yet, challenges such as long reconstruction time, dependency on prior knowledge of the system Point Spread Function (PSF), and separability of the Ultrasound Contrast Agents (UCAs), need to be addressed for translation of super-resolution US into the clinic. In this work we use a deep neural network architecture that makes effective use of signal structure to address these challenges. We present in vivo human results of three different breast lesions acquired with a clinical US scanner. By leveraging our trained network, the microvasculature structure is recovered in a short time, without prior PSF knowledge, and without requiring separability of the UCAs. Each of the recoveries exhibits a different structure that corresponds with the known histological structure. This study demonstrates the feasibility of in vivo human super resolution, based on a clinical scanner, to increase US specificity for different breast lesions and promotes the use of US in the diagnosis of breast pathologies.