Biomarkers in colorectal cancer (CRC) are of great interest in the current literature due to improvements in techniques such as liquid biopsy and next-generation sequencing (NGS). However, screening methods vary globally, with multi-target stool DNA (mt-sDNA) predominantly used in the USA and, more recently, the Cologuard Plus; biomarkers such as the Galectins family and septins show promise in early detection. Gut microbiome assessments, such as Fusobacterium nucleatum, are under intense exploration. Diagnostic tests, such as circulating DNA analysis via NGS, exhibit effectiveness and are being increasingly adopted. Circulating tumor cells emerge as potential alternatives to traditional methods in terms of diagnosis and prognosis. Predictive biomarkers are well established in guidelines; nonetheless, with the aid of machine learning and artificial intelligence, these biomarkers may be improved. This review critically explores the actual dynamic landscape of CRC biomarkers and future, promising biomarkers involved in screening, diagnosis, and prognosis.

Rectal cancer (RC) presents significant challenges in diagnosis and treatment, with increasing incidence among younger populations. Treatment approaches, particularly for locally advanced rectal cancer (LARC), have evolved, notably with the introduction of total neoadjuvant therapy (TNT). TNT combines neoadjuvant chemotherapy and chemoradiotherapy before surgery, improving overall survival and reducing both metastasis and local recurrence rates compared to traditional methods, while enabling more patients to complete the full oncological treatment. Clinical trials, such as RAPIDO, OPRA, and PRODIGE 23, have demonstrated the effectiveness of TNT in tumor downstaging and complete pathological responses, offering better outcomes for patients; however, debates persist regarding the role of neoadjuvant radiotherapy, with novel strategies exploring its omission in specific cases to reduce toxicity and enhance quality of life. In addition, organ preservation strategies, such as the watch-and-wait (WW) approach, have emerged as viable options for patients with a complete response to neoadjuvant therapy. Future directions point towards personalized treatment plans incorporating radiogenomics and the integration of artificial intelligence into diagnostics to optimize patient outcomes. This review aims to synthesize current treatment strategies and ongoing advancements in rectal cancer management, providing insights into potential future innovations.

Background and clinical significance: Phyllodes tumors (PTs) are rare stromal neoplasms originating in the connective tissue of the breast, distinct from carcinomas that arise from the ducts or lobules. These tumors exhibit a broad spectrum of morphologic features and are traditionally classified as benign, borderline, or malignant. Case presentation: We present the case of a 71-year-old female diagnosed with a malignant PT and treated at our hospital. The patient noticed a gradually enlarging lump in her right breast over several months. Mammography was inconclusive, but an ultrasound later revealed a lobulated, firm mass, classified as BIRADS 5. Physical examination identified a 20 cm mass, and core needle biopsy suggested a borderline PT. Following lumpectomy, pathology confirmed a malignant tumor with narrow surgical margins (0.1 cm). Although mastectomy was recommended to achieve wider margins, the patient opted for adjuvant radiotherapy. She received 50 Gy in 25 fractions to the whole breast, followed by a 16 Gy boost to the tumor bed in 8 fractions. The treatment was well tolerated and completed successfully. Initially, the patient’s therapeutic management was delayed due to a combination of personal and organizational factors. However, the process was later streamlined through the use of a novel digital tool developed to facilitate the entire patient journey within our hospital system. Conclusions: This case highlights the diagnostic complexities of PTs, the critical need for effective collaboration between specialties, and the importance of timely treatment planning for optimal patient outcomes.

Starting from the importance of conforming to biological reality in medicine, in this paper we propose an optimization technique for increasing resolution of computed tomography (CT) images acquired using various existing scanners. Considering a three-dimensional Hounsfield Units (HU) array, together with the corresponding spatial metadata of interest (pixel sizes and slice thickness), the procedure is based on halving each voxel along the directions of the device's Cartesian frame of reference and find those values which are both satisfying the X-Rays attenuation coefficient average requirement and minimizing the HU distance to classical interpolation points. The discussed method was tested by implementing a C# .Net 6, cross-platform library containing two algorithm flavors that could be independently applied: “Z” for doubling the number of slices, and “XY” for doubling the resolution of individual slices. This design allows also chaining (e.g. one could apply the “Z,XY,Z” sequence in order to reduce four times slice thickness). In the context of existing unavoidable limitations, the first results are suggesting the “CT compatible” interpolation technique could provide a reasonable approximation of reality. However, the main advantage comes from satisfying mass conservation, which is of high importance in medical diagnosis and treatment. • The Hounsfield Units scale is defined as a linear transformation of the X-Rays attenuation coefficients. Thus, splitting a computed tomography voxel into two congruent volumes must satisfy the HU average requirement (the initial value must equal the average of the two output HU values). • Existing interpolation methods (linear, spline, etc.) are not compatible with the computed tomography HU average requirement. This could also result in mass estimate anomalies with significant impact in medical diagnosis. • The proposed “CT compatible” interpolation method is based on finding those values which are both satisfying the X-Rays attenuation coefficient average requirement and minimizing the Hounsfield Units distance to classical interpolation points.

The Systems Theory provides a valuable conceptual framework for analyzing biological complexity, particularly in oncology, where the multiple interactions between biological subsystems can influence tumor initiation, progression, and treatment response. This approach facilitates the integration of data derived from genomics, metabolomics, routine medical analyses, and the clinical examination of the patient, thus overcoming the limitations of the reductionist model. By applying the principles of living systems, new tumor vulnerabilities can be identified, as well as models of cellular, metabolic, and informational organization. The use of systems theory in oncology may contribute to the development of personalized and precision medicine, improving diagnostic and prognostic methods, and enhancing the efficiency of personalized therapies with the aim of improving therapeutic outcomes and the patient’s quality of life.