PriMera Scientific Medicine and Public Health (ISSN: 2833-5627)

Editorial

Volume 7 Issue 2

Automated Next-Gen Oncology through Robotics and Analytics (ANORA) for Breast Cancer

Maritess D Cation*

August 01, 2025

Abstract

In the Japanese anime Cells at Work!, anthropomorphized cells patrol blood channels, fight off invading germs, and keep the body in perfect operation. It gives a simpler but still interesting picture of how cells work together, protect themselves, and fix things to keep balance at the cellular level. The series is fun, and its point is straightforward: the body is a battleground of tiny complexity, and early detection and tailored response are key to survival.

This metaphor is especially powerful when it comes to breast cancer, as cells grow uncontrollably fast, evolve to evade detection, and don't respond to standard chemotherapies. Science in fiction is nearly aligned with current advancements in artificial intelligence (AI), omics, nanotechnology, robotics, and more sophisticated imaging systems, all of which are coming together to transform how we find, combat, and control cancer. With these advancements, it will soon be possible to manage breast cancer effectively and deliver drugs precisely to the right locations, helping to preserve healthy tissue and improve treatment outcomes.

Data-Independent Acquisition (DIA) Mass Spectrometry, especially using SWATH-MS (Sequential Window Acquisition of All Theoretical Mass Spectra), is still the most advanced and comprehensive approach to breast cancer proteomics. The latest ultra-high-resolution mass spectrometers and DIA make it easier to find the unique protein expression patterns in breast cancers, which helps identify protein biomarkers for diagnosis, prognosis, and treatment response. These patterns are like molecular "fingerprints" that help guide precise, targeted treatments. Coupled with AI, it allows a new perspective on these multi-omic profiles and medical images in real time, which is the best help for diagnosis, risk assessment, and personalized medical care planning.

Nanotechnology has also brought us the "Cancer in a Drop" age, which includes liquid biopsy platforms that can find tumor-derived materials, including circulating tumor DNA (ctDNA), exosomes, and proteins in a single drop of blood. Researchers are looking at nanorobotics and soft robotics to see whether they can precisely administer therapies directly onto tumors or act as in vivo sensors for minimally invasive interventions.

Enhanced imaging systems like photoacoustic computed tomography (PACT), photon-counting computed tomography (PCCT), wearable ultrasound, contrast-enhanced spectral mammography (CSEM), digital breast tomosynthesis (DBT), and AI models that use radiomics now offer very accurate and non-invasive ways to diagnose with great detail. Enhanced by AI, these systems help doctors find tumors earlier and more accurately using the images developed using ionizing radiation and contrast agents. PACT, however, is the only approach to imaging that is safer for repeated use since it uses laser light and ultrasound, both of which are non-ionizing forms of energy.

The convergence of these new ideas represents a significant shift in breast cancer care from broad, invasive procedures and one-size-fits-all therapies to precision oncology based on early detection, real-time analytics, and patient-specific strategies. Automated Next-Gen Oncology through Robotics and Analytics, or simply "ANORA," may soon redefine cancer and its complete transformation, envisioned as the union of advanced imaging, multi-omic profiling, AI-powered diagnostics, and nanorobotic delivery platform. ANORA is not merely a concept; it is a call to action—towards patient care that is profoundly more gentle, exact, and rapid.

References

  1. Cation MD and Ramos MC. “Accelerating Breast Cancer Biomarker Discovery by Mass Spectrometry Proteomics and Current Bioinformatics Tools”. Asian Pacific Journal of Cancer Biology 7.3 (2022): 275-283.
  2. Roberto Borea., et al. “Cancer in a Drop: advances in liquid biopsy in 2024”. Crit Rev Oncol Hematol (2025).
  3. Tong X., et al. “Panoramic photoacoustic computed tomography with learning-based classification enhances breast lesion characterization”. Nat Biomed Eng (2025).
  4. UCL Research. “Hand-held 3D photoacoustic scanner for cancer diagnosis”. Nat Biomed Eng (2024).
  5. Berg WA., et al. “Contrast‑enhanced mammography non‑inferior to MRI in dense breast patients (SCEMAM)”. Radiology (2025).
  6. Berg WA et al. “TOCEM interim: enhanced detection in high-risk dense women”. Radiology (2024).
  7. Katalinic A., et al. “Nationwide AI‑supported mammography screening (PRAIM) increases detection rate”. Nat Med (2024).
  8. Yang Z and Liu C. “Research on the application of radiomics in breast cancer: bibliometrics and visualization (Medicine)”. Medicine 103.35 (2024): e39463.
  9. Lee J., et al. “A deep learning approach for virtual contrast enhancement in CESM”. Biosens. Bioelectron (2024): 102398.