two path book reviews audio script

 

Two introductions to digital pathology

Luis Cano’s Getting Started with Digital Pathology and Aleksandra Zuraw’s Digital Pathology 101, second edition, offer complementary introductions to a field whose scientific possibilities have advanced faster than many professionals’ ability to evaluate them. Cano provides a concise entry into computational tissue analysis and the professional skills needed to begin. Zuraw offers a substantially broader account of the technology, organizational work, and validation required to make digital pathology function in practice. Readers entering the field can profit from both; readers responsible for implementation will find Zuraw the more substantial foundation.

The distinction matters because “digital pathology” encompasses several different activities: viewing scanned slides, managing diagnostic workflows, quantifying tissue features, and deriving clinically meaningful predictions from images. Both authors recognize these differences. Their books are best understood as professional orientation guides, rather than comprehensive textbooks or substitutes for technical and regulatory source documents.

This review evaluates the supplied 48-page Cano PDF and 179-page Zuraw PDF. Page references below refer to PDF page positions, including covers, rather than printed pagination.

The authors and their perspectives

Luis Cano Ayestas is a medical pathologist with a PhD in oncology and the author of Beyond the Slide, a publication addressing digital pathology, AI, and diagnostic systems. His public LinkedIn listing identifies a Paris-based independent professional and lists Université de Rennes I. His own website describes an interest in connecting histologic interpretation with computational methods and healthcare strategy. In the book, he recounts training in Peru, doctoral study in France, and gradually acquiring skills in R, QuPath, and Python. That trajectory informs his central educational message: clinical expertise provides a useful starting point for computational work, even before a physician develops advanced technical skills. [1–3]

Aleksandra Zuraw, DVM, PhD, DACVP, is a board-certified veterinary pathologist, founder of Digital Pathology Place, and host of the Digital Pathology Podcast. Her professional background includes tissue image analysis, pharmaceutical biomarker work, and toxicologic pathology. Her public LinkedIn listing and author website support that combination of pathology, education, and technology. The book draws extensively on her work with image analysis scientists and on interviews with clinicians, developers, and industry leaders. Her veterinary background gives the review of digital pathology a useful breadth, particularly where human clinical pathology intersects with preclinical drug development. [4–5]

Where the scope differs

Dimension	Cano	Zuraw
Organizing purpose	Help a newcomer understand the opportunities and begin participating	Explain the ecosystem and prepare readers to evaluate and implement it
Technology coverage	Brief WSI introduction; emphasis on analysis software and computational tasks	More extensive treatment of scanners, image management, storage, interoperability, and displays
Image analysis	Concentrated discussion of quantification, biomarker scoring, segmentation, and spatial relationships	More extended teaching of computer vision, machine learning, transfer learning, and foundation models
Practical implementation	Initial skills, resources, mentoring, and small projects	Workflow redesign, validation, software deployment, monitoring, and organizational change
Drug development	Translational research and clinical trial applications	Clinical development plus a dedicated treatment of toxicologic pathology and GLP workflows
Learning style	Concise explanations, takeaway lists, software examples, and personal advice	Conversational explanations, diagrams, repeated case examples, and links to interviews and courses

These differences are not simply consequences of length. Cano spends a large proportion of his short book explaining what can be extracted from tissue images. Zuraw devotes considerably more space to the conditions under which extraction, interpretation, and reporting become dependable institutional services.

Cano’s strongest contribution

Cano is particularly effective at connecting computational terminology with biologic questions. His discussion of cell quantification progresses from counting cells to identifying clinically relevant populations and interpreting their proportions. His treatment of spatial analysis then asks where those cells lie in relation to tumor, stroma, vessels, and one another. Examples involving immune infiltration and tumor microenvironments explain why tissue architecture contains information that a simple positive-cell percentage cannot capture (PDF pp. 20–28).

This is a useful introduction for a reader who understands histopathology but has not yet considered how to formulate an image analysis problem. Semantic and instance segmentation become intelligible through their consequences: identifying a tissue compartment requires a different representation from separating and counting individual nuclei. The discussion of nearest-neighbor distances, clustering, and related spatial measures broadens the reader’s conception of what digital pathology can measure, although it does not teach those methods in sufficient detail to implement them independently.

The software survey also provides an accessible starting point. QuPath, HALO, and Aiforia represent different approaches to access, support, customization, and model development. Cano’s strongest practical recommendation is to begin with available images and a manageable analytic task. His references to public datasets and learning communities make that recommendation actionable without requiring a laboratory to purchase a scanner first (PDF pp. 17–19, 33–38).

His personal account is relevant to the educational purpose. It addresses a recognizable barrier: pathologists may assume they must master programming and neural-network mathematics before they can contribute. Cano instead explains how a clinician can define meaningful questions, annotate responsibly, and collaborate with technical colleagues. For residents and early-career investigators, this may be the book’s most valuable contribution.

The limitations follow from the same approach. The software descriptions provide orientation rather than a rigorous purchasing comparison. The guide is also more encouraging than discriminating in some descriptions of clinical benefit. Statements about improved accuracy, molecular prediction, or reproducibility need to be read in relation to the particular task and validation setting. Success on a research dataset does not establish that an algorithm can replace an indicated molecular assay or improve treatment decisions.

There are also editorial weaknesses in the referencing. The opening definition is attributed to USCAP, while reference 1 identifies the Royal College of Pathologists. A discussion invoking Borowsky’s diagnostic noninferiority work points to a reference listing Barisoni’s nephropathology review. These mismatches do not invalidate the introductory account, but they make verification less straightforward than a professional reader should expect.

Zuraw’s strongest contribution

Zuraw’s principal strength is explaining why a working digital pathology service requires much more than image acquisition or a high-performing algorithm. Her treatment connects scanner performance and image quality with storage, file formats, image management, laboratory information systems, and the pathologist’s workstation. The reader gains a clearer understanding of how a failure in one component can affect the entire diagnostic process (PDF pp. 36–49, 61–73).

The image analysis teaching is also stronger than the title “101” might suggest. Comparisons among object detection, semantic segmentation, and instance segmentation tie technical choices to the information required from an image. Zuraw repeatedly asks whether a simpler method can accomplish the task adequately, and she connects computational complexity with processing time and infrastructure demands. This helps readers evaluate a proposed solution on its intended function rather than the sophistication of its architecture (PDF pp. 97–119).

The most valuable section concerns deployment. Drawing on Andrew Janowczyk’s account of an H. pylori detection tool, Zuraw describes a project in which algorithm development took approximately two weeks and clinical deployment took three years. The lesson is developed through specific requirements: stable software dependencies, documentation, error handling, integration, defined responsibilities, and continued monitoring. Her explanation of a locked model operating within a changing laboratory environment makes performance drift concrete (PDF pp. 142–154).

The dedicated chapter on toxicologic pathology is the book’s clearest distinctive contribution. It explains why cohort-based evaluation across animals and dose groups differs from diagnosing an individual patient, then connects digital workflows with peer review, electronic records, auditability, and Good Laboratory Practice. Readers whose experience is confined to clinical oncology may find this chapter especially useful because it exposes an important application of digital pathology outside routine patient diagnosis (PDF pp. 160–172).

Zuraw also gives more attention to bias, human interaction with AI, generative systems, and data governance. She recognizes that pathologists can reinforce an algorithm’s errors rather than automatically correct them. Her discussion therefore provides a useful counterweight to simpler descriptions of AI as an invariably beneficial second reader.

The tradeoff is a longer and sometimes repetitive book. Cases and motivational themes recur, and references to the author’s courses, podcast, and subscriptions periodically interrupt the exposition. Interview material supplies valuable operational detail, but individual experiences cannot establish how typical an outcome or implementation timeline will be. A graduate-level reader should distinguish those accounts from independent comparative evidence.

Regulatory statements also require careful checking. For example, an early passage states broadly that a remote home office must meet CLIA certification requirements; later passages describe continued flexibility for digital review. CMS’s post-emergency guidance permits qualifying remote digital review under the primary laboratory’s certificate, subject to specified conditions. The book would benefit from a more consistent presentation of that distinction (PDF pp. 24, 28–29). [6]

Important topics that remain insufficiently developed

The most consequential shared limitation is the lack of a systematic framework for judging clinical utility. Both books discuss validation, and Zuraw covers it substantially more thoroughly. Neither consistently separates technical performance, diagnostic accuracy, changes in management, and improved patient outcomes. A prognostic association does not by itself demonstrate prediction of treatment benefit. Likewise, detecting additional abnormalities does not establish that acting on them improves care.

This distinction is particularly relevant to Cano’s statement that the McGenity systematic review confirmed clinical utility. That review evaluated diagnostic test accuracy and emphasized limitations in the underlying evidence. It should not be treated as a general demonstration that AI-guided care improves outcomes. A stronger evidence chapter would teach readers how to assess external validation, patient-level data separation, calibration, treatment interactions, and prospective workflow or outcome studies. [7]

Reimbursement and market access are another substantial gap. Cano mentions Category III digitization codes as emerging reimbursement opportunities, while Zuraw more cautiously emphasizes that digital conversion often creates operational value without separate revenue. Neither supplies a sufficient framework for distinguishing a code’s existence from coverage, payment methodology, or actual collection. CAP describes the relevant digitization codes as reporting mechanisms without assigned RVUs or established payment. A business case should therefore identify the payer and payment setting before assigning revenue to them. [8]

Both books also stop short of a complete procurement and financial assessment. Zuraw discusses total ownership costs and ongoing expenses; Cano points readers toward ROI resources. Neither develops a worked model incorporating scanning labor, rescans, validation time, storage growth, downtime, and achievable changes in case throughput. They give readers useful questions, but not enough information to approve a major investment.

Data governance receives attention, especially in Zuraw, but neither provides detailed guidance on ownership and licensing of images, annotations, derived models, and secondary commercial uses. Similarly, interoperability is discussed without sufficient detail to design and acceptance-test an enterprise integration. Procurement contracts, cybersecurity recovery exercises, vendor exit arrangements, and the allocation of responsibility after an AI error would all require additional sources and specialist advice.

Finally, neither is a hands-on methods manual. Cano encourages experimentation and Zuraw explains concepts clearly, but readers seeking a reproducible analysis pipeline will need software documentation, exercises, and a defined validation protocol. Their common emphasis on histologic whole-slide imaging also leaves cytology and other technically demanding specimen types less fully treated.

Recommendations to readers

Readers seeking a short introduction before exploring quantitative histology should start with Cano. His guide is especially suitable for pathology residents, clinicians entering translational research, and investigators who need to turn a biologic question into a tractable image analysis task. Its value is greatest when reading leads immediately to a small exercise with real images.

Readers evaluating a service, selecting technology, or organizing implementation should give priority to Zuraw. Her broader treatment is more useful to laboratory directors, implementation teams, pathology informaticists, pharmaceutical scientists, and professionals moving between clinical and preclinical settings. Chapters on AI deployment and toxicologic pathology deserve attention even from readers already familiar with basic WSI concepts.

Both books are worth reading for professionals who must connect scientific opportunity with practical adoption: a pathologist serving as the clinical lead on a computational project, a translational scientist moving an image-derived biomarker toward clinical use, or a product manager who needs to understand both tissue analysis and laboratory operations. Cano provides a quick introduction to the questions digital tissue analysis can answer; Zuraw helps the reader recognize the work required to answer them reliably in practice.

For those readers, a sensible sequence is Cano first, followed by Zuraw, with particular attention to validation, deployment, and the regulatory setting relevant to the intended use. A concrete implementation or investment decision should then be supported by current primary guidance, independent evidence for the specific application, and a local financial analysis. The books provide a useful foundation for that work and help readers recognize where further expertise is needed.

Sources and author links

Primary texts: Luis Cano, Getting Started with Digital Pathology, supplied 48-page PDF; Aleksandra Zuraw, Digital Pathology 101: All You Need to Know to Start and Continue Your Digital Pathology Journey, second edition, copyright 2026, supplied 179-page PDF. The supplied Word notes provided discovery links. Biographical context was checked against public LinkedIn search listings and accessible author websites; full LinkedIn profile pages were not retrievable.

1. Luis Cano, author description and book access: https://payhip.com/b/wldfe

2. Luis Cano, Beyond the Slide author page: https://beyondtheslide.substack.com/about

3. Luis Cano Ayestas, LinkedIn: https://www.linkedin.com/in/luiscanoayestas/

4. Aleksandra Zuraw, author biography: https://digitalpathologyplace.com/about/

5. Aleksandra Zuraw, LinkedIn: https://www.linkedin.com/in/aleksandra-zuraw-dvm-phd-dacvp/

6. CMS, QSO-23-15-CLIA, May 11, 2023, pp. 2–3: https://www.cms.gov/files/document/qso-23-15-clia-original-release-2023-05-11.pdf

7. McGenity C et al. Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy. npj Digital Medicine. 2024;7:114. https://doi.org/10.1038/s41746-024-01106-8

8. College of American Pathologists, How To Use The New Digital Pathology CPT Codes: https://www.cap.org/article/how-to-use-the-new-digital-pathology-cpt-codes-cap-answers-questions/

 

 

EXTRA

 

I asked Chat GPT if they discuss my thesis for dramatic digital pathology growth – 3 points.

 

 

1.      The supply chain must not end with a DICOM presented to a doctor.  We must present a full report ,with draft micro and draft diagnosis, draft grading (e.g. Gleason), for validation and sign out. 

2.      This software MUST BE FDA APPROVED.  No more horsing around with RUO shareware that must be tediously validated at each location.  Sell sAMD to 3000 hospitals not shareware for 4.

3.      We had to dramatically reform and advance input or interaction devices – a new generation that we can’t even clearly foresee.  But we can do it. 

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Yes—especially Zuraw. But your thesis goes further than either book, and the leap in input and control devices is largely missing from both. It would make a useful additional lens for the review.

Your argument sets a concrete endpoint for digital pathology: the system should help produce a completed diagnostic work product, ready for pathologist review and confirmation, using commercially supported clinical software and a substantially better interface.

Your priority	Cano	Zuraw
Draft the microscopic description and diagnosis for pathologist confirmation	Compatible with his vision of human–AI collaboration, but not developed into a reporting workflow.	Explicitly discusses report drafting, image-and-text integration, and automated reporting. However, she treats diagnostic generation cautiously and emphasizes supporting applications.
FDA-cleared commercial products that ordinary laboratories can purchase and deploy	Supports regulation and validation, but his practical starting point emphasizes QuPath, experimentation, and acquiring analysis skills.	Much closer to your position: emphasizes production-grade software, commercial development, maintenance, integration, and reliable failure handling. She does not, however, adopt an exclusively FDA-cleared pathway.
A generational advance in how pathologists control and navigate virtual slides	Essentially undeveloped.	Discusses ergonomics, usability, and workflow integration, but does not develop a new model of physical interaction with digital slides.

The reporting connection is quite direct in Zuraw. On PDF pp. 123–127, she discusses drafting synoptic reports from dictated findings, generating structured reports from image inputs, and combining image findings with clinical information. Those are relevant passages.

There is nevertheless an important difference between turning a pathologist’s dictation into a report and examining the tissue and preparing a proposed microscopic description and diagnosis. Your thesis centers on the latter. Zuraw touches that possibility, but does not organize the book around delivering it.

Her discussion of commercial software is the closest match to your frustration. On PDF p. 144, she distinguishes research code that works in its developer’s hands from a clinical tool that other people can use, maintain, and trust. On p. 157, she explicitly identifies commercialization and engineering as necessary to produce dependable products. That is strongly compatible with your demand that laboratories should be able to buy a finished clinical product.

But it would overstate her position to say she demands FDA clearance for every clinical application. She also discusses internally developed laboratory algorithms and substantial local validation. Your position is a stronger product and commercialization requirement. I would also distinguish your objection to laboratories having to finish and substantiate a research product from any suggestion that purchasing cleared software eliminates local implementation checks.

Your interface argument adds something neither book adequately supplies. Zuraw’s treatment of ergonomics on PDF p. 68 concerns monitors, seating, lighting, and software usability. That addresses today’s workstation. Your question is more ambitious: what interaction system would let a pathologist examine, compare, interrogate, and confirm tissue findings with dramatically less effort? Neither author provides that vision.

You do not need to specify the winning device to identify the unmet requirement. Possible directions—my extrapolations, not their proposals—include coordinated voice and physical controls, navigation that anticipates the next relevant field, synchronized movement across stains, and direct movement between a proposed report statement and its supporting tissue region.

My assessment: Zuraw supplies substantial support for your first two priorities; neither book adequately develops your third. Your contribution would be to make all three explicit criteria for success, rather than treating digitization, interoperability, or an accurate standalone algorithm as sufficient endpoints.