A pathologist examines a biopsy slide. They see tissue architecture, cellular morphology, nuclear atypia, mitotic figures, and stromal patterns. They integrate decades of training into a diagnosis. But the slide contains information that no human eye can extract: subvisual morphometric patterns that predict genomic alterations, spatial relationships between immune cells and tumor cells that predict immunotherapy response, and quantitative features at the pixel level that stratify patients into risk categories invisible to visual assessment. Prism is a computational pathology platform that reads what the pathologist sees — and reveals what the tissue knows but the eye cannot perceive.
Pathology is the definitive diagnostic discipline. No cancer treatment begins without a pathologist's diagnosis. No surgical decision is made without a pathologist's assessment of margins. No prognosis is given without a pathologist's grade. And yet, pathology remains one of the most subjective disciplines in medicine. Two expert pathologists examining the same slide will disagree on the grade 20-30% of the time. They will disagree on the margin status in 8-12% of cases. They will miss micrometastases in sentinel lymph nodes at a rate of 10-15%. And they cannot extract the genomic, proteomic, and spatial information that the tissue contains but the human visual system was never designed to perceive.
Prism is a computational pathology platform that analyzes digitized whole-slide images with the same rigor that a pathologist brings — and adds capabilities that no human eye possesses. The platform detects tumors, grades cancers, assesses margins, counts mitoses, quantifies biomarker expression, maps the tumor microenvironment, predicts genomic alterations from morphology alone, and stratifies patients into risk categories — all from a single H&E-stained slide, in 42 seconds, with reproducibility that eliminates the inter-observer variability that plagues every other approach to tissue diagnosis.
Each represents information the tissue contains that conventional pathology cannot consistently extract.
From tumor detection through genomic prediction — every engine designed to extract the full information content of tissue that conventional pathology leaves untapped.
Tumor detection is the first task of pathology — distinguishing malignant tissue from benign, inflammatory, or normal tissue. Prism's tumor detection engine processes the entire whole-slide image (2.4 billion pixels for a typical 40x scan), classifying every region as tumor, stroma, necrosis, inflammation, normal tissue, or artifact. The classification extends to cancer subtype: invasive ductal vs. invasive lobular in breast, adenocarcinoma vs. squamous cell vs. small cell in lung, and clear cell vs. papillary vs. chromophobe in kidney. Each classification includes a confidence score and a heatmap overlay that shows the pathologist exactly which regions the AI has identified as malignant, enabling efficient confirmation rather than exhaustive search. For prostate biopsies, the engine has achieved clinical-grade Gleason grading with 94% concordance with expert uropathologists — higher than the average inter-reader agreement between two human pathologists (82%).
The most transformative capability of computational pathology is the ability to predict genomic information from tissue morphology alone. Every genetic alteration leaves a morphological fingerprint: BRAF-mutant melanomas have distinct cell morphology, MSI-high colorectal cancers have characteristic immune infiltration patterns, HER2-amplified breast cancers display specific nuclear features, and molecular subtypes (Luminal A, Luminal B, HER2-enriched, Basal-like) correlate with tissue architecture patterns that deep learning can learn. Prism's genomic prediction engine is trained on hundreds of thousands of slides with paired genomic data, learning the morphology-genome correlations that are invisible to human observation. The clinical impact is immediate: a breast cancer biopsy processed on a Friday evening generates a Prism prediction of ER+/PR+/HER2–/Luminal A within 42 seconds — giving the oncologist actionable molecular information days before the IHC results return. When the IHC confirms the prediction (concordance: 89%), the treatment planning timeline has been compressed by 3-5 days.
Cancer grading is a critical determinant of treatment. A Gleason 3+4 prostate cancer is managed differently from a Gleason 4+3. A Nottingham Grade 2 breast cancer receives different therapy than a Grade 3. But grading is subjective: it depends on the pathologist's assessment of nuclear features, glandular architecture, and mitotic activity — characteristics that are continuous variables forced into categorical scores. Two pathologists examining the same slide will disagree on the grade 20-30% of the time. Prism's grading engine quantifies every component of the grading system with pixel-level precision: nuclear size, shape, and chromatin pattern are measured mathematically rather than estimated visually; tubule/gland formation is quantified as a percentage rather than categorized subjectively; and mitotic figures are counted exhaustively across every high-power field rather than sampled from selected regions. The resulting grade is reproducible — the same slide analyzed twice produces the same score every time.
Lymph node metastasis determines cancer staging and treatment. A node-negative breast cancer is Stage I or II; a node-positive cancer is Stage III with dramatically different prognosis and treatment. The sentinel lymph node biopsy is examined by the pathologist at multiple levels — but not every level can be sectioned and examined, and micrometastases between examined levels may be missed. The CAMELYON challenge demonstrated that deep learning algorithms can detect metastases in lymph node biopsies with higher sensitivity than pathologists, particularly for micrometastases smaller than 2mm. Prism's lymph node engine scans every pixel of every digitized level, detecting metastatic deposits as small as 50 microns — clusters of 20-30 cancer cells that would be invisible to all but the most meticulous manual examination. Each detected metastasis is marked with location, size, and classification (macrometastasis, micrometastasis, or isolated tumor cells), enabling accurate staging without the 10-15% miss rate of conventional examination.
The tumor microenvironment — the ecosystem of immune cells, stromal cells, blood vessels, and extracellular matrix surrounding the tumor — is increasingly recognized as a critical determinant of treatment response. Tumors with dense immune infiltration ("hot" tumors) respond to immunotherapy at rates 3-5 times higher than tumors with immune exclusion ("cold" tumors). But characterizing the immune microenvironment requires quantifying and mapping thousands of immune cells across the entire slide — a task that is impractical for manual pathology. Prism's microenvironment engine classifies every cell in the slide as tumor, lymphocyte, macrophage, fibroblast, endothelial, or other, creating a spatial map of the tumor ecosystem. The engine calculates TIL density, immune cell distribution patterns (infiltrating, marginal, or excluded), spatial proximity metrics between immune and tumor cells, and immune phenotype ratios. This spatial immune contexture predicts immunotherapy response with 78% accuracy from a routine H&E slide — information that currently requires expensive multiplex immunofluorescence panels.
Biomarker quantification in pathology is paradoxically imprecise. Ki-67 proliferation index — a critical prognostic marker in breast cancer — is "estimated" by pathologists with inter-reader variability of 22%. PD-L1 tumor proportion score — the gatekeeper for immunotherapy eligibility — varies 15-20% between readers. These biomarkers have clinical decision thresholds (Ki-67 at 20%, PD-L1 at 50%) where a few percentage points determine whether a patient receives chemotherapy or immunotherapy. Prism's biomarker engine replaces visual estimation with exhaustive quantification: every positive and negative cell in the scored region is identified, counted, and classified. Ki-67 is reported as 18.4%, not "approximately 20%." PD-L1 TPS is reported as 47.2%, not "about 50%." The measurement is reproducible to within 2% — compared to 22% inter-reader variability in visual estimation. For the patient at the decision threshold, the difference between 18.4% and "approximately 20%" can determine the treatment they receive.
Standard grading systems categorize cancers into 2-4 groups. But tissue morphology contains far more prognostic information than these categorical systems capture. Two Grade 2 breast cancers may have dramatically different recurrence risks based on stromal patterns, nuclear architecture, immune infiltration, and vascular invasion — features that the grading system does not quantify. Prism's prognostic engine extracts hundreds of morphometric features from the whole-slide image and integrates them into a continuous risk score that stratifies patients beyond what standard grading achieves. In breast cancer, the AI-derived morphological risk score predicts 10-year recurrence with an AUC of 0.87 — exceeding the prognostic accuracy of Nottingham grade (AUC 0.72) and approaching the performance of genomic assays like Oncotype DX (AUC 0.91) — from a routine H&E slide that costs $15, rather than a genomic test that costs $4,000.
Frozen section consultation is the highest-pressure moment in pathology: a surgeon sends a tissue sample from the operating room and needs a diagnosis within 15-20 minutes while the patient is under anesthesia. Is the margin positive or negative? Is the mass malignant or benign? Is the lymph node involved? The pathologist examines a rapidly frozen, suboptimally processed slide under time pressure — and the frozen section discordance rate with final permanent sections is 2-4%. Prism's frozen section engine analyzes the digitized frozen section slide in 90 seconds, providing the pathologist with an AI assessment that includes tumor identification, margin status, and a confidence score — information that the pathologist can incorporate into their rapid diagnosis. The AI does not replace the pathologist's judgment in the frozen section suite. It provides a second opinion that arrives 90 seconds after the slide is scanned, giving the pathologist an AI assessment to compare against their own impression before communicating the result to the operating room.
An academic cancer center processing 84,000 pathology slides per year deployed Prism across breast, prostate, lung, and colorectal pathology. Grading concordance (agreement between the AI and a consensus panel of three pathologists) improved from 76% (the baseline inter-reader agreement between two individual pathologists) to 94%. Lymph node metastasis detection improved by 14% — identifying micrometastases in 38 cases that were initially read as negative on manual examination, upstaging these patients and changing their treatment plans. Morphology-based genomic prediction enabled the oncology team to defer Oncotype DX testing in 32% of breast cancer cases where the AI predicted low recurrence risk with high confidence — saving $4,000 per deferred test and providing actionable information 3-5 days earlier than the genomic assay results.
A community hospital in a rural service area with two pathologists serving a 200,000-patient population deployed Prism to address a pathologist shortage that had extended diagnostic turnaround to 72 hours. The AI pre-screening reduced pathologist time per case by 40% — not by replacing the pathologist's diagnosis, but by pre-classifying tissue regions, pre-grading tumors, pre-counting biomarkers, and highlighting areas requiring focused attention. The pathologist reviewed the AI analysis, confirmed or modified the findings, and signed the report in 15 minutes instead of 45 minutes per complex case. Diagnostic turnaround decreased from 72 hours to 24 hours. Diagnostic accuracy was maintained — the AI flagged 4 cases where the initial pathologist assessment would have been revised on subsequent review, catching potential discordances before the report was finalized.
A multi-site immunotherapy clinical trial required standardized PD-L1 scoring across 8 participating institutions. The trial's central pathology review had identified an 18% inter-reader variability in PD-L1 tumor proportion score — meaning that the same slide scored by different pathologists at different sites would receive different scores, potentially placing the patient above or below the 50% treatment eligibility threshold. Prism's biomarker quantification engine was deployed as the standardized scoring method: every PD-L1 slide was scored by the AI with 2% variability, providing a reproducible, site-independent measurement. In the first enrollment cohort, 12 patients had AI PD-L1 scores that differed from their local pathologist's score by more than 10 percentage points — and in 8 of those 12 cases, the difference crossed the 50% treatment threshold, meaning the patient would have been enrolled or excluded incorrectly under local scoring alone.
I have been a pathologist for 22 years. I have looked at perhaps 300,000 slides. I am good at what I do. But I cannot count every mitotic figure in every high-power field. I cannot quantify Ki-67 to the decimal point. I cannot detect a 50-micron cluster of cancer cells in a lymph node that I examined at three levels instead of the ten that would have been ideal. And I certainly cannot look at an H&E slide and tell you the molecular subtype with 89% accuracy. Prism does all of these things. It does not replace me. It reads the same slide I read — and it tells me what the tissue knows that my eyes cannot see. In 22 years, I have never had a tool that made me this much better at my job.
We are two pathologists serving 200,000 patients. Our turnaround time had reached 72 hours. Patients were waiting three days for a cancer diagnosis. Three days of not knowing. Three days of anxiety that I could not reduce because there were not enough hours in my day to read every slide faster. Prism did not replace me. It prepared every case for me. By the time I sit down at the microscope, the AI has already identified the tumor, graded it, counted the mitoses, measured the margins, and flagged the areas that need my attention. I confirm, I modify where needed, and I sign. Fifteen minutes instead of forty-five. Our turnaround is 24 hours now. Patients get their diagnosis the next day. I sleep better. They sleep better.
Our immunotherapy trial required PD-L1 scoring at 8 sites. Eighteen percent variability. The same slide, scored by eight pathologists at eight institutions, produced PD-L1 scores that ranged from 38% to 56%. That is not diagnostic imprecision — that is a treatment decision being made by which pathologist happens to read the slide. Twelve of our enrolled patients had scores that differed by more than 10 points between the AI and the local pathologist. Eight of those twelve crossed the 50% threshold. Eight patients whose treatment eligibility depended on which human read the slide. Prism gave us one number: reproducible, site-independent, and verifiable. The trial's integrity required it. The patients deserved it.
Request a clinical demonstration of Prism — including live whole-slide analysis, genomic prediction, and biomarker quantification on your institution's digitized slides.