Table 1.

Sample CTA algorithms from the published literature.

Stain	Approach	Ref	Data set	Method	Ground truth	Notes
H&E	Patch classification	24	Multiple sites	CNN	Labeled patches (yes/no TILs)	Strengths: large-scale study with investigation of spatial TIL maps. AV includes molecular correlates.
			TCGA data set		Annotations are open-access	Limitations: does not distinguish sTIL and iTIL; does not classify individual TILs*.
						Other: we defined CTA TIL score as fraction of patches that contain TILs, and found this to be correlated with VTA (R = 0.659, p = 2e-35).
	Semantic segmentation	16	Breast	FCN	Traced region boundaries (exhaustive)	Strengths: large sample size and regions; investigates inter-rater variability at different experience levels; delineation of tumor, stroma and necrosis regions.
			TCGA data set		Annotations are open-access	Limitations: only detects dense TIL infiltrates*; does not classify individual TILs*.
	Semantic segmentation + Object detection	25	Breast	Seeding + FCN	Traced region boundaries (exhaustive)	Strengths: mostly follows TIL-WG VTA guidelines. AV includes correlation with consensus VTA scores and inter-pathologist variability.
			Private data set		Labeled & segmented nuclei within labeled region	Limitations: heavy ground truth requirement*; underpowered CV; and limited manually annotated slides.
	Object detection	26	Breast	SVM using morphology features	Labeled nuclei	Strengths: robust analysis and exploration of molecular TIL correlates.
			METABRIC data set		Qualitative density scores	Limitations: individual labeled nuclei are limited; does not distinguish TILs in different histologic regions*.
		27	Breast	RG and MRF	Labeled patches (low-medium-high density)	Strengths: explainable model and modular pipeline.
			Private data set			Limitations: does not distinguish sTIL and iTIL; does not classify individual TILs. Limited AV sample size.
		28	NSCLC	Watershed + SVM classifier	Labeled nuclei	Strengths: explainable model; robust CV; captures spatial TIL clustering.
			Private data sets			Limitations: limited AV; does not distinguish sTIL and iTIL.
	Object detection + inferred TIL localization	31	Breast	SVM classifier using morphology features	Labeled nuclei	Strengths: infers TIL localization using spatial localization. Robust CV. Investigation of spatial TIL patterns.
			METABRIC + private data sets		Qualitative density scores	Limitations: individual labeled nuclei are limited. not clear if spatial clustering has 1:1 correspondence with regions.
IHC	Object detection + manual regions	29	Colon	Complex pipeline (non-DL)	Overall density estimates	Strengths: CTA within manual regions, including invasive margin.
			Private data set			Limitations: unpublished AV.
	Object detection	30	Multiple	Multiple DL pipelines	Labeled nuclei within FOV (exhaustive)	Strengths: large-scale, robust AV. Systematic benchmarking.
			Private data set			Limitations: no CV; does not distinguish TILs in different regions*.

This non-exhaustive list has been restricted to H&E and chromogenic IHC, although excellent works exist showing CTA based on other approaches like multiplexed immunofluorescence^21–23. Published CTA algorithms vary markedly in their approach to TIL scoring, the robustness of their validation, their interpretability, and their consistency with published VTA guidelines. Strengths and limitations of each publication is highlighted, with general limitations (related to the broad approach used, not the specific paper) are marked with an asterisk (*). Going forward, nuanced approaches are needed, ideally incorporating workflows for robust quantification and validation as presented in this paper. Different approaches have different ground truth requirements (illustrated in Fig. 1, panel f), hence the need for large-scale ground truth data sets. We encourage all future CTA publications to open-access their data sets whenever possible. Of note are two major efforts: 1. A group of scientists, including the US FDA and the TIL-WG, is collaborating to crowdsource pathologists and collect images and pathologist annotations that can be qualified by the FDA medical device development tool program; 2. The TIL-WG is organizing a challenge to validate CTA algorithms against clinical trial outcome data (CV).

AV analytical validation, CNN convolutional neural network, DL deep learning, FCN fully convolutional network, FOV field of view, MRF markov random field, RG region growing, NSCLC non-small cell lung cancer, SVM support vector machine.