Table 2 |.

Predictive performance results for adverse pathologies from prostate tissue and breast tissue samples

Predicted adverse pathology	Sensitivity	Specificity	AuC	N	True positive	True negative	Predicted positive	Predicted negative
Prostate tissue
Seminal vesicle invasion	0.89	0.96	0.93	57	9	48	8	46
Positive surgical margin	0.99	0.93	0.94	59	18	41	18	38
Extra-prostatic extension	0.95	0.97	0.96	53	21	32	20	31
Perineural invasion	0.99	0.99	0.99	50	37	13	37	13
Lymph node positive	0.95	0.96	0.81	47	4	43	4	41
Lymph vascular invasion	0.99	0.98	0.98	54	6	48	6	47
GAPP	0.91	0.93	0.88	59	45	14	41	13
LAPP	0.93	0.90	0.93	59	28	31	26	28
MAPP	0.95	0.84	0.89	59	40	19	38	16
Breast tissue
Extra-nodal extension	0.99	0.73	0.84	37	14	23	13	19
Positive surgical margin	0.99	0.95	0.98	45	3	42	3	39
Lympho-vascular invasion	0.90	0.87	0.87	44	21	23	19	19
Lymph invasion	0.96	0.79	0.91	46	27	19	20	18
GAPPa	0.81	0.93	0.85	47	32	15	26	14
LAPPa	0.99	0.72	0.81	47	15	32	15	23
MAPPa	0.84	0.88	0.85	47	31	16	26	14
MAPPLIb	0.90	0.85	0.83	32	19	13	15	12
MAPPLVIb	0.83	0.87	0.86	32	18	14	15	12

Sensitivity, specificity, AUC, total number of samples, true-positive and true-negative numbers, and number of samples predicted positive or negative were obtained from machine-learning-derived statistical algorithms.

^aFor breast samples, GAPP, LAPP and MAPP scores are derived from algorithms trained on all breast samples.

^bFor breast samples, MAPP_LI and MAPP_LVI scores are derived from algorithms trained on DCIS positive samples only.