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During the diagnostic workup of lung adenocarcinomas (LAC), pathologists evaluate distinct histological tumor growth patterns. The percentage of each pattern on multiple slides bears prognostic significance. To assist with the quantification of growth patterns, we constructed a pipeline equipped with a convolutional neural network (CNN) and soft-voting as the decision function to recognize solid, micropapillary, acinar, and cribriform growth patterns, and non-tumor areas. Slides of primary LAC were obtained from Cedars-Sinai Medical Center (CSMC), the Military Institute of Medicine in Warsaw and the TCGA portal. Several CNN models trained with 19,924 image tiles extracted from 78 slides (MIMW and CSMC) were evaluated on 128 test slides from the three sites by F1-score and accuracy using manual tumor annotations by pathologist. The best CNN yielded F1-scores of 0.91 (solid), 0.76 (micropapillary), 0.74 (acinar), 0.6 (cribriform), and 0.96 (non-tumor) respectively. The overall accuracy of distinguishing the five tissue classes was 89.24%. Slide-based accuracy in the CSMC set (88.5%) was significantly better (p < 2.3E-4) than the accuracy in the MIMW (84.2%) and TCGA (84%) sets due to superior slide quality. Our model can work side-by-side with a pathologist to accurately quantify the percentages of growth patterns in tumors with mixed LAC patterns.

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After validation, this classification may be useful to accurately identify acromegaly patients with distinctive patterns of disease aggressiveness and outcome, as well as to provide an accurate tool for selection criteria in clinical studies.

Objective: Clinically nonfunctioning pituitary adenoma (NFPA) remains the only pituitary tumor subtype for which no effective medical therapy is available or recommended. We evaluated dopamine agonist (DA) therapy for preventing growth of postsurgical pituitary tumor remnants. Design: The study design included historical cohort analysis of clinical results at two pituitary referral centers with different standard practices for postoperative NFPA management: DA therapy or conservative follow-up. Methods: Seventy-nine patients followed for 8.8 ± 6.5 years were treated with DA, initiated upon residual tumor detection on postoperative MRI (preventive treatment (PT) group, n = 55), or when tumor growth was subsequently detected during follow-up (remedial treatment (RT) group, n = 24). The control group (n = 60) received no medication. Tumoral dopamine and estrogen receptor expression assessed by quantitative RT-PCR and immunostaining were correlated with response to treatment. Results: Tumor mass decreased, remained stable, or enlarged, respectively, in 38, 49, and 13% of patients in the PT group, and in 0, 53, and 47% of control subjects; shrinkage or stabilization was achieved in 58% of enlarging tumors in the RT group, P < 0.0001. Fifteen-year progression-free survival rate was 0.805, 0.24, and 0.04, respectively, for PT, RT, and control groups (P < 0.001). About 42% of patients in the control group required additional surgery or radiotherapy, compared with 38 and 13% subjects in the RT and PT groups, respectively (P = 0.002). Outcome measures were not related to NFPA D2R abundance. Conclusions: Dopamine agonist therapy in patients with NFPA is associated with decreased prevalence of residual tumor enlargement after transsphenoidal surgical resection.