Identifying primary tumor site of origin for liver metastases via a combination of handcrafted and deep learning features

Clinical characteristics and prognosis of liver metastases with unknown primary site

Background

Liver metastases from cancer of unknown primary (CUPL) constitute a rare disease, particularly among individuals younger than 50 years old. This paper aims to investigate the clinical characteristics of patients with CUPL and analyze prognostic differences across distinct age groups.

Methods

Data pertaining to patients with CUPL were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Propensity score matching (PSM) was employed to adjust for clinical variables. Cox regression analysis identified risk factors influencing overall survival (OS), while competing-risk analyses were conducted to determine prognostic factors for cancer-specific survival (CSS). Survival differences were compared using the Kaplan-Meier method and cumulative incidence function (CIF).

Results

The study encompassed 4,691 patients, with 319 (6.8%) in the age <50 years group and 4,372 (93.2%) in the age ≥50 years group. Individuals with unexplained liver metastases exhibited a 1-year OS rate of 14.7% and a 1-year CSS rate of 23%. Following matching, age, histology, brain metastases, and chemotherapy were identified as independent prognostic factors affecting OS. Additionally, race, grade, histology, brain metastases, and chemotherapy were recognized as independent prognostic factors influencing CSS. Notably, the age <50 years group demonstrated superior OS and CSS compared to the age ≥50 years group before and after PSM. Among patients undergoing chemotherapy, the age <50 years group exhibited enhanced OS and CSS compared to their age ≥50 years counterparts. Furthermore, in individuals subjected to radiotherapy, the age <50 years group demonstrated superior OS, although no significant difference in CSS was observed.

Conclusions

The survival prognosis of patients with CUPL was found to be poor. However, both OS and CSS were more favorable in the age <50 years group compared to the age ≥50 years group. Additionally, radiotherapy and chemotherapy were associated with an OS benefit for patients in the age <50 years group.

A more objective PD diagnostic model: integrating texture feature markers of cerebellar gray matter and white matter through machine learning

Objective

The purpose of this study is to explore whether machine learning can be used to establish an effective model for the diagnosis of Parkinson's disease (PD) by using texture features extracted from cerebellar gray matter and white matter, so as to identify subtle changes that cannot be observed by the naked eye.

Method

This study involved a data collection period from June 2010 to March 2023, including 374 subjects from two cohorts. The Parkinson's Progression Markers Initiative (PPMI) served as the training set, with control group and PD patients (HC: 102 and PD: 102) from 24 global sites. Our institution's data was utilized as the test set (HC: 91 and PD: 79). Machine learning was employed to establish multiple models for PD diagnosis based on texture features of the cerebellum's gray and white matter. Results underwent evaluation through 5-fold cross-validation analysis, calculating the area under the receiver operating characteristic curve (AUC) for each model. The performance of each model was compared using the Delong test, and the interpretability of the optimized model was further augmented by employing Shapley additive explanations (SHAP).

Results

The AUCs for all pipelines in the validation dataset were compared using FeAture Explorer (FAE) software. Among the models established by Kruskal-Wallis (KW) and logistic regression via Lasso (LRLasso), the AUC was highest using the "one-standard error" rule. 'WM_original_glrlm_GrayLevelNonUniformity' was considered the most stable and predictive feature.

Conclusion

The texture features of cerebellar gray matter and white matter combined with machine learning may have potential value in the diagnosis of Parkinson's disease, in which the heterogeneity of white matter may be a more valuable imaging marker.

Closing the loop - the role of pathologists in digital and computational pathology research

An increasing number of manuscripts related to digital and computational pathology are being submitted to The Journal of Pathology: Clinical Research as part of the continuous evolution from digital imaging and algorithm-based digital pathology to computational pathology and artificial intelligence. However, despite these technological advances, tissue analysis still relies heavily on pathologists' annotations. There are three crucial elements to the pathologist's role during annotation tasks: granularity, time constraints, and responsibility for the interpretation of computational results. Granularity involves detailed annotations, including case level, regional, and cellular features; and integration of attributions from different sources. Time constraints due to pathologist shortages have led to the development of techniques to expedite annotation tasks from cell-level attributions up to so-called unsupervised learning. The impact of pathologists may seem diminished, but their role is crucial in providing ground truth and connecting pathological knowledge generation with computational advancements. Measures to display results back to pathologists and reflections about correctly applied diagnostic criteria are mandatory to maintain fidelity during human-machine interactions. Collaboration and iterative processes, such as human-in-the-loop machine learning are key for continuous improvement, ensuring the pathologist's involvement in evaluating computational results and closing the loop for clinical applicability. The journal is interested particularly in the clinical diagnostic application of computational pathology and invites submissions that address the issues raised in this editorial.