A clinical-radiomic-pathomic model for prognosis prediction in patients with hepatocellular carcinoma after radical resection.

PURPOSE: Radical surgery, the first-line treatment for patients with hepatocellular cancer (HCC), faces the dilemma of high early recurrence rates and the inability to predict effectively. We aim to develop and validate a multimodal model combining clinical, radiomics, and pathomics features to predict the risk of early recurrence. MATERIALS AND METHODS: We recruited HCC patients who underwent radical surgery and collected their preoperative clinical information, enhanced computed tomography (CT) images, and whole slide images (WSI) of hematoxylin and eosin (H & E) stained biopsy sections. After feature screening analysis, independent clinical, radiomics, and pathomics features closely associated with early recurrence were identified. Next, we built 16 models using four combination data composed of three type features, four machine learning algorithms, and 5-fold cross-validation to assess the performance and predictive power of the comparative models. RESULTS: Between January 2016 and December 2020, we recruited 107 HCC patients, of whom 45.8% (49/107) experienced early recurrence. After analysis, we identified two clinical features, two radiomics features, and three pathomics features associated with early recurrence. Multimodal machine learning models showed better predictive performance than bimodal models. Moreover, the SVM algorithm showed the best prediction results among the multimodal models. The average area under the curve (AUC), accuracy (ACC), sensitivity, and specificity were 0.863, 0.784, 0.731, and 0.826, respectively. Finally, we constructed a comprehensive nomogram using clinical features, a radiomics score and a pathomics score to provide a reference for predicting the risk of early recurrence. CONCLUSIONS: The multimodal models can be used as a primary tool for oncologists to predict the risk of early recurrence after radical HCC surgery, which will help optimize and personalize treatment strategies.

Enhancing Specific Fluorescence In Situ Hybridization with Quantum Dots for Single-Molecule RNA Imaging in Formalin-Fixed Paraffin-Embedded Tumor Tissues.

Single-molecule fluorescence in situ hybridization (smFISH) represents a promising approach for the quantitative analysis of nucleic acid biomarkers in clinical tissue samples. However, low signal intensity and high background noise are complications that arise from diagnostic pathology when performed with smFISH-based RNA imaging in formalin-fixed paraffin-embedded (FFPE) tissue specimens. Moreover, the associated complex procedures can produce uncertain results and poor image quality. Herein, by combining the high specificity of split DNA probes with the high signal readout of ZnCdSe/ZnS quantum dot (QD) labeling, we introduce QD split-FISH, a high-brightness smFISH technology, to quantify the expression of mRNA in both cell lines and clinical FFPE tissue samples of breast cancer and lung squamous carcinoma. Owing to its high signal-to-noise ratio, QD split-FISH is a fast, inexpensive, and sensitive method for quantifying mRNA expression in FFPE tumor tissues, making it suitable for biomarker imaging and diagnostic pathology.

Radiomics-guided prognostic assessment of early-stage hepatocellular carcinoma recurrence post-radical resection.

PURPOSE: The prognosis of early-stage hepatocellular carcinoma (HCC) patients after radical resection has received widespread attention, but reliable prediction methods are lacking. Radiomics derived from enhanced computed tomography (CT) imaging offers a potential avenue for practical prognostication in HCC patients. METHODS: We recruited early-stage HCC patients undergoing radical resection. Statistical analyses were performed to identify clinicopathological and radiomic features linked to recurrence. Clinical, radiomic, and combined models (incorporating clinicopathological and radiomic features) were built using four algorithms. The performance of these models was scrutinized via fivefold cross-validation, with evaluation metrics including the area under the curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE) being calculated and compared. Ultimately, an integrated nomogram was devised by combining independent clinicopathological predictors with the Radscore. RESULTS: From January 2016 through December 2020, HCC recurrence was observed in 167 cases (64.5%), with a median time to recurrence of 26.7 months following initial resection. Combined models outperformed those solely relying on clinicopathological or radiomic features. Notably, among the combined models, those employing support vector machine (SVM) algorithms exhibited the most promising predictive outcomes (AUC: 0.840 (95% Confidence interval (CI): [0.696, 0.984]), ACC: 0.805, SEN: 0.849, SPE: 0.733). Hepatitis B infection, tumour size > 5 cm, and alpha-fetoprotein (AFP) > 400 ng/mL were identified as independent recurrence predictors and were subsequently amalgamated with the Radscore to create a visually intuitive nomogram, delivering robust and reliable predictive performance. CONCLUSION: Machine learning models amalgamating clinicopathological and radiomic features provide a valuable tool for clinicians to predict postoperative HCC recurrence, thereby informing early preventative strategies.

Detecting urine metabolites of bladder cancer by surface-enhanced Raman spectroscopy.

AIM: Metabolites present in urine reflect the current phenotype of the cancer state. Surface-enhanced Raman spectroscopy (SERS) can be used in urine supernatant or sediment to largely reflect the metabolic status of the body. MATERIALS & METHODS: SERS was performed to detect bladder cancer (BCa) and predict tumour grade from urine supernatant, which contains various system metabolites, as well as from urine sediment, which contains exfoliated tumour cells. RESULTS & DISCUSSION: Upon combining the urinary supernatant and sediment results, the total diagnostic sensitivity and specificity of SERS were 100% and 98.85%, respectively, for high-grade tumours and 97.53% and 90.80%, respectively, for low-grade tumours. CONCLUSION: The present results suggest high potential for SERS to detect BCa from urine, especially when combining both urinary supernatant and sediment results.

Label-free detection of multiple genitourinary cancers from urine by surface-enhanced Raman spectroscopy.

Detecting cancers through testing biological fluids, namely, "liquid biopsy", is noninvasive and shows great promise in cancer diagnosis, surveillance and screening. Many metabolites that may reflect cancer specificity are concentrated in and excreted through urine. In this study, urine samples were collected from healthy subjects and patients with bladder or prostate cancer. By using surface-enhanced Raman spectroscopy (SERS) with silver nanoparticles, urine sample spectra from 500-1800 cm-1 were obtained. The spectra were classified by principal component analysis and linear discriminant analysis (PCA-LDA). The results showed that the classification accuracy of the model for healthy individuals, bladder cancer patients and prostate cancer patients was 91.9%, and the classification accuracy of the test set was 89%, which indicated that SERS combined with the PCA-LDA diagnostic algorithm could be used as a classification and diagnostic tool to detect and distinguish bladder cancer and prostate cancer through testing urine.

A surface-enhanced Raman scattering-based probe method for detecting chromogranin A in adrenal tumors.

Aim: We aim to demonstrate that a surface-enhanced Raman spectroscopy (SERS) probe can be effectively used for protein detection in adrenal tumors. Materials & methods: The SERS probe method, which uses Au@Ag core-shell nanoparticles conjugated with a CgA antibody and a SERS reporter, was applied to detect CgA in adrenal tumors. Results: Our data reveal that the results of the CgA-SERS probe method were almost identical to those of western blot and superior to those of traditional immunohistochemistry. Conclusion: This study offers a novel strategy to detect CgA in adrenal tumors and provides more reliable protein test results than traditional immunohistochemistry analysis for adrenal pathologists, meaning that it might be a better clinical reference for the diagnosis of pheochromocytoma.

Assessing the effectiveness of artificial intelligence methods for melanoma: A retrospective review.

BACKGROUND: Artificial intelligence methods for the classification of melanoma have been studied extensively. However, few studies compare these methods under the same standards. OBJECTIVE: To seek the best artificial intelligence method for diagnosis of melanoma. METHODS: The contrast test used 2200 dermoscopic images. Image segmentations, feature extractions, and classifications were performed in sequence for evaluation of traditional machine learning algorithms. The recent popular convolutional neural network frameworks were used for transfer learning training classification. RESULTS: The region growing algorithm has the best segmentation performance, with an intersection over union of 70.06% and a false-positive rate of 17.67%. Classification performance was better with logistic regression, with a sensitivity of 76.36% and a specificity of 87.04%. The Inception V3 model (Google, Mountain View, CA) worked best in deep learning algorithms: the accuracy was 93.74%, the sensitivity was 94.36%, and the specificity was 85.64%. LIMITATIONS: There was no division in the severity of melanoma samples used in this experiment. The data set was relatively small for deep learning. CONCLUSION: The performance of traditional machine learning is satisfactory for the small data set of melanoma dermoscopic images, and the potential for deep learning in the future big data era is enormous.

Establishment and Verification of a Bagged-Trees-Based Model for Prediction of Sentinel Lymph Node Metastasis for Early Breast Cancer Patients.

Purpose: Lymph node metastasis is a multifactorial event. Several scholars have developed nomograph models to predict the sentinel lymph nodes (SLN) metastasis before operation. According to the clinical and pathological characteristics of breast cancer patients, we use the new method to establish a more comprehensive model and add some new factors which have never been analyzed in the world and explored the prospect of its clinical application. Materials and methods: The clinicopathological data of 633 patients with breast cancer who underwent SLN examination from January 2011 to December 2014 were retrospectively analyzed. Because of the imbalance in data, we used smote algorithm to oversample the data to increase the balanced amount of data. Our study for the first time included the shape of the tumor and breast gland content. The location of the tumor was analyzed by the vector combining quadrant method, at the same time we use the method of simply using quadrant or vector for comparing. We also compared the predictive ability of building models through logistic regression and Bagged-Tree algorithm. The Bagged-Tree algorithm was used to categorize samples. The SMOTE-Bagged Tree algorithm and 5-fold cross-validation was used to established the prediction model. The clinical application value of the model in early breast cancer patients was evaluated by confusion matrix and the area under receiver operating characteristic (ROC) curve (AUC). Results: Our predictive model included 12 variables as follows: age, body mass index (BMI), quadrant, clock direction, the distance of tumor from the nipple, morphology of tumor molybdenum target, glandular content, tumor size, ER, PR, HER2, and Ki-67.Finally, our model obtained the AUC value of 0.801 and the accuracy of 70.3%.We used logistic regression to established the model, in the modeling and validation groups, the area under the curve (AUC) were 0.660 and 0.580.We used the vector combining quadrant method to analyze the original location of the tumor, which is more precise than simply using vector or quadrant (AUC 0.801 vs. 0.791 vs. 0.701, Accuracy 70.3 vs. 70.3 vs. 63.6%). Conclusions: Our model is more reliable and stable to assist doctors predict the SLN metastasis in breast cancer patients before operation.

Analysis and classification of kidney stones based on Raman spectroscopy.

The number of patients with kidney stones worldwide is increasing, and it is particularly important to facilitate accurate diagnosis methods. Accurate analysis of the type of kidney stones plays a crucial role in the patient's follow-up treatment. This study used microscopic Raman spectroscopy to analyze and classify the different mineral components present in kidney stones. There were several Raman changes observed for the different types of kidney stones and the four types were oxalates, phosphates, purines and L-cystine kidney stones. We then combined machine learning techniques with Raman spectroscopy. KNN and SVM combinations with PCA (PCA-KNN, PCA-SVM) methods were implemented to classify the same spectral data set. The results show the diagnostic accuracies are 96.3% for the PCA-KNN and PCA-SVM methods with high sensitivity (0.963, 0.963) and specificity (0.995,0.985). The experimental Raman spectra results of kidney stones show the proposed method has high classification accuracy. This approach can provide support for physicians making treatment recommendations to patients with kidney stones.