Predict Early Recurrence of Resectable Hepatocellular Carcinoma Using Multi-Dimensional Artificial Intelligence Analysis of Liver Fibrosis

Potential of Using qFibrosis Analysis to Predict Recurrent and Survival Outcome of Patients with Hepatocellular Carcinoma after Hepatic Resection

BACKGROUND

There remains a lack of studies addressing the stromal background and fibrosis features and their prognostic value in liver cancer. qFibrosis can identify, quantify, and visualize the fibrosis features in biopsy samples. In this study, we aim to demonstrate the prognostic value of histological features by using qFibrosis analysis in liver cancer patients.

METHODS

Liver specimens from 201 patients with hepatocellular carcinoma (HCC) who underwent curative resection were imaged and assessed using qFibrosis system and generated a total of 33 and 156 collagen parameters from tumor part and non-tumor liver tissue, respectively. We used these collagen parameters on patients to build two combined indexes, RFS index and OS index, in order to differentiate patients with early recurrence and early death, respectively. The models were validated using the leave-one-out method.

RESULTS

Both combined indexes had significant prediction value for patients' outcome. The RFS index of 0.52 well differentiates patients with early recurrence (p < 0.001), and the OS index of 0.73 well differentiates patients with early death during follow-up (p = 0.02).

CONCLUSIONS

Combined index calculated with qFibrosis from a digital readout of the fibrotic status of peri-tumor liver specimen in patients with HCC has prediction values for their disease and survival outcomes. These results demonstrated the potential to transform histopathological features into quantifiable data that could be used to correlate with clinical outcome.

Development and External Validation of a Nomogram Including Body Composition Parameters for Predicting Early Recurrence of Hepatocellular Carcinoma After Hepatectomy

RATIONALE AND OBJECTIVES

Body composition, including adipose and muscle tissues, evaluated by computer tomography is correlated with the prognosis of hepatocellular carcinoma (HCC). However, its relationship with early recurrence (ER) remains unclear. This study aimed at establishing and validating a nomogram based on body composition and clinicopathological indices to predict ER of HCC.

MATERIALS AND METHODS

One hundred ninety-five patients from institution A formed the training cohort and internal validation cohort, and 50 patients from institution B formed the external validation cohort. Independent predictors of ER were identified using LASSO and Cox regression analyses. The performance of nomogram was evaluated using the calibration curve, concordance index (C-index), area under the curve (AUC), and decision curve analysis (DCA).

RESULTS

After data screening, the nomogram was constructed using eight independent predictors of ER, including the tumor size, alpha fetoprotein, body mass index, Edmondson Steiner grade, visceral adipose tissue radiodensity, intermuscular adipose tissue index, intramuscular adipose tissue content, and skeletal muscle area. The calibration curve exhibited excellent concordances, with C-indices of 0.808 (95%CI: 0.771-0.860), 0.802 (95%CI: 0.747-0.942), and 0.804 (95%CI: 0.701-0.861) in training, internal validation, and external validation cohorts, respectively. In addition, compared to conventional staging systems and pure clinical model, the nomogram exhibited a higher AUC and wider range of threshold probabilities in DCA, which indicated better discriminative ability and greater clinical benefit. Finally, patients with nomogram scores of <183.07, 183.07-243.09, and >243.09 were considered to have low, moderate, and high risks of ER, respectively.

CONCLUSION

The nomogram exhibits excellent ER predictive ability for patients with HCC who underwent hepatectomy.

Incomplete radiofrequency ablation following transarterial chemoembolization accelerates the progression of large hepatocellular carcinoma

Purpose

To examine post-operative progression and risk impact of insufficient radiofrequency ablation (RFA) following transarterial chemoembolization (TACE) for the prognosis of large hepatocellular carcinoma (HCC).

Materials and Methods

From January 2014 to January 2021 were analyzed. A total of 343 patients with large HCC (diameter >5 cm) who received TACE combined with RFA were enrolled and were divided into two groups: complete ablation (CA, n = 172) and insufficient ablation (IA, n = 171). Overall survival (OS) and progression-free survival (PFS) were determined by the Kaplan-Meier curve and compared with the log-rank test. To find parameters influencing OS and PFS, clinicopathological variables underwent univariate and multivariate analysis.

Results

The cumulative 1-, 3-, and 5-year OS and PFS rates of the CA group were significantly higher than that of the IA group (P < 0.001). 25 (41%) patients in local tumor progression (LTP), 36 (59%) in intrahepatic distant recurrence (IDR), and 0 (0%) in extrahepatic distant recurrence (EDR) in the CA group. 51 (32.1%) patients in LTP, 96 (60.4%) patients in IDR, and 12 (7.5%) cases in EDR in the IA group. The recurrence patterns of the two groups were statistically significant difference (P = 0.039). In multivariate analysis, inadequate ablation and conjunction with TKIs were both significant risk factors for OS and PFS. Apart from these, older age and >7 cm of tumor size were indicators of poor OS and multiple tumors were indicators of poor PFS.

Conclusion

Insufficient ablation causes a poor survival outcome of TACE combined with RFA for large HCC, particularly, which can promote IDR.

Quantitative collagen analysis using second harmonic generation images for the detection of basal cell carcinoma with ex vivo multiphoton microscopy

Basal cell carcinoma (BCC) is the most common skin cancer, and its incidence is rising. Millions of benign biopsies are performed annually for BCC diagnosis, increasing morbidity, and healthcare costs. Non-invasive in vivo technologies such as multiphoton microscopy (MPM) can aid in diagnosing BCC, reducing the need for biopsies. Furthermore, the second harmonic generation (SHG) signal generated from MPM can classify and prognosticate cancers based on extracellular matrix changes, especially collagen type I. We explored the potential of MPM to differentiate collagen changes associated with different BCC subtypes compared to normal skin structures and benign lesions. Quantitative analysis such as frequency band energy analysis in Fourier domain, CurveAlign and CT-FIRE fibre analysis was performed on SHG images from 52 BCC and 12 benign lesions samples. Our results showed that collagen distribution is more aligned surrounding BCCs nests compared to the skin's normal structures (p < 0.001) and benign lesions (p < 0.001). Also, collagen was orientated more parallelly surrounding indolent BCC subtypes (superficial and nodular) versus those with more aggressive behaviour (infiltrative BCC) (p = 0.021). In conclusion, SHG signal from type I collagen can aid not only in the diagnosis of BCC but could be useful for prognosticating these tumors. Our initial results are limited to a small number of samples, requiring large-scale studies to validate them. These findings represent the groundwork for future in vivo MPM for diagnosis and prognosis of BCC.

Predictors of Hepatocellular Carcinoma Early Recurrence in Patients Treated with Surgical Resection or Ablation Treatment: A Single-Center Experience

Introduction: Hepatocellular carcinoma (HCC) is the sixth most diagnosed malignancy and the fourth leading cause of cancer-related death worldwide, with poor overall survival despite available curative treatments. One of the most crucial factors influencing survival in HCC is recurrence. The current study aims to determine factors associated with early recurrence of HCC in patients with BCLC Stage 0 or Stage A treated with surgical resection or local ablation. Materials and Methods: We retrospectively enrolled 58 consecutive patients diagnosed with HCC within BCLC Stage 0 or Stage A and treated either by surgical resection or local ablation with maximum nodule diameter < 50 mm. In the first year of follow-up after treatment, imaging was performed regularly one month after treatment and then every three months. Each case was discussed collectively by the Liver Multidisciplinary Group to decide diagnosis, treatment, follow-up, and disease recurrence. Variables resulting in statistically significant difference were then studied by Cox regression analysis; univariately and then multivariately based on forward stepwise Cox regression. Results are represented in hazard ratio (H.R.) with 95% confidence interval (C.I.). Results: There was no statistically significant difference in recurrence rates (34.8 vs. 45.7%, log-rank test, p = 0.274) between patients undergoing surgical resection and local ablation, respectively. Early recurrence was associated with male gender (HR 2.5, 95% C.I. 1.9−3.1), nodule diameter > 20 mm (HR 4.5, 95% C.I. 3.9−5.1), platelet count < 125 × 103 cell/mm3 (HR 1.6, 95% C.I. 1.2−1.9), platelet-lymphocyte ratio < 95 (HR 2.1, 95% C.I. 1.7−2.6), lymphocyte-monocyte ratio < 2.5 (HR 1.9, 95% C.I. 1.4−2.5), and neutrophil-lymphocyte ratio > 2 (HR 2.7, 95% C.I. 2.2−3.3). Discussion and Conclusions: Our results are in line with the current literature. Male gender and tumor nodule dimension are the main risk factors associated with early HCC recurrence. Platelet count and other combined scores can be used as predictive tools for early HCC recurrence, although more studies are needed to define cut-offs.

5-year recurrence prediction after hepatocellular carcinoma resection: deep learning vs. Cox regression models

Deep learning algorithms have yet to be used for predicting clinical prognosis after cancer surgery. Therefore, this study compared performance indices and permutation importance of potential confounders in three models for predicting 5-year recurrence after hepatocellular carcinoma (HCC) resection: a deep-learning deep neural network (DNN) model, a recurrent neural network (RNN) model, and a Cox proportional hazard (CPH) regression model. Data for 725 patients who had received HCC resection at three medical centers in southern Taiwan between April, 2011, and December, 2015, were randomly divided into three datasets: a training dataset containing data for 507 subjects was used for model development, a testing dataset containing data for 109 subjects was used for internal validation, and a validating dataset containing data for 109 subjects was used for external validation. Feature importance analysis was also performed to identify potential predictors of recurrence after HCC resection. Univariate Cox proportional hazards regression analyses were performed to identify potential significant predictors of 5-year recurrence after HCC resection, which were included in the forecasting models (P < 0.05). All performance indices for the DNN model were significantly higher than those for the RNN model and the conventional CPH model (P < 0.001). The most important potential predictor of 5-year recurrence after HCC resection was surgeon volume followed by, in order of importance, hospital volume, preoperative Beck Depression Scale score, preoperative Beck Anxiety Scale score, co-residence with family, tumor stage, and tumor size. The feature importance analysis performed to investigate interpretability in this study elucidated the potential use of deep learning models for predicting recurrence after HCC resection and for identifying predictors of recurrence. Further experiments using the proposed DNN model would clarify its potential uses for developing, promoting, and improving health policies for treating HCC patients after surgery.