Prof. Pingfu Fu Profile

Prof. Pingfu Fu

at Case Western Reserve Univ

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Publications (7)

Proceedings Article | 7 April 2023 Presentation + Paper

A pathomic study for risk stratification and unraveling molecular associations of different histologic subtypes of papillary thyroid cancer

Shayan Monabbati, Sirvan Khalighi, Pingfu Fu, Sylvia Asa, Anant Madabhushi

Proceedings Volume 12471, 1247114 (2023) https://doi.org/10.1117/12.2655929

KEYWORDS: Tumors, Thyroid, Cancer, Decision support systems, Tumor growth modeling, Tissues, Statistical analysis, Medical research, Biological samples

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Papillary thyroid carcinoma (PTC) has the highest incidence rate of all thyroid cancers for the last several decades. Although this particular disease tends to be fairly indolent overall, classical and tall cell variants exhibit more aggressive behavior due to a higher progression and mortality rate. However among classical PTCs, there is a need for improvement of clinical management of patients who are at higher risk of progression/death and who will potentially benefit from more aggressive management, and similarly for lower risk patients who are unnecessarily overtreated. In this study, we aimed to evaluate the prognostic role of nuclear features for disease-specific and disease-free survival within classical and follicular histological subtypes of PTC. A set of features describing the nuclear shape, architecture, and texture of both tumor and lymphocyte cells were used to train a Lasso-Cox regression model with 199 patients. Coefficients of the model were then used to assign a quantitative risk score (QuRiS) per patient. Patients were further subdivided into high and low risk of recurrence to compare survival probabilities within a 15 year study period using Kaplan-Meier estimates (C-index of 0.786 (p = 0.009 on log-rank test). Survival probabilities of both risk groups were computed with a hazard ratio 1.54 and compared using the log-rank test (p = 0.004). The trained model was then validated on the remaining 149 patients, consisting of 3 independent sets of classical (N = 107), follicular (N = 74), and tall cell (N = 16) variants of PTC, with HR=8.83, 4.21, and 0.583, respectively. We then identified specific genes that were differentially expressed between the risk groups identified by the pathomic model. Using gene set enrichment and mutation analysis, we discovered that the signaling pathways TCA cycle and amino acid metabolism are associated with poorer outcome, while mutation in the BRAF oncogene is significantly associated with better survival in cPTC patients. We also discovered that the DTX4 gene was prognostic for disease-specific survival (DSS) and further stratified the risk of disease-related death of patients with high and low expression of DTX4 using the image-based nuclear morphology features.

Proceedings Article | 4 April 2022 Presentation + Paper

Computer extracted features of tumor-infiltrating lymphocytes (TILs) architecture are prognostic of progression-free survival in stage III colon cancer

Aya Aqeel, Germán Corredor, Vidya Sankar Viswanathan, Chuheng Chen, Mogjan Mokhtari, Pingfu Fu, Joseph Willis, Anant Madabhushi

Proceedings Volume 12039, 120390V (2022) https://doi.org/10.1117/12.2613161

KEYWORDS: Colorectal cancer, Tumors, Feature extraction, Cancer, Image segmentation, Data modeling, Medical research

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Stage III Colorectal cancer (CRC) is treated with surgery followed by chemotherapy. Yet >20% of clinically low-risk patients develop recurrence. It is critical to identify high-risk stage III patients who can benefit from closer monitoring and escalation of therapy. Previous studies showed promising results in predicting risk from H&E slides in CRC using deep learning based algorithms. One biomarker which was heavily studied and showed good results in predicting risk is Tumor Infiltrating Lymphocytes (TILs). In CRC, TIL density has been shown to be significantly and independently associated with overall patient survival¹. Furthermore, additional studies have demonstrated that analyzing spatial organization of TILs could be more informative than density alone². Hence, our study aimed to stratify stage III CRC patients into distinct risk groups based on features derived from TILs and to determine if this classification could have independent prognostic significance. The training set (D₁) included 50 patients and validation set (D₂) consisted of 70 patients from an independent site. A survival model was trained to predict the risk of recurrence in stage III CRC patients. First, a deep learning (DL) model was used to segment TILs on WSIs. Next, 1036 features related to spatial architecture (SpaTIL) and density of TILs (DenTIL) were extracted. A Cox proportional hazards regression model in conjunction with the least absolute shrinkage and selection operator (Lasso) was used to find top 5 features and the feature coefficients associated with progression free survival (PFS) and provide risk scores to each patient. The risk scores for the training dataset were computed using the selected features with their respective coefficients. A cut-off value was determined according to these risk scores, above which patients were labelled high-risk and below was low risk. In the validation set, the median PFS for the high-risk group was 15.1mos and in the low-risk group was 27mos. The model was able to accurately predict higher incidence of progression in patients in the high-risk group (HR = 3.76, 95% CI 1.3-10.9, p-value=0.0053, c-index=0.687) in the validation set. Future work will entail additional multi-site, multi-institutional validation of our biomarker to further understand its strengths and applications.

Proceedings Article | 4 April 2022 Poster

Quantitative histomorphometric features of tumor nuclei are prognostic of disease-free survival in papillary thyroid carcinoma

Shayan Monabbati, Paula Toro, Kaustav Bera, Pingfu Fu, Sylvia Lou, Anant Madabhushi

Proceedings Volume 12039, 120391B (2022) https://doi.org/10.1117/12.2613789

KEYWORDS: Tissues, Shape analysis, Image analysis, Hazard analysis, Cancer

SPIE Journal Paper | 13 July 2021 Open Access

Computational pathology reveals unique spatial patterns of immune response in H&E images from COVID-19 autopsies: preliminary findings

Paula Toro, Kaustav Bera, Pingfu Fu, Lisa Barton, Hannah Gilmore, Sanjay Mukhopadhyay, Anant Madabhushi, Germán Corredor, Dylan Rasmussen, Vidya Sankar Viswanathan, Christina Buzzy, Edana Stroberg, Eric Duval

JMI, Vol. 8, Issue S1, 017501, (July 2021) https://doi.org/10.1117/12.10.1117/1.JMI.8.S1.017501

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Proceedings Article | 16 March 2020 Presentation + Paper

Computer extracted features related to the spatial arrangement of tumor-infiltrating lymphocytes predict overall survival in epithelial ovarian cancer receiving adjuvant chemotherapy

Sepideh Azarianpour, Germán Corredor, Kaustav Bera, Patrick Leo, Nathaniel Braman, Pingfu Fu, Haider Mahdi, Anant Madabhushi

Proceedings Volume 11320, 113200Q (2020) https://doi.org/10.1117/12.2550188

KEYWORDS: Tumors, Ovarian cancer, Cancer, Feature extraction, Lung cancer, Statistical analysis, Tumor growth modeling, Tissues, Data modeling, Medical research

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Proceedings Article | 13 March 2019 Presentation + Paper

Quantitative vessel tortuosity radiomics on baseline non-contrast lung CT predict response to immunotherapy and are prognostic of overall survival

Mehdi Alilou, Pranjal Vaidya, Mohammadhadi Khorrami, Alexia Zagouras, Pradnya Patil, Kaustav Bera, Pingfu Fu, Vamsidhar Velcheti, Anant Madabhushi

Proceedings Volume 10950, 109501F (2019) https://doi.org/10.1117/12.2513648

KEYWORDS: Computed tomography, Tumors, Image segmentation, Lung cancer, Cancer, Lung, Feature extraction, Medical research

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Proceedings Article | 13 March 2019 Presentation + Paper

A combination of intra- and peritumoral features on baseline CT scans is associated with overall survival in non-small cell lung cancer patients treated with immune checkpoint inhibitors: a multi-agent multi-site study

Mohammadhadi Khorrami, Mehdi Alilou, Prateek Prasanna, Pradnya Patil, Pirya Velu, Kaustav Bera, Pingfu Fu, Vamsidhar Velcheti, Anant Madabhushi

Proceedings Volume 10950, 109500R (2019) https://doi.org/10.1117/12.2513001

KEYWORDS: Tumors, Computed tomography, Lung cancer, Feature extraction, Cancer, Lung, Tissues, Medical research, Feature selection, Proteins

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Immune checkpoint inhibitors targeting the programmed cell death (PD)1/ L1 axis have been approved for treatment of chemotherapy refractory advanced non-small cell lung cancer (NSCLC) for a few years. While higher PD-L1 expression is associated with better outcomes after monotherapy with immune checkpoint inhibitors, it is not a perfect predictive biomarker for clinical benefit from immunotherapy, because some patients with low PD-L1 expression have sustained responses. In clinical practice, using radiological tools like Response Evaluation Criteria in Solid Tumors (RECIST), tends to underestimate the benefit of therapy. For instance, some patients treated with immunotherapy suffer from pseudoprogression while actually having a favorable response, RECIST in this setting is inadequate to capture the response. In this study we sought to explore whether radiomic texture features extracted from both inside and outside of the tumor from baseline CT scans were associated with overall patient survival (OS) in 139 NSCLC patients being treated with IO from two separate sites. Patients were divided into a discovery (D₁ = 50; nivolumab from Cleveland Clinic) and two validation sets (D₂ = 62 from Cleveland Clinic, D₃ = 27 from University of Pennsylvania Health System. Patients in the validation sets had been treated with different types of checkpoint inhibitor drugs including nivolumab, pembrolizumab, and atezolizumab. 454 radiomic texture features from within (intra-tumoral) and outside the tumor (peri-tumoral) were extracted from baseline contrast CT images. Following feature selection on the discovery set, a radiomic risk-score signature was generated by using least absolute shrinkage and selection operator. Using a Cox regression model, the association of the radiomic signature with overall survival (OS) was evaluated in the discovery and two validation sets. In addition, 95% confidence intervals (CI) and relative hazard ratios (HR) were calculated. Our results revealed that the radiomics signature was significantly associated with OS, both in the discovery set (HR = 5.06, 95%CI = 3, 8.55; p-value < 0.0001) and the two validation data sets (D₂: HR = 5.88, 95% CI = 2.19, 21.63, p-value = 0.0009; D₃: HR = 5.37, 95% CI = 1.74, 16.57, p-value = 0.0034). Our initial results appear to suggest that our radiomic signature could serve as a non-invasive way of predicting and monitoring response to checkpoint inhibitors for patients with non-small cell lung cancer.

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