Wed, 2020-07-08 16:00
Radiomics Can Identify High-Risk Early Stage Lung Cancer
Wednesday, July 8, 2020 - 16:00

The article “Radiomics Can Identify High-Risk Early Stage Lung Cancer” from Medscape quotes Anant Madabhushi, PhD, and references several studies from Madabhushi and the CCIPD team including a 2019 study in radiomics showing that combining radiomics and imaging may be able to determine which patients with lung cancer were most likely to respond to chemotherapy. Madabhushi commented that the new study is, "complementary and supports the premise that radiomics both from inside and outside the tumor can tell us about outcome and treatment response.”

Mon, 2020-07-06 11:50
Madabhushi presents July 8 virtual lecture at Memorial Sloan-Kettering
Monday, July 6, 2020 - 11:50

CCIPD Director Anant Madabhushi, PhD will be giving the virtual professorship lecture, "Artificial Intelligence and Computational Imaging in Radiology and Pathology: Implications for Precision Oncology", with the Memorial Sloan Kettering Cancer Center Department of Medical Physics on July 8, 3-4 pm EDT.

The John S. Laughlin Visiting Professorship was established in 1990 by a generous endowment from the late Dr. Helen Q. Woodard. The professorship honors the distinguished career of Dr. John S. Laughlin whose contributions to medical physics span almost half a century. The Professorship is chosen annually and is intended to honor a distinguished contributor to the field of medical physics or to the medical uses of radiation.

Email kpg11 at for the zoom link.

Thu, 2020-07-02 16:13
CCIPD researchers awarded patents
Thursday, July 2, 2020 - 16:13

On June 23rd, 2020, two patents were awarded to inventors from the Center for Computational Imaging and Personalized Diagnostics (CCIPD): “Intra-Perinodular Textural Transition (IPRIS): A Three Dimensional (3D) Descriptor for Nodule Diagnosis on Lung Computed Tomography (CT) Images” and “Treatment planning and evaluation for rectal cancer via image analytics”. Congratulations to Anant Madabhushi, Satish Viswanath, Mehdi Alilou and Jacob Antunes. Read details about the patents below.


“Treatment planning and evaluation for rectal cancer via image analytics”

United States Serial Number (USSN): 10,692,607, June 23rd, 2020.

Inventors: Satish Viswanath, Anant Madabhushi, Jacob Antunes

Abstract: Methods and apparatus associated with predicting colorectal cancer tumor invasiveness are described. One example apparatus includes a set of circuits, and a data store that stores radiological images of tissue demonstrating colorectal cancer. The set of circuits includes a circumferential resection margin (CRM) prediction circuit that generates a CRM probability score for a diagnostic radiological image, an image acquisition circuit that acquires a diagnostic radiological image of a region of tissue demonstrating colorectal cancer pathology and that provides the diagnostic radiological image to the CRM prediction circuit, and a training circuit that trains the CRM prediction circuit to quantify chemoradiation response in the region of tissue represented in the diagnostic radiological image. The training circuit trains the CRM prediction circuit using a set of composite images.


“Intra-Perinodular Textural Transition (IPRIS): A Three Dimensional (3D) Descriptor for Nodule Diagnosis on Lung Computed Tomography (CT) Images”

United States Serial Number (USSN): 10,692,211, June 23rd, 2020

Inventors: Mehdi Alilou, Anant Madabhushi

Abstract: Embodiments classify lung nodules by accessing a 3D radiological image of a region of tissue, the 3D image including a plurality of voxels and slices, a slice having a thickness; segmenting the nodule represented in the 3D image across contiguous slices, the nodule having a 3D volume and 3D interface, where the 3D interface includes an interface voxel; partitioning the 3D interface into a plurality of nested shells, a nested shell including a plurality of 2D slices, a 2D slice including a boundary pixel; extracting a set of intra-perinodular textural transition (Ipris) features from the 2D slices based on a normal of a boundary pixel of the 2D slices; providing the Ipris features to a machine learning classifier which computes a probability that the nodule is malignant, based, at least in part, on the set of Ipris features; and generating a classification of the nodule based on the probability.

Thu, 2020-06-25 11:01
CCIPD’s Haojia Li is one of Crain’s Cleveland 2020 “Twenty in their 20s”
Thursday, June 25, 2020 - 11:01

Congratulations to Haojia Li, PhD student in biomedical engineering and researcher in the Center for Computational Imaging and Personalized Diagnostics (CCIPD) for making Crain's Cleveland 2020 "Twenty in their 20s"! Li’s work focuses on how artificial intelligence can be used to better diagnose and treat breast cancer. She has developed algorithms that can look at images of a certain kind of pre-malignant breast lesion and determine which ones are likely to lead to cancer.